cnn_grad_norm.ipynb

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    "colab": {
      "name": "cnn_grad_norm.ipynb",
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    "kernelspec": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15zMAVUVWsSx"
      },
      "source": [
        "### DL Assignment 2 \n",
        "Implement a cnn model having 2 convolusion block, each convolusion block consists of convolusion layer which is followed by a relu function and then downsampled using max pooling. The 2nd conv block is then followed by 2 fully connected layers. We need to draw a scatter plot of gradient norm vs epoch."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvPo_DcYTbMC"
      },
      "source": [
        "Import the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKlw95K5k78c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data\n",
        "import torch.optim as optim\n",
        "import torchvision.datasets as dataset\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G32SJW8MpJYc",
        "outputId": "9ad8fe27-43e6-4998-b10c-f3bdb2d970a6"
      },
      "source": [
        "print(torch.__version__)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.7.0+cu101\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0vE5bV5pK7C",
        "outputId": "467d6d03-6895-4153-c78a-768b4c44b24e"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbA4ljzUPHg5"
      },
      "source": [
        "#### Download the data and then normalize it so that we get a distribution with a mean of 0.1307 and a standard deviation of 0.3081."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNB55FIXpNk7"
      },
      "source": [
        "train_set = dataset.MNIST(\n",
        "    root = './data',\n",
        "    train = True,\n",
        "    download = True,\n",
        "    transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.1307,), (0.3081))                                 \n",
        "    ])\n",
        ")\n",
        "\n",
        "test_set = dataset.MNIST(\n",
        "    root = './data',\n",
        "    train = False,\n",
        "    download = True,\n",
        "    transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.1307,), (0.3081))                                 \n",
        "    ])\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8omGV__xpeeJ"
      },
      "source": [
        "# initialising batch size for training and testing\n",
        "bs = 100"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaD5VS66pWrU"
      },
      "source": [
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size = bs, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(test_set, batch_size = bs, shuffle=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XcDSQl8G1FH"
      },
      "source": [
        "To calculate the output dimension, we will use formula : `W_out = ((W_in - F + 2P)/S) + 1`\n",
        "\n",
        "where \n",
        "W : width,\n",
        "S : number of stride,\n",
        "F = filter size (or kernel size)\n",
        "\n",
        "`1st Layer = Conv1 (using 5 * 5 kernel) + Relu + MaxPooling (using 2 * 2 kernel)`\n",
        ">The output of 1st layer is 14 * 14 * 32 i.e., there are 32 channels and each image is having 14 * 14 dimension.\n",
        "\n",
        "`2nd Layer = Conv2 (using 5 * 5 kernel) + Relu + MaxPooling (using 2 * 2 kernel)`\n",
        ">The output of 2nd layer is 7 * 7 * 64 i.e., there are 64 channels and each image is having 7 * 7 dimension.\n",
        " \n",
        "We have used maxpooling to down-sample the data by reducing the effective image size by a factor of 2. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgMWPVJCTBE8"
      },
      "source": [
        "#### Define the neural network structure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSjIhw0mwPhP"
      },
      "source": [
        "class ConvNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ConvNet,self).__init__()\n",
        "\n",
        "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, stride = 1, padding=2)\n",
        "        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride = 1, padding=2)\n",
        "        self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        self.fc1 = nn.Linear(7*7*64, 1000)\n",
        "        self.fc2 = nn.Linear(1000,10)\n",
        "    \n",
        "    def forward(self,x):\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = self.max_pool(x)\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x = self.max_pool(x)\n",
        "        # flatten the data dimension from 7*7*64 into 3136*1 (i.e., reshaping the tensor)\n",
        "        x = x.view(-1, 7*7*64)\n",
        "        x = self.fc1(x)\n",
        "        x = self.fc2(x)\n",
        "# No need to use softmax function as it is already included in CrossEntropyLoss()\n",
        "        return x"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMVE_c_6sdA-"
      },
      "source": [
        "num_epochs = 500\n",
        "log_interval = 10\n",
        "learning_rate = 0.001\n",
        "train_counter = []\n",
        "train_losses = []"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1p0US7KMSeyl"
      },
      "source": [
        "#### Define the loss function and optimisation algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXDRs26ErP-P"
