Introduction_to_neural_network.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sudhirJain/Colab-Research/blob/master/Introduction_to_neural_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fAZdyclSL5s1",
        "colab_type": "code",
        "outputId": "292fb32e-8dcd-42f9-8790-4e437779161d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        }
      },
      "source": [
        "# The following is used to install the new version of tensorflow version 2.2.0\n",
        "!pip install tensorflow --upgrade"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: tensorflow in /usr/local/lib/python3.6/dist-packages (2.2.0rc2)\n",
            "Requirement already satisfied, skipping upgrade: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.18.2)\n",
            "Requirement already satisfied, skipping upgrade: tensorboard<2.3.0,>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (2.2.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.27.2)\n",
            "Requirement already satisfied, skipping upgrade: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (3.2.0)\n",
            "Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied, skipping upgrade: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (3.10.0)\n",
            "Requirement already satisfied, skipping upgrade: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.3.3)\n",
            "Requirement already satisfied, skipping upgrade: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (2.10.0)\n",
            "Requirement already satisfied, skipping upgrade: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.34.2)\n",
            "Requirement already satisfied, skipping upgrade: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.12.1)\n",
            "Requirement already satisfied, skipping upgrade: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied, skipping upgrade: tensorflow-estimator<2.3.0,>=2.2.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (2.2.0rc0)\n",
            "Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.9.0)\n",
            "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (3.2.1)\n",
            "Requirement already satisfied, skipping upgrade: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (2.21.0)\n",
            "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (1.0.0)\n",
            "Requirement already satisfied, skipping upgrade: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (1.7.2)\n",
            "Requirement already satisfied, skipping upgrade: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (46.0.0)\n",
            "Requirement already satisfied, skipping upgrade: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (0.4.1)\n",
            "Requirement already satisfied, skipping upgrade: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow) (1.6.0.post2)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow) (2019.11.28)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow) (2.8)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (3.1.1)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (0.2.8)\n",
            "Requirement already satisfied, skipping upgrade: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (4.0)\n",
            "Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow) (1.3.0)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow) (0.4.8)\n",
            "Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow) (3.1.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfiNj29ENWj3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roVqRIR7OERe",
        "colab_type": "code",
        "outputId": "f5b6ff66-4c99-4ba5-c725-9813106f260e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.0-rc2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vc10lUxSOQjP",
        "colab_type": "code",
        "outputId": "86be6c3c-e449-4d68-8002-56f9c3f1838f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# Checking GPU avaliablet\n",
        "tf.test.is_gpu_available()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-4-17bb7203622b>:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4y56Zr_OjUX",
        "colab_type": "code",
        "outputId": "321140c8-9185-431e-b05f-6bae67f10aa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from tensorflow import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import to_categorical\n",
        "from keras import layers, models\n",
        "from tensorflow.python.keras.models import Sequential\n",
        "from tensorflow.python.keras.layers import Dense, Activation\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jks7NS5MOmzw",
        "colab_type": "code",
        "outputId": "52ee8198-b84c-41f9-9cff-09821f232f76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "(train_data, train_labels), (test_data, test_labels) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOA_JNjWOw63",
        "colab_type": "code",
        "outputId": "3625c334-2577-4a23-caee-aa4ba59d7e62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2C_ga_YrO3qX",
        "colab_type": "code",
        "outputId": "b56f3a4e-1c6f-4ca7-a3bd-630c2d405526",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIO9WikRPNGP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_data.reshape((60000,28*28)).astype('float32') / 255\n",
        "test_images = test_data.reshape((10000,28*28)).astype('float32') / 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5aAKwTDPB-o",
