tensorflow4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tensorflow4.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/utkarsh0702/Tensorflow/blob/master/tensorflow4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnf2PfhxvZ8T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JqK_CnUv3P_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7e89c757-2beb-4edb-de4d-126265174128"
      },
      "source": [
        "x= np.array([-1.0,0.0,1.0,2.0,3.0,4.0], dtype= float)\n",
        "y= np.array([-3.0,-1.0,1.0,3.0,5.0,7.0], dtype= float)\n",
        "\n",
        "#creating model and training\n",
        "model= keras.Sequential([keras.layers.Dense(1, input_shape=[1])\n",
        "                        ])\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
        "\n",
        "model.fit(x,y, epochs=500)\n",
        "model.predict([10.0])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "6/6 [==============================] - 1s 84ms/sample - loss: 25.3654\n",
            "Epoch 2/500\n",
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            "Epoch 3/500\n",
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            "Epoch 4/500\n",
            "6/6 [==============================] - 0s 458us/sample - loss: 13.0384\n",
            "Epoch 5/500\n",
            "6/6 [==============================] - 0s 353us/sample - loss: 10.5324\n",
            "Epoch 6/500\n",
            "6/6 [==============================] - 0s 353us/sample - loss: 8.5551\n",
            "Epoch 7/500\n",
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            "Epoch 8/500\n",
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            "Epoch 9/500\n",
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            "Epoch 10/500\n",
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            "Epoch 11/500\n",
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            "Epoch 12/500\n",
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            "Epoch 499/500\n",
            "6/6 [==============================] - 0s 271us/sample - loss: 4.9177e-05\n",
            "Epoch 500/500\n",
            "6/6 [==============================] - 0s 292us/sample - loss: 4.8167e-05\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18.979752]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xxwPBxBwAtK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "0b23d9d8-1998-4d12-b17f-fc64f288f9c7"
      },
      "source": [
        "#Neural Network for Fashion MNIST\n",
        "fashion_mnist= keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels),(test_images, test_labels)= fashion_mnist.load_data()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99tsxTzPwBXM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "56e54fee-1b56-444f-a569-a06d217349f6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(train_images[0])\n",
        "print(train_images[0])\n",
        "print(train_labels[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "   54   0   0   0   1   3   4   0   0   3]\n",
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            "9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V+zEDgBkJ988+r8beitqa9goAG1P2z/Y3M18z659l3Eui/37gdLPt1kH3Yw4A00KWTN/S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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9-c0vgDyoaJ",
        "colab_type": "text"
      },
      "source": [
        "#**Normalization**\n",
        "To reduce the size of the 0 to 255 range image to 0 to 1 for the convience of the Neural Net. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ddCskvSwB36",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images= train_images/255.0\n",
        "test_images= test_images/255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Dvnqr510TOn",
        "colab_type": "text"
      },
      "source": [
        "# **Terminologies**\n",
        "\n",
        "\n",
        "1.   **Sequential -** It defines sequence of layers of neural network.\n",
        "2.   **Flatten -** It takes the square matrix of an image and convert it into 1D matrix. \n",
        "3.   **Dense -** Adds a layer of neuron.\n",
        "4.   **Relu -** It is an activation function that: If X>0 then X otherwise 0.\n",
        "5.   **Softmax -** Find the max element from the array of output and convert the max element to 1 and others to 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-18K4Y8wCbr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# creating the model\n",
        "model= keras.models.Sequential([keras.layers.Flatten