      },
      "source": [
        "model = ConvNet()\n",
        "model.to(device) # transfer to GPU if available\n",
        "optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)\n",
        "loss_func = nn.CrossEntropyLoss()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3fJkk15S8r9"
      },
      "source": [
        "#### Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QGWHO5RrCii",
        "outputId": "f5fa4fa1-d0fe-4752-e1bf-a85360fef30c"
      },
      "source": [
        "gradient_norm = []\n",
        "for epoch in range(num_epochs):\n",
        "    print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "    print('-' * 55)\n",
        "# Train the model\n",
        "    model.train() \n",
        "    for batch_idx, data_set in enumerate(train_loader):\n",
        "        \n",
        "        data , target = data_set\n",
        "        data = data.to(device)\n",
        "        target = target.to(device)\n",
        "        # Forward propagation\n",
        "        optimizer.zero_grad()  # set gradients to zero\n",
        "        output = model(data)   \n",
        "        output.to(device)\n",
        "        loss = loss_func(output,target) # compute loss\n",
        "\n",
        "        # Back propagation and perform adam optimization\n",
        "        loss.backward() # calculate gradients\n",
        "        optimizer.step()  # update weights\n",
        "\n",
        "        if batch_idx % log_interval == 0:\n",
        "         print('Train Epoch: {} [{}/{} ({:.0f}%)]\\t Loss: {:.6f}'.format(\n",
        "           epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "           100. * batch_idx / len(train_loader), loss.item()))\n",
        "         \n",
        "        train_losses.append(loss.item())\n",
        "        train_counter.append((batch_idx*100) + ((epoch-1)*len(train_loader.dataset)))\n",
        "    \n",
        "    total_tr_loss = sum(train_losses)/(batch_idx + 1)\n",
        "    print(f\"Total train loss : {total_tr_loss}\")\n",
        "    # Calculating gradient norm\n",
        "    gradients = []\n",
        "    for name, param in model.named_parameters():\n",
        "        if param.requires_grad and \"bias\" not in name:\n",
        "            # Uncomment any of the below line if you want to see the gradient values\n",
        "            #print (name, param.grad.data)\n",
        "            #print(f'{name}.grad', param.grad, epoch)\n",
        "            gradients.append(torch.flatten(param.grad.data))\n",
        "    print(f'Grad_norm : {torch.norm(torch.cat([gradients[0],gradients[1],gradients[2],gradients[3]],dim=0))}')\n",
        "    gradient_norm.append(torch.norm(torch.cat([gradients[0],gradients[1],gradients[2],gradients[3]],dim=0)).item())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Train Epoch: 421 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 421 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 422/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 422 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 422 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 423/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 423 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 423 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 424/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 424 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 424 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 425/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 425 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 425 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 426/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 426 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 426 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 427/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 427 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 427 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 428/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 428 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 428 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 429/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 429 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 429 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 430/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 430 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 430 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 431/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 431 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 431 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 432/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 432 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 432 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 433/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 433 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 433 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 434/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 434 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 434 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 