        "colab_type": "code",
        "outputId": "910d9199-90b7-49b8-f4fc-2585a8041d77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HasqxukkPMZo",
        "colab_type": "code",
        "outputId": "9b7f95ce-8598-4f42-a31e-5468dd78fcfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcJEMtD6PRqM",
        "colab_type": "code",
        "outputId": "da1d6fc4-96e0-47e4-8a14-5ff1c0b4b040",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rz2PgllrPSVT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels = to_categorical(train_labels)\n",
        "test_labels = to_categorical(test_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3baKouvQQUIW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(256, activation='relu', input_dim=784))\n",
        "model.add(Dense(10, activation='softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EPGHWUBQdc-",
        "colab_type": "code",
        "outputId": "6afec049-29ae-499b-e9e9-c0bb0d3d161a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "model.summary() # Bias is also added to it"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 256)               200960    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 203,530\n",
            "Trainable params: 203,530\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xH9ygrTBYZ9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-GEjRlYNU6l",
        "colab_type": "code",
        "outputId": "3dfe67cd-d19b-4313-9d66-81a98ebfeefa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "model_history = model.fit(train_images, train_labels, epochs=5, batch_size=128)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.2892 - acc: 0.9179\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.1274 - acc: 0.9622\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0873 - acc: 0.9743\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0659 - acc: 0.9807\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0517 - acc: 0.9847\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZXw0szoUL52",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "epochs = range(5)\n",
        "train_acc = model.history.history['acc']\n",
        "train_loss = model.history.history['loss']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHF6cRm-Vyi8",
        "colab_type": "code",
        "outputId": "ebe2da10-220a-4c74-cc02-ab5fec108771",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "plt.plot(epochs, train_acc,'r')\n",
        "plt.title('Plot Accuracy of Taining')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6a106e0940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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85CfQo0fe0ZhZO+EaREu3ejWceSbsvntqXjIzq5CyJghJB0qaK2m+pLFFjveT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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XeM_emlfX3WO",
        "colab_type": "code",
        "outputId": "8f7e55aa-80a7-419e-8d5a-35d6c9ef7116",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "plt.plot(epochs, train_loss,'r')\n",
        "plt.title('Plot Loss of Taining')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6a1022c0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MUjvG7NnLt2M8/3za1qNHmk2vPjB23jmFiHV4DggzK23BguXvx3jssXTmAbDF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\nleSAMDOzkhwQZmZWkgPCzMxK+v/jAtGCiSAYBQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1ihpUCLOVjy",
        "colab_type": "code",
        "outputId": "5d00df6e-f181-4255-e42d-f33c02b283d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0785 - acc: 0.9750\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ljrFLDdTMk6",
        "colab_type": "code",
        "outputId": "a16f8fcb-e323-436f-8b18-265e574663f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_loss"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07846779376268387"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYi3GABQTT3C",
        "colab_type": "code",
        "outputId": "53957326-5fbe-41d7-f8ed-abf1ae799294",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_accuracy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9750000238418579"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cz5MQt6lTVko",
        "colab_type": "text"
      },
      "source": [
        "Tensor Flow Basics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXcgBU8C9J8o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anDQV-Zw-Q4e",
        "colab_type": "code",
        "outputId": "6125349a-adb1-49d4-deb3-0829d45d2743",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.0-rc2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIrNSOmF-eA9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# pip install torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPA57VyH_HZo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOSwbhYR3JPP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5l-eeW0r4jOE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = torch.Tensor(4,5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4mlLubV48IP",
        "colab_type": "code",