435/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 435 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 435 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 436/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 436 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 436 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 437/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 437 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 437 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 438/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 438 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 438 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 439/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 439 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 439 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 440/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 440 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 440 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 441/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 441 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 441 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 442/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 442 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 442 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 443/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 443 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 443 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 444/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 444 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 444 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 445/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 445 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 445 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 446/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 446 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 446 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 447/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 447 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 447 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 448/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 448 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 448 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 449/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 449 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 449 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 450/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 450 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 450 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 451/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 451 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 451 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 452/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 452 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 452 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 453/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 453 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 453 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 1.0439735337957435e-16\n",
            "Epoch 454/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 454 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 454 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 455/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 455 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 455 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 456/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 456 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 456 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 457/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 457 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 457 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 458/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 458 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 458 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 459/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 459 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 459 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 460/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 460 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 460 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 461/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 461 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 461 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 462/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 462 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 462 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 463/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 463 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 463 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 464/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 464 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 464 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 465/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 465 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 465 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 466/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 466 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 466 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 467/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 467 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 467 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 468/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 468 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 468 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 469/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 469 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 469 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 470/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 470 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 470 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 471/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 471 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 471 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 472/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 472 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 472 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 473/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 473 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 473 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 474/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 474 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 474 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 475/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 475 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 475 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 476/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 476 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 476 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 477/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 477 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 477 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 478/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 478 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 478 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 479/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 479 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 479 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 480/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 480 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 480 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 481/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 481 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 481 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 482/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 482 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 482 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 483/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 483 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 483 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 484/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 484 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 484 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 485/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 485 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 485 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 486/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 486 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 486 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 487/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 487 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 487 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 488/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 488 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 488 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 489/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 489 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 489 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 490/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 490 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 490 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 491/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 491 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 491 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 492/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 492 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 492 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 493/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 493 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 493 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 494/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 494 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 494 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 495/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 495 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 495 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 496/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 496 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 496 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 497/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 497 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 497 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 498/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 498 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 498 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n",
            "Epoch 499/499\n",
            "-------------------------------------------------------\n",
            "Train Epoch: 499 [0/60000 (0%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [1000/60000 (2%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [2000/60000 (3%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [3000/60000 (5%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [4000/60000 (7%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [5000/60000 (8%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [6000/60000 (10%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [7000/60000 (12%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [8000/60000 (13%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [9000/60000 (15%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [10000/60000 (17%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [11000/60000 (18%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [12000/60000 (20%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [13000/60000 (22%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [14000/60000 (23%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [15000/60000 (25%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [16000/60000 (27%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [17000/60000 (28%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [18000/60000 (30%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [19000/60000 (32%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [20000/60000 (33%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [21000/60000 (35%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [22000/60000 (37%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [23000/60000 (38%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [24000/60000 (40%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [25000/60000 (42%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [26000/60000 (43%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [27000/60000 (45%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [28000/60000 (47%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [29000/60000 (48%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [30000/60000 (50%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [31000/60000 (52%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [32000/60000 (53%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [33000/60000 (55%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [34000/60000 (57%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [35000/60000 (58%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [36000/60000 (60%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [37000/60000 (62%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [38000/60000 (63%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [39000/60000 (65%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [40000/60000 (67%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [41000/60000 (68%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [42000/60000 (70%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [43000/60000 (72%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [44000/60000 (73%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [45000/60000 (75%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [46000/60000 (77%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [47000/60000 (78%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [48000/60000 (80%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [49000/60000 (82%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [50000/60000 (83%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [51000/60000 (85%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [52000/60000 (87%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [53000/60000 (88%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [54000/60000 (90%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [55000/60000 (92%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [56000/60000 (93%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [57000/60000 (95%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [58000/60000 (97%)]\t Loss: 0.000000\n",
            "Train Epoch: 499 [59000/60000 (98%)]\t Loss: 0.000000\n",
            "Total train loss : 7.4945105772219\n",
            "Grad_norm : 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdllrj4R4Fsr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88263e32-0323-49f3-fd5b-7306d6c3910d"
      },
      "source": [
        "# Gradient norm for each epoch\n",
        "#print(gradient_norm)\n",