        "outputId": "155b1807-17b2-43b7-fecd-e3b7355b6b15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.type"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function Tensor.type>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLj9zTNc4998",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = torch.Size([3,4])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jzu5k-_N5dLi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z = torch.zeros(3,4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNOlV44X50Lx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Tensor operations"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6kyGf8m7XEA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = torch.Tensor(3,4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ttEeCBP7hZ-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = torch.Tensor(3,4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GAcpwViQ7mzx",
        "colab_type": "code",
        "outputId": "49832086-8cb9-4f7c-8888-71f9b5ad80c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "x + y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2.5516e-03, 0.0000e+00, 8.9683e-44, 0.0000e+00],\n",
              "        [       nan, 0.0000e+00, 1.2966e-09, 1.6605e-09],\n",
              "        [3.5323e-04, 8.3996e-08, 2.6622e+22, 5.3107e-06]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0l0Z4eCt7obx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = x.sum(dim=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBnhHSCHC7PG",
        "colab_type": "code",
        "outputId": "c025c76c-b3de-4886-90b8-05f38e88020a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([       nan, 4.1998e-08, 1.3311e+22, 2.6562e-06])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hl3A12voC84T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# AutoGrad and Gradients"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14fuxhEDDLoy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = torch.rand(3,3, requires_grad=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UX5cVlyDugZ",
        "colab_type": "code",
        "outputId": "8a21471a-e6b2-470c-94f8-6f68211fa1d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.2782, 0.2206, 0.9308],\n",
              "        [0.5963, 0.8187, 0.0310],\n",
              "        [0.6678, 0.4071, 0.4855]], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqtFH-8LDwsX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = x**3 + 7"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXj9Qt8sEwAF",
        "colab_type": "code",
        "outputId": "925fc281-b32f-4a4f-9b7b-d6b42b728e9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[7.0215, 7.0107, 7.8064],\n",
              "        [7.2121, 7.5487, 7.0000],\n",
              "        [7.2979, 7.0675, 7.1144]], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8wcFT-6ExkI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z = y.sum(dim=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jf0vr3wxE5x9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z = z.sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSWwLmZBF-1V",
        "colab_type": "code",
        "outputId": "cb41d96e-d8ea-4334-f183-5b8405269b24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "z "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(65.0792, grad_fn=<SumBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDAZmMHtE7lB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "z.backward()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21_6lPsJFI_R",
        "colab_type": "code",
        "outputId": "70e6af76-d465-4f36-c13c-539bc35b03fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "x.grad"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.2322, 0.1460, 2.5991],\n",
              "        [1.0669, 2.0106, 0.0029],\n",
              "        [1.3380, 0.4972, 0.7071]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gpcgb9B4FUGD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !pip install torch torchvision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7J3fYhhYHt6m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torchvision\n",
        "import matplotlib.pyplot as plt\n",
        "from torch import nn\n",
        "import torch.nn.functional as P\n",
        "from torchvision import datasets, transforms\n",
        "from collections import OrderedDict"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4G5whoWNt9s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,),(0.5,))])\n",
        "# transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0,5],[0.5])])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mORtBKAQBQo",
        "colab_type": "code",
        "outputId": "93575510-7cae-4deb-f783-86df1a70bf92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349,
          "referenced_widgets": [
            "fff62bd526ac41c092b76d9e36b58b6d",
            "b5078c6d9521496bb1b48404e667f02f",
            "a105fca47a95414a95e0c0c71d471143",
            "f17a9167bb374734887e9e3bfafe26f4",
            "0ec9bab417fe49a1b0baa729c0c7ce62",
            "8db89409899c45e5ba6f20f0ae01680c",
            "223e34d8aa344d2b8db6a4eaa8e93682",
            "2e63ce3068f741c08f5ef20cd4e80e36",
            "a705a0e753b742fbbdd881d9b7b9156a",
            "68aeca2c31d1420f894abfaabe0b9c21",
            "e89d2d4358114767a2c2c7545bd8aec1",
            "6903f742c65748078ff8bb7f1ecf6465",
            "4f4f9827c19542178282788997f288ef",
            "24e46d9980124ca1ac55279decbfdd08",
            "6cb4dc00381b40869ce534c6c689a208",
            "114462367ad141e8a5207344eefef3b6",
            "5eb4b3e54f06436694352cc391987243",
            "4d91e5aeeec14bbda8ce8f285c99328e",
            "550efda8bb944d9294e3447b9a38bea3",
            "f217ddf1fab242ffa3a8205e1408be50",
            "b758c0bf157c4c389ec80c464deb50dd",