        "print(len(gradient_norm))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QQzT6nM6WjG"
      },
      "source": [
        "ep = [e for e in range(num_epochs)]\n",
        "#ep"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PLys15rSxsG"
      },
      "source": [
        "#### Evaluating and testing the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8cFE3ZC9w8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "472c7941-8538-4f08-de90-a4adb97f27fb"
      },
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "pred = []\n",
        "# Test the model\n",
        "model.eval()\n",
        "test_loss = 0\n",
        "for data, target in test_loader:\n",
        "  data, target = data.to(device), target.to(device)\n",
        "  output = model(data)\n",
        "  test_loss += loss_func(output,target)\n",
        "  _, predicted = torch.max(output.data,1)\n",
        "  pred.append(output)\n",
        "  total += target.size(0)\n",
        "  correct += (predicted == target).sum().item()\n",
        "\n",
        "test_loss = test_loss/len(test_loader.dataset)\n",
        "print('Average loss for the test set is {} \\nThe test accuracy of the 2-layered cnn model on the {} test images: {} %'.format(test_loss, len(test_set),(correct / (total)) * 100))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average loss for the test set is 0.037344757467508316 \n",
            " The test accuracy of the 2-layered cnn model on the 10000 test images: 99.19 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WW-5MvxHSqTE"
      },
      "source": [
        "#### Plot of gradient norm vs epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AD9JDWPI-7vO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "158d7e0c-55cc-42db-ff10-1fdfebfbcc72"
      },
      "source": [
        "fig = plt.figure()\n",
        "fig.set_size_inches(16, 8)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Norm of the gradient\")\n",
        "plt.scatter(ep,gradient_norm)\n",
        "plt.plot(ep,gradient_norm)\n",
        "plt.title(\"Gradient norm vs Epoch\")\n",
        "plt.savefig('scatter plot of gradient norm vs epoch (with 500 epoch).pdf',dpi = 300)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-FQ9xYChBxJ",
        "outputId": "d70d54ff-53ba-433f-efcd-c8f62dea5d06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "fig.set_size_inches(16, 8)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Norm of the gradient\")\n",
        "plt.scatter(ep,gradient_norm)\n",
        "plt.title(\"Gradient norm vs Epoch\")\n",
        "plt.savefig('scatter plot of gradient norm vs epoch (with 500 epochs).pdf',dpi = 300)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAAD0ksAKAABALwmsAAAA9JLACgAAQC8JrAAAAPSSwAoAAEAvCawAAAD0ksAKAABALwmsAAAA9NLIAmtV3auqrqyq91bVNVX17K79blX1xqr6QPfzrqOqAQAAgOk1yh7WW5I8t7V2/yQPSfJjVXX/JM9P8qbW2v2SvKnbBgAAgJOMLLC21q5rrb2re35jkvcl2ZHk8Uku6152WZK9o6oBAACA6TWWe1ir6rwku5K8Pcm5rbXrul0fT3LuCsc8s6oOVdWhG264YRxlAgAA0CMjD6xVdackf57kOa21zw3va621JG2541prl7bWdrfWdm/btm3UZQIAANAzIw2sVTWTQVh9ZWvt8q75E1V1j27/PZJcP8oaAAAAmE6jnCW4krw0yftaa786tOuKJE/vnj89yV+MqgYAAACm19kjPPfDkzwtyZGquqpre0GSS5K8tqqekeQjSZ44whoAAACYUiMLrK21tyapFXZ/66jeFwAAgI1hLLMEAwAAwHoJrAAAAPSSwAoAAEAvCawAAAD00ikDa1U9ey1tAAAAcCatpYf16cu0/eAZrgMAAABOsuKyNlX15CTfn+Q+VXXF0K47J/n0qAsDAABgc1ttHdZ/THJdknOS/MpQ+41Jrh5lUQAAALBiYG2tfSTJR5I8dHzlAAAAwMBaJl26sKo+UFWfrarPVdWNVfW5cRQHAADA5rXakOBFv5zksa219426GAAAAFi0llmCPyGsAgAAMG5r6WE9VFWvSXIgyc2Lja21y0dWFQAAAJveWgLrVya5Kcl3DLW1JAIrAAAAI3PKwNpa+6FxFAIAAADD1jJL8NdX1Zuq6j3d9gOq6mdGXxoAAACb2VomXfr9JBclOZYkrbWrkzxplEUBAADAWgLrHVpr71jSdssoigEAAIBFawmsn6yq+2Yw0VKq6glJrhtpVQAAAGx6a5kl+MeSXJrkG6pqLsn/TfLUkVYFAADApreWWYI/lOTbquqOSc5qrd04+rIAAADY7FYMrFX11NbaH1fVTy5pT5K01n51xLUBAACwia3Ww3rH7uedx1EIwDgcODyX/QeP5tr5hWzfOpt9e3Zm764dky4LAIBlrBhYW2u/1/188fjKARidA4fnctHlR7Jw7HiSZG5+IRddfiRJhFYAgB5abUjwb652YGvtJ858OQCjs//g0RNhddHCsePZf/CowAoA0EOrLWvzzu5x+yQPSvKB7nFBktuNvjSAM+va+YV1tQMAMFmrDQm+LEmq6llJHtFau6Xb/t0kfz+e8gDOnO1bZzO3TDjdvnX2tM7rvlgAgNFYrYd10V2TfOXQ9p26NoCpsm/PzszObDmpbXZmS/bt2Xmbz7l4X+zc/EJavnxf7IHDc6dZLQAAawmslyQ5XFUvr6rLkrwryS+OtiyAM2/vrh25+MLzs2PrbCrJjq2zufjC80+rN3S1+2IBADg9qy1rkyRprf1hVf1Nkm/ump7XWvv4aMsCGI29u3ac0eG67osFABidtfSwJsnNSa5L8pkkX19V3zK6kgCmx0r3v57ufbEAAKwhsFbVDyd5S5KDSV7c/XzRaMsCmA6juC8WAICBtfSwPjvJNyX5SGvtUUl2JZkfaVUAU2IU98UCADBwyntYk3yxtfbFqkpVfUVr7f1VpesAoHOm74sFAGBgLYH1Y1W1NcmBJG+sqs8k+choywIAAGCzW8sswd/bPX1RVV2Z5C5J/nakVQEAALDprRpYq2pLkmtaa9+QJK21vxtLVQAAAGx6q0661Fo7nuRoVd17TPUAAABAkrXdw3rXJNdU1TuSfGGxsbX2uJFVBQAAwKa3lsD630ZeBQAAACyxlkmX3LcKAADA2J0ysFbVjUnakubPJjmU5LmttQ+NojAAAAA2t7UMCf71JB9L8qokleRJSe6b5F1JXpbkkaMqDgAAgM1r1VmCO49rrf1ea+3G1trnWmuXJtnTWntNBhMyAQAAwBm3lsB6U1U9sarO6h5PTPLFbt/SocIAAABwRqwlsD4lydOSXJ/kE93zp1bVbJIfH2FtAAAAbGJrmSX4Q0keu8Lut57ZcgAAAGBgLT2sAAAAMHYCKwAAAL0ksAIAANBLpwysVXVuVb20qv6m275/VT1j9KUBAACwma2lh/XlSQ4m2d5t/3OS54yqIAAAAEjWFljPaa29NsmXkqS1dkuS4yOtCgAAgE3vlMvaJPlCVd09SUuSqnpIks+OtCpgQzpweC77Dx7NtfML2b51Nvv27MzeXTsmXRYAAD21lsD6k0muSHLfqvqHJNuSPGGkVQEbzoHDc7no8iNZODYYoDE3v5CLLj+SJEIrAADLOuWQ4Nbau5L8uyQPS/Kfk3xja+3qUx1XVS+rquur6j1DbS+qqrmquqp7fPfpFA9Mj/0Hj54Iq4sWjh3P/oNHJ1QRAAB9t5Ye1iR5cJLzutc/qKrSWvujUxzz8iS/lWTp636ttfY/1lMkMP2unV9YVzsAAJwysFbVK5LcN8lV+fJkSy23DqInaa29parOO836gA1i+9bZzC0TTrdvnZ1ANQAATIO19LDuTnL/1lo7Q+/541X1A0kOJXlua+0zZ+i8QI/t27PzpHtYk2R2Zkv27dk5waoAAOiztSxr854kX32G3u8lGfTWXpDkuiS/stILq+qZVXWoqg7dcMMNZ+jtgUnZu2tHLr7w/OzYOptKsmPrbC6+8HwTLgEAsKJaqeO0qv4yg6G/d84gYL4jyc2L+1trjzvlyQdDgl/fWvu369m31O7du9uhQ4dO9TIAAACmUFW9s7W2e2n7akOCz/jESFV1j9badd3m92bQewsAAAC3smJgba39XZJU1S+11p43vK+qfinJ36124qr6kySPTHJOVX0syQuTPLKqLsig5/bDGSyTAwAAALeylkmXvj3J85a0fdcybSdprT15meaXrrEuAAAANrkVA2tVPSvJjyb52qq6emjXnZP8w6gLAwAAYHNbrYf1VUn+JsnFSZ4/1H5ja+3TI60KAACATW+1e1g/m+SzSZYb2gsAAAAjtZZ1WAEAAGDsVgysVfUV4ywEAAAAhq3Ww/q2JKmqV4ypFgAAADhhtUmXbldV35/kYVV14dKdrbXLR1cWAAAAm91qgfVHkjwlydYkj12yryURWAEAABiZ1WYJfmuSt1bVodbaS8dYEwAAAKzaw7roFVX1E0m+pdv+uyS/21o7NrqyAAAA2OzWElh/J8lM9zNJnpbkJUl+eFRFAQAAwFoC6ze11h44tP3mqnr3qAoCAACAZG2B9XhV3be19i9JUlVfm+T4aMsChh04PJf9B4/m2vmFbN86m317dmbvrh2TLgsAAEZqLYF1X5Irq+pDSSrJ1yT5oZFWBZxw4PBcLrr8SBaODf6daG5+IRddfiRJhFYAADa0UwbW1tqbqup+SXZ2TUdbazePtixg0f6DR0+E1UULx45n/8GjAisAABvaWnpY0wXUq0dcC7CMa+cX1tUOAAAbxVmTLgBY3fats+tqBwCAjUJghZ7bt2dnZme2nNQ2O7Ml+/bsXPb1Bw7P5eGXvDn3ef5f5eGXvDkHDs+No0wAADjj1jQkuKoekOS84de31i4fUU3AkMX7VNcyS7AJmgAA2EhOGVir6mVJHpDkmiRf6ppbEoEVxmTvrh1rCpwmaAIAYCNZSw/rQ1pr9x95JcBpM0ETAAAbyVruYX1bVQmsMAVM0AQAwEaylsD6RxmE1qNVdXVVHakqS9xAD613giYAAOiztQwJfmmSpyU5ki/fwwr00HomaAIAgL5bS2C9obV2xcgrAc6ItU7QBAAAfbeWwHq4ql6V5C+T3LzYaFkbAAAARmktgXU2g6D6HUNtlrUBAABgpFYNrFW1JcmnWms/NaZ6AAAAIMkpZglurR1P8vAx1QIAAAAnrGVI8FVVdUWSP03yhcVG97ACAAAwSmsJrLdP8qkkjx5qcw8rAAAAI3XKwNpa+6FxFAIAAADDVr2HNUmq6p5V9bqqur57/HlV3XMcxQEAALB5nTKwJvnDJFck2d49/rJrAwAAgJFZS2Dd1lr7w9baLd3j5Um2jbguAAAANrm1BNZPVdVTq2pL93hqBpMwAQAAwMisJbD+xyRPTPLxJNcleUISEzEBAAAwUmuZJfgjSR43hloAAADghBUDa1X991WOa621nxtBPQAAAJBk9R7WLyzTdsckz0hy9yQCKwAAACOzYmBtrf3K4vOqunOSZ2dw7+qrk/zKSscBAADAmbDqPaxVdbckP5nkKUkuS/Kg1tpnxlEYAAAAm9tq97DuT3JhkkuTnN9a+/zYqgIAAGDTW21Zm+cm2Z7kZ5JcW1Wf6x43VtXnxlMeAAAAm9Vq97CuZY1WAAAAGAmhFAAAgF4SWAEAAOglgRUAAIBeElgBAADoJYEVAACAXhJYAQAA6CWBFQAAgF4SWAEAAOglgRUAAIBeElgBAADoJYEVAACAXhpZYK2ql1XV9VX1nqG2u1XVG6vqA93Pu47q/QEAAJhuo+xhfXmS71zS9vwkb2qt3S/Jm7ptAAAAuJWRBdbW2luSfHpJ8+OTXNY9vyzJ3lG9PwAAANNt3Pewnttau657/vEk5475/QEAAJgSE5t0qbXWkrSV9lfVM6vqUFUduuGGG8ZYGQAAAH0w7sD6iaq6R5J0P69f6YWttUtba7tba7u3bds2tgIBAADoh3EH1iuSPL17/vQkfzHm9wcAAGBKjHJZmz9J8rYkO6vqY1X1jCSXJPn2qvpAkm/rtgEAAOBWzh7ViVtrT15h17eO6j0BAADYOCY26RIAAACsRmAFAACglwRWAAAAeklgBQAAoJcEVgAAAHpJYAUAAKCXBFYAAAB6SWAFAACglwRWAAAAeklgBQAAoJcEVgAAAHrp7EkXABvVgcNz2X/waK6dX8j2rbPZt2dn9u7aMemyAABgagisMAIHDs/losuPZOHY8STJ3PxCLrr8SJIIrQAAsEaGBMMI7D949ERYXbRw7Hj2Hzw6oYoAAGD6CKwwAtfOL6yrHQAAuDWBFUZg+9bZdbUDAAC3JrDCCOzbszOzM1tOapud2ZJ9e3ZOqCIAAJg+Jl2CEVicWMkswQAAcNsJrDAie3ftEFABAOA0GBIMAABALwmsAAAA9JLACgAAQC8JrAAAAPSSwAoAAEAvCawAAAD0ksAKAABALwmsAAAA9JLACgAAQC8JrAAAAPSSwAoAAEAvCawAAAD0ksAKAABALwmsAAAA9JLACgAAQC8JrAAAAPSSwAoAAEAvnT3pAmDSDhyey/6DR3Pt/EK2b53Nvj07s3fXjkmXBQAAm57AyqZ24PBcLrr8SBaOHU+SzM0v5KLLjySJ0AoAABNmSDCb2v6DR0+E1UULx45n/8GjE6oIAABYJLCyqV07v7CudgAAYHwEVja17Vtn19UOAACMj8DKprZvz87Mzmw5qW12Zkv27dk5oYoAAIBFJl1iU1ucWMkswQAA0D8CK5ve3l07BFQAAOghgZVNx7qrAAAwHQRWNhXrrgIAwPQw6RKbinVXAQBgegisbCrWXQUAgOkhsLKpWHcVAACmh8DKpmLdVQAAmB4mXWJTse4qAABMD4GVTce6qwAAMB0MCQYAAKCXBFYAAAB6SWAFAACglwRWAAAAemkiky5V1YeT3JjkeJJbWmu7J1EHAAAA/TXJWYIf1Vr75ATfHwAAgB4zJBgAAIBemlQPa0vyhqpqSX6vtXbp0hdU1TOTPDNJ7n3ve4+5PDaKA4fnsv/g0Vw7v5DtW2ezb89Oa7ACAMCUmFRgfURrba6qvirJG6vq/a21twy/oAuxlybJ7t272ySKZLodODyXiy4/koVjx5Mkc/MLuejyI0kitAIAwBSYyJDg1tpc9/P6JK9L8uBJ1MHGtv/g0RNhddHCsePZf/DohCoCAADWY+yBtaruWFV3Xnye5DuSvGfcdbDxXTu/sK52AACgXyYxJPjcJK+rqsX3f1Vr7W8nUAcb3Pats5lbJpxu3zo7gWoAAID1GnsPa2vtQ621B3aPb2yt/cK4a2Bz2LdnZ2ZntpzUNjuzJfv27JxQRQAAwHpMch1WGKnFiZXMEgwAANNJYKX3Tmdpmr27dgioAAAwpQRWes3SNAAAsHlNZFkbWCtL0wAAwOalh5WJW23IrxhSzksAAA/jSURBVKVpAABg8xJYmZgDh+fyoiuuyfzCsRNtS4f8WpoGAAA2L0OCmYjFe1OHw+qi4SG/lqYBAIDNSw8rE7HcvanDFof8WpoGAAA2L4GViTjVPajDQ34tTQMAAJuTwMpErHRvarL6kN/TWZMVAACYLu5hZSKWuzc1Se56h5lcfOH5y4bQxfte5+YX0vLlCZoOHJ4bQ8UAAMC46WFlIm7LvamrrcmqlxUAADYegZWJWe+9qdZkPTVDpgEA2EgMCWZqrLT2qjVZBwyZBgBgoxFYmRrWZF3dakOmAQBgGhkSzNSwJuvqDJkGAGCjEViZKtZkXdlKSwUZMg0AwLQyJBg2CEOmAQDYaPSwwgZhyDQAABuNwMpJLIsy3QyZBgBgIxFYOWFxWZTFmWYXl0VJIgQBAABj5x5WTrAsCgAA0CcCKydYFgUAAOgTgZUTVlr+xLIoAADAJAisnGBZFAAAoE9MusQJa10WxUzCAADAOAisnORUy6KYSRgAABgXQ4JZFzMJAwAA4yKwsi5mEgYAAMZFYGVdzCQMAACMi8DKuphJGAAAGBeTLrEua51JmH4ywzMAANNEYO2h5UJF0p+QeKqZhOknMzwDADBtBNYJWamna7lQse9P351Ucux4O9EmaLBeq83w7DoCAKCPBNYzYL3DLFfr6VouVBz7UrvVOQQN1ssMzwAATBuB9TTdlmGWq/V0rSc8TFPQcO/k5G3fOpu5Za4ZMzwDANBXZgk+TauFz5Ws1tO1nvAwLUFjMdTPzS+k5cuh/sDhuUmXtqmY4RkAgGkjsJ6m2zLMcrW1TJcLFTNnVWa21Elt0xQ0bkuo58zbu2tHLr7w/OzYOptKsmPrbC6+8Hw93QAA9JYhwafptgyz3Ldn50nDiJMvB9CVlo1JkhddcU3mF44lSW4/Mz3/1uDeyf4wwzMAANNEYD1Nq4XPlZxqLdPlQsWBw3O5+ZYvndj+zE3HpmamYPdOAgAAt4XAeppOFT5XO249QXOalyS5LaEeAABAYD0DxjHMcpqH1d7WUA8AAGxuAuuUmPZhte6dBAAA1mt6Zu7Z5PqyJMmBw3N5+CVvzn2e/1d5+CVvtjQNAAAwMnpYp0QfhtUurqe6eC/q4nqqw/UBAACcKQJrTx04PLdsOJ1kMJzmiZ8AAIDpY0jwaRrFENnFnsy5+YW0DHoyn/Oaq7LrZ98w0SG40zzxEwAAMH30sJ6G5YbI/tfXXJXnvOaqbJ2dSVUyf9OxWw3fXan3dNFyPZnJ5NdenfaJnwAAgOmih/U0LBcsW/dzfuFYPnPTsRM9pBddfiQHDs8t23u6uG/Raj2Wi0NwJ6EvEz8BAACbgx7W07CeobDDQfNU94Gu1JO52vueqtf2TOjDxE8AAMDmIbCehlMFy6VWC7jD+/bt2XnSUOPl3nfYOGfvnfTETwAAwOZhSPBpWG6I7Gq2b51d8X7P4fa9u3bk4gvPz9bZmVu9brkhuKvN3gsAADCt9LCehuEhsnPzC6l8+R7WpYaD5tLe0+VC6GJP5lqG+pq9l3EMCQcAgHETWE/T8BDZnzlwJK/8P//vVqH1rneYyQsf+423mgl4LeFiLUNwVxqafJdlemiXEnRumz59b+McEg4AAOMksJ5BV77/hmV7WO9wu7NPCg7DIXQx+PzX11y1ruAzHJjuMjuTsyr50pI3/8K/3pIDh+dO9