            "f64b80112101450cb3ec12f20a0a3a87",
            "528f0a66f16c4c88b85cd26da627c4f7",
            "09ca8f3114074c3d9a5ede4c3b396d78",
            "eed5ce677be64029a39190c29dbd8a6e",
            "7fc50621dd6343e6b31ce5dac3e375cb",
            "a58dcf5ecd8e42e8aab203f91d510a61",
            "3e25bfba6cd6471da652640085abcb45",
            "622b18fea26a406c87a5a895c7178cd2",
            "433ce3f5e984422aa58416681c29f6a5",
            "517e6d4d9a7e4be88f3e3a821c6aa561",
            "a0337f34bf784e2498265f748a0bc82d"
          ]
        }
      },
      "source": [
        "mnist_train_dataset = datasets.MNIST('mnist_train_dataset', download=True, train=True, transform=transform)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to mnist_train_dataset/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fff62bd526ac41c092b76d9e36b58b6d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting mnist_train_dataset/MNIST/raw/train-images-idx3-ubyte.gz to mnist_train_dataset/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to mnist_train_dataset/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a705a0e753b742fbbdd881d9b7b9156a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting mnist_train_dataset/MNIST/raw/train-labels-idx1-ubyte.gz to mnist_train_dataset/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to mnist_train_dataset/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5eb4b3e54f06436694352cc391987243",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting mnist_train_dataset/MNIST/raw/t10k-images-idx3-ubyte.gz to mnist_train_dataset/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to mnist_train_dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eed5ce677be64029a39190c29dbd8a6e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting mnist_train_dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz to mnist_train_dataset/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amQTt1t5Rh7Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist_train_data_loader = torch.utils.data.DataLoader(mnist_train_dataset, batch_size=64, shuffle=True) # creating data loader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLZGpOZKU4hx",
        "colab_type": "text"
      },
      "source": [
        "##**Check Sample MNIST images**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7AbGJ-tU-gT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset_iterator = iter(mnist_train_data_loader)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZ-gtwauVfj6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images, train_labels = train_dataset_iterator.next()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3fKRkqBVVrwX",
        "colab_type": "code",
        "outputId": "f1d52187-b182-4da0-c63c-56cd4dfab990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_images.shape # 1 means it is gray scacle value"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([64, 1, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkTXG1QnYpqc",
        "colab_type": "code",
        "outputId": "69d5499c-8e75-4a9e-a0a4-9ca3ac57b2d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "train_labels # Shoqing the images of the pictures"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([6, 9, 9, 8, 0, 0, 5, 1, 2, 8, 1, 8, 2, 0, 8, 3, 4, 7, 4, 1, 7, 1, 5, 6,\n",
              "        5, 8, 0, 2, 2, 0, 3, 9, 5, 4, 0, 9, 7, 1, 2, 1, 1, 1, 5, 1, 0, 6, 2, 3,\n",
              "        1, 3, 1, 7, 9, 8, 3, 0, 1, 7, 3, 5, 1, 6, 4, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brwN6kJGZCbO",
        "colab_type": "code",
        "outputId": "feb9a7dd-57c1-4226-b7ce-217d6496e431",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "plt.imshow(train_images[4].numpy().squeeze(), cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f69a59eab38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eyr7ebbs3SVvU2az7X3X2bayS9BeStkvaK+l/JM3ood7+Q52hvd9WJ1h9LfV2szqb6G9L\n2pl9LWl73SX66sp643RZIAh20AFBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEP8HjZGvMbwrpp4AAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Le7cLovdv_6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Build Simple Neural Network\n",
        "\n",
        "class NeuralNetwork(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "    # Defining the neural network layers\n",
        "    self.hidden_layer = nn.Linear(784, 256)\n",
        "\n",
        "    # output layer units - one for each layer\n",
        "\n",
        "    self.output_layer = nn.Linear(256, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    # Pass the input tensor through each of our operations\n",
        "    # Hidden lauer with relu activation\n",
        "\n",
        "    x = F.relu(self.hidden_layer(x))\n",
        "\n",
        "    # output with the softmax activation\n",
        "    x = F.softmax(self.output_layer(x), dim=1)\n",
        "\n",
        "    return x\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7nTRObMATS8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = NeuralNetwork()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNyXVKsWAaVn",
        "colab_type": "code",
        "outputId": "b77d58de-f99c-4256-fd68-134cf5ab0038",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.hidden_layer"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Linear(in_features=784, out_features=256, bias=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDnCv5eoAuEh",
        "colab_type": "code",
        "outputId": "7f74bf5e-e283-4aae-9400-339928d250b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "  model.hidden_layer.weight.data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.0022,  0.0200, -0.0313,  ...,  0.0320, -0.0125, -0.0137],\n",
              "        [ 0.0348, -0.0178,  0.0063,  ...,  0.0276, -0.0123, -0.0238],\n",
              "        [ 0.0198,  0.0167, -0.0080,  ..., -0.0271, -0.0094, -0.0129],\n",
              "        ...,\n",
              "        [ 0.0346,  0.0269,  0.0023,  ..., -0.0187, -0.0209,  0.0211],\n",
              "        [-0.0333, -0.0192,  0.0049,  ..., -0.0136,  0.0210,  0.0294],\n",
              "        [-0.0142, -0.0286, -0.0151,  ...,  0.0057, -0.0209, -0.0290]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLjve8SFA8AY",
        "colab_type": "code",
        "outputId": "ec8dc32d-6f31-462b-dd71-aecf00ddd248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "model.hidden_layer.bias"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([-0.0341,  0.0321,  0.0294, -0.0216, -0.0087,  0.0103, -0.0068, -0.0186,\n",
              "         0.0300, -0.0093, -0.0047, -0.0124,  0.0152,  0.0320,  0.0177,  0.0295,\n",
              "        -0.0201,  0.0050,  0.0022,  0.0239,  0.0231, -0.0214,  0.0337,  0.0191,\n",
              "        -0.0217, -0.0159,  0.0185,  0.0229, -0.0070,  0.0142, -0.0173, -0.0108,\n",
              "         0.0263,  0.0231,  0.0150, -0.0177, -0.0071, -0.0248, -0.0191, -0.0129,\n",
              "         0.0045, -0.0044,  0.0353,  0.0304, -0.0177, -0.0124,  0.0163,  0.0154,\n",
              "         0.0210,  0.0194,  0.0147,  0.0103, -0.0045, -0.0074, -0.0318,  0.0127,\n",
              "         0.0047, -0.0213,  0.0116, -0.0343,  0.0179,  0.0212,  0.0020,  0.0338,\n",
              "         0.0222, -0.0073, -0.0116, -0.0304, -0.0295, -0.0162,  0.0149, -0.0348,\n",
              "        -0.0297, -0.0053,  0.0269,  0.0048,  0.0191, -0.0122,  0.0257, -0.0101,\n",
              "         0.0305,  0.0316,  0.0190,  0.0351,  0.0066,  0.0044,  0.0149, -0.0236,\n",
              "        -0.0091,  0.0077,  0.0139,  0.0200,  0.0098,  0.0008, -0.0043,  0.0307,\n",
              "         0.0112, -0.0184,  0.0004, -0.0086,  0.0346, -0.0255,  0.0070,  0.0261,\n",
              "         0.0351, -0.0255,  0.0016, -0.0273,  0.0196,  0.0107, -0.0180, -0.0104,\n",
              "        -0.0208,  0.0154,  0.0125,  0.0173,  0.0063, -0.0239, -0.0066,  0.0192,\n",
              "        -0.0194, -0.0018,  0.0005, -0.0189, -0.0168, -0.0277,  0.0249, -0.0318,\n",
              "         0.0178, -0.0313,  0.0167, -0.0205, -0.0251, -0.0308,  0.0309,  0.0047,\n",
              "        -0.0201,  0.0287,  0.0154, -0.0073,  0.0312, -0.0086,  0.0261, -0.0205,\n",
              "        -0.0136, -0.0233, -0.0154,  0.0067, -0.0104, -0.0015,  0.0290, -0.0050,\n",
              "         0.0146,  0.0097,  0.0177, -0.0119,  0.0329,  0.0163,  0.0097,  0.0170,\n",
              "        -0.0094, -0.0223, -0.0167, -0.0215, -0.0175, -0.0219, -0.0197,  0.0165,\n",
              "        -0.0228,  0.0215,  0.0086,  0.0233, -0.0087, -0.0121, -0.0041,  0.0033,\n",
              "         0.0167, -0.0298, -0.0097,  0.0155,  0.0086, -0.0147,  0.0292,  0.0006,\n",
              "         0.0282, -0.0045,  0.0318, -0.0282, -0.0061,  0.0285,  0.0051, -0.0354,\n",
              "         0.0316,  0.0097,  0.0225,  0.0159, -0.0185, -0.0144, -0.0345,  0.0110,\n",
              "         0.0001,  0.0036, -0.0318,  0.0240, -0.0115, -0.0215,  0.0198,  0.0076,\n",
              "         0.0244,  0.0029,  0.0205, -0.0064,  0.0063, -0.0106,  0.0201, -0.0225,\n",
              "        -0.0235,  0.0020, -0.0081, -0.0079,  0.0012,  0.0017,  0.0159, -0.0289,\n",
              "        -0.0168, -0.0051,  0.0201, -0.0194, -0.0290, -0.0239,  0.0275,  0.0283,\n",
              "        -0.0288, -0.0095, -0.0297, -0.0044,  0.0342, -0.0131,  0.0045, -0.0151,\n",
              "         0.0225, -0.0126, -0.0344,  0.0256,  0.0090,  0.0342,  0.0300, -0.0054,\n",
              "        -0.0189, -0.0095, -0.0197, -0.0021,  0.0076, -0.0325, -0.0188,  0.0200],\n",
              "       requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmg4yAPqFeYI",
        "colab_type": "text"
      },
      "source": [
        "##**Define Loss Function**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nj97pgjTGMBv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGelF2JMHKUR",
        "colab_type": "text"
      },
      "source": [
        "##**Calculating the loss for single image**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrvVozPEHR8_",
        "colab_type": "code",
        "outputId": "7df11c16-0889-40ff-d839-7bb496437a78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([64, 1, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9L22yrs_HdJB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images_batch = train_images.view(train_images.shape[0],-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Igyda9cPHxT3",
        "colab_type": "code",
        "outputId": "fc7cb837-7d90-46a2-b8d5-0f036dee39bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_images_batch.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([64, 784])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6gStXB8IAOW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images_batch = train_images.view(train_images.shape[0], -1) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5FoDTmNLMBX",