NS+6IprMr9wLMkgRD/mAffIn79z7rSDTp/C2zj0LSCuNiR8I/8eAADY+Kq1lQaxjvBNq74zyW8k2ZLkD1prl6z2+t27d7dDhw6NpbbTcZ/n/9WKQ4IXLQbLHVtn86hv2HZSYExyYljxjlWC39LAtJrF8+z703fn2NJEu8ox//D8R6/ptcvVMjuzJRdfeH5vwtKZDtQPv+TNy/ZoL/e9jSPMr3TdVZL/e8ljzuh7Ldps/0gBAMBoVdU7W2u7b9U+7sBaVVuS/HOSb0/ysST/lOTJrbX3rnTMtATWlYLMSla75zVZOfjt+tk35DM3HbttRa7RXe8wk8/cdOykGofD9uI9t8997btzfJVraPGYLVU53lq2zs6kKvnMTcdOtC2G9yvff0Pm5hdOtA+/92KP8JXvv+FEr/K/3nI8Nx370knvs1y9y33Pd5g5K18xs+WkOoZrW+t5TvW5VzpmvfUOfwevf/d1J3rKl+tZX7R1diZ3/Iqzb3V/9XLnOd3aFp3qmMW22/pd35baltu/2nd5W957+Bo6netypXMu912N4nu5Lb+fcf3OztQx01avY8Z3zLTV65jxHTNt9TpmfMdMW73L3abYJ30KrA9N8qLW2p5u+6Ikaa1dvNIx0xJY19PzuVZLe+0OHJ7Lc15z1Rk7/201c1YllRw7Pt7rh7U5q5ItZ5XfDwAAJ8xsqex/wgN7GVpXCqyTWNZmR5KPDm1/rGubeovL0ezYOps6Q+dcOtNvX5aqOfalJgz1WIt/TAAA4GTHjrfe5Im16u2kS1X1zCTPTJJ73/veE65m7YYnVFrvEOHlLF231VI1rMUEbk0HAGAKTFuemEQP61ySew1t37NrO0lr7dLW2u7W2u5t27aNrbgzad+enYOhsyuYndmSO95uy6r7l67PujTAwnK21Jnq4wcAYCOZtjwxicD6T0nuV1X3qarbJXlSkismUMfI7d21I/u/74HZOrQe6mJ+3bF1NhdfeH5+4XvPz+zMrUPrXe8ws+yES/v27Fz29YtmzqrMbFk5rJxVWXX/Wp3qfZic2ZktefI332vV6wQAgM1nZkvdqkOs78Y+JLi1dktV/XiSgxksa/Oy1to1465jXIaHCK9mrUuELLYPr8FalczfdOzEsYv7l5sd9oWP/caT9i+diXT+pmMnzrmWWYKX1rHSMZOcJfhU9d6WWYLXOpvq8HsPr4N7pmcJHv4uF6+f3V9ztxWvgzMxS/ByMy2fqe9t1DPnmSXYLMHTUq9jxnfMtNXrmPEdM231OmZ8x0xbvX2fJXglE1mHdb2mZZZgAAAA1q9PswQDAADAKQmsAAAA9JLACgAAQC8JrAAAAPSSwAoAAEAvCawAAAD0ksAKAABALwmsAAAA9JLACgAAQC8JrAAAAPSSwAoAAEAvCawAAAD0ksAKAABALwmsAAAA9JLACgAAQC9Va23SNZxSVd2Q5COTruMUzknyyUkXwabnOqQvXIv0geuQvnAt0hd9vha/prW2bWnjVATWaVBVh1pruyddB5ub65C+cC3SB65D+sK1SF9M47VoSDAAAAC9JLACAADQSwLrmXPppAuAuA7pD9cifeA6pC9ci/TF1F2L7mEFAACgl/SwAgAA0EsC62mqqu+sqqNV9cGqev6k62Fjq6qXVdX1VfWeoba7VdUbq+oD3c+7du1VVb/ZXZtXV9WDJlc5G0lV3auqrqyq91bVNVX17K7dtchYVdXtq+odVfXu7lp8cdd+n6p6e3fNvaaqbte1f0W3/cFu/3mTrJ+Npaq2VNXhqnp9t+06ZOyq6sNVdaSqrqqqQ13bVP99FlhPQ1VtSfLbSb4ryf2TPLmq7j/ZqtjgXp7kO5e0PT/Jm1pr90vypm47GVyX9+sez0zykjHVyMZ3S5Lnttbun+QhSX6s+/8+1yLjdnOSR7fWHpjkgiTfWVUPSfJLSX6ttfZ1ST6T5Bnd65+R5DNd+691r4Mz5dlJ3je07TpkUh7VWrtgaPmaqf77LLCengcn+WBr7UOttX9N8uokj59wTWxgrbW3JPn0kubHJ7mse35Zkr1D7X/UBv5Pkq1VdY/xVMpG1lq7rrX2ru75jRn8B9qOuBYZs+6a+ny3OdM9WpJHJ/mzrn3ptbh4jf5Zkm+tqhpTuWxgVXXPJI9J8gfddsV1SH9M9d9ngfX07Ejy0aHtj3VtME7nttau655/PMm53XPXJyPXDWXbleTtcS0yAd0wzKuSXJ/kjUn+Jcl8a+2W7iXD19uJa7Hb/9kkdx9vxWxQv57kp5N8qdu+e1yHTEZL8oaqemdVPbNrm+q/z2dPugDgzGmttaoy9TdjUVV3SvLnSZ7TWvvccAeBa5Fxaa0dT3JBVW1N8rok3zDhkthkqup7klzfWntnVT1y0vWw6T2itTZXVV+V5I1V9f7hndP491kP6+mZS3Kvoe17dm0wTp9YHL7R/by+a3d9MjJVNZNBWH1la+3yrtm1yMS01uaTXJnkoRkMa1v8R/nh6+3Etdjtv0uST425VDaehyd5XFV9OIPbwx6d5DfiOmQCWmtz3c/rM/hHvAdnyv8+C6yn55+S3K+bBe52SZ6U5IoJ18Tmc0WSp3fPn57kL4baf6CbAe4hST47NBwEbrPuXquXJnlfa+1Xh3a5FhmrqtrW9aymqmaTfHsG91RfmeQJ3cuWXouL1+gTkry5WZCe09Rau6i1ds/W2nkZ/Lfgm1trT4nrkDGrqjtW1Z0Xnyf5jiTvyZT/fS7/+zg9VfXdGdy3sCXJy1prvzDhktjAqupPkjwyyTlJPpHkhUkOJHltknsn+UiSJ7bWPt2Fit/KYFbhm5L8UGvt0CTqZmOpqkck+fskR/Ll+7VekMF9rK5FxqaqHpDBBCJbMvhH+Ne21n62qr42g56uuyU5nOSprbWbq+r2SV6RwX3Xn07ypNbahyZTPRtRNyT4p1pr3+M6ZNy6a+513ebZSV7VWvuFqrp7pvjvs8AKAABALxkSDAAAQC8JrAAAAPSSwAoAAEAvCawAAAD0ksAKAABALwmsAHCGVdXxqrpq6PH8M3ju86rqPWfqfADQZ2dPugAA2IAWWmsXTLoIAJh2elgBYEyq6sNV9ctVdaSq3lFVX9e1n1dVb66qq6vqTVV176793Kp6XVW9u3s8rDvVlqr6/aq6pqreUFWz3et/oqre253n1RP6mABwxgisAHDmzS4ZEvwfhvZ9trV2fpLfSvLrXdv/THJZa+0BSV6Z5De79t9M8nettQcmeVCSa7r2+yX57dbaNyaZT/Lvu/bnJ9nVnedHRvXhAGBcqrU26RoAYEOpqs+31u60TPuHkzy6tfahqppJ8vHW2t2r6pNJ7tFaO9a1X9daO6eqbkhyz9bazUPnOC/JG1tr9+u2n5dkprX281X1t0k+n+RAkgOttc+P+KMCwEjpYQWA8WorPF+Pm4eeH8+X56R4TJLfzqA39p+qylwVAEw1gRUAxus/DP18W/f8H5M8qXv+lCR/3z1/U5JnJUlVbamqu6x00qo6K8m9WmtXJnlekrskuVUvLwBME//yCgBn3mxVXTW0/bettcWlbe5aVVdn0Ev65K7tvyT5w6ral+SGJD/UtT87yaVV9YwMelKfleS6Fd5zS5I/7kJtJfnN1tr8GftEADAB7mEFgDHp7mHd3Vr75KRrAYBpYEgwAAAAvaSHFQAAgF7SwwoAAEAvCawAAAD0ksAKAABALwmsAAAA9JLACgAAQC8JrAAAAPTS/w+k3RE41giacQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZQdPXPhOawN"
      },
      "source": [
        "Below is the plot of gradient norm vs epoch (considering total number of epoch = 100).\n",
        "In that case, the test accuracy of the model on the 10000 test images was 95.22 %."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "iMt_BUjQ9ma3",
        "outputId": "907d27c4-3024-4042-91f1-3842d37cb36e"
      },
      "source": [
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Norm of the gradient\")\n",
        "plt.scatter(ep,gradient_norm)\n",
        "plt.plot(ep,gradient_norm)\n",
        "plt.title(\"Gradient norm vs Epoch\")\n",
        "plt.savefig('scatter plot of gradient norm vs epoch (with 100 epoch).pdf', dpi = 300)\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llLUQVnYKzdH"
      },
      "source": [
        "from google.colab import files\n",
        "#files.download('scatter plot of gradient norm vs epoch (with 100 epoch).pdf')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oS0MMXAJMhYr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "a68c45a7-fe87-4c1f-b1fa-af6721489aeb"
      },
      "source": [
        "files.download('scatter plot of gradient norm vs epoch (with 500 epoch).pdf')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_61be7941-207f-49b5-9915-a53c2cd9ba07\", \"scatter plot of gradient norm vs epoch (with 500 epoch).pdf\", 13850)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFmd-H97gzpa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}