        "colab_type": "code",
        "outputId": "363c927a-b00b-450b-f765-7275e942a78c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_images_batch.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([64, 784])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDacfHkpLSeT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output_predictions = model(train_images_batch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47UFyNCHLfZi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = criterion(output_predictions, train_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bByt_9oYNKHb",
        "colab_type": "code",
        "outputId": "aded7447-0f30-4ed6-800f-d1a6e9ab2896",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loss"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(2.3001, grad_fn=<NllLossBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XrAAr72SNR0c",
        "colab_type": "text"
      },
      "source": [
        "##**Train the Network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cBeMq_dNVsR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = torch.optim.SGD(model.parameters(),lr=0.01)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6aPcLhROF76",
        "colab_type": "text"
      },
      "source": [
        "##**Training using the train images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mH46cLkqONxh",
        "colab_type": "code",
        "outputId": "be3f0b3b-e223-4c97-dcdd-353ca9f21e3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "epochs = 8\n",
        "for e in range(epochs):\n",
        "  training_loss = 0\n",
        "  for train_images, train_labels in mnist_train_data_loader:\n",
        "    # Flatten train_images batch\n",
        "    images = train_images.view(train_images.shape[0],-1)\n",
        "    # Make the gradient values to zero\n",
        "    optimizer.zero_grad()\n",
        "    # get out predictions from the images\n",
        "    output_predictions = model(images)\n",
        "    #calculate loss\n",
        "    loss = criterion(output_predictions,train_labels)\n",
        "    # Do backward to find the gradients\n",
        "    loss.backward()\n",
        "    #update the weights of the parameters\n",
        "    optimizer.step()\n",
        "    training_loss += loss.item()\n",
        "  else:\n",
        "    print (f\"training loss for epoch - {e} {training_loss/len(mnist_train_data_loader)}\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training loss for epoch - 0 2.158570678122262\n",
            "training loss for epoch - 1 1.8622147311279769\n",
            "training loss for epoch - 2 1.7554249950309297\n",
            "training loss for epoch - 3 1.6937631728298375\n",
            "training loss for epoch - 4 1.6691004791493609\n",
            "training loss for epoch - 5 1.6568279340068923\n",
            "training loss for epoch - 6 1.6470590462562626\n",
            "training loss for epoch - 7 1.6122722560917135\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SrdaC9m1w9QB",
        "colab_type": "text"
      },
      "source": [
        "###**Inference on a digit**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0xwvnVXyHZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images, labels = next(iter(mnist_train_data_loader))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XuzyirHa_TJd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "infer_img = images[0].view(1, 784)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qxofh3ZJ_kP2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with torch.no_grad():\n",
        "  output_prediction = model(infer_img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZGTqZxI_0cA",
        "colab_type": "code",
        "outputId": "96c86dbb-cbc1-41eb-90c8-b74c340fb284",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "output_prediction.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oUIPBmvH_6zT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "probabilities = output_prediction.data.numpy().squeeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGgJYZJoAL95",
        "colab_type": "code",
        "outputId": "666ae42d-bfb6-4bb1-e4bf-8217c71f2fa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "probabilities.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TXdeGbyAXYH",
        "colab_type": "code",
        "outputId": "3f9f6997-db4a-4bd8-8ee0-b56b5b52b693",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "probabilities # 1 is having the largest probability"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.9712902e-01, 4.5845177e-12, 7.0339942e-05, 2.9743527e-05,\n",
              "       1.7471380e-11, 2.7687794e-03, 1.7843725e-07, 8.4279272e-08,\n",
              "       1.8002164e-06, 2.6165829e-08], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpH2JrxICjNw",
        "colab_type": "code",
        "outputId": "688f5e97-224b-409e-cbd2-4444fe9987cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "plt.imshow(infer_img.resize(1,28,28).numpy().squeeze(), cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/tensor.py:362: UserWarning: non-inplace resize is deprecated\n",
            "  warnings.warn(\"non-inplace resize is deprecated\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f69a5896780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2to91Z-C9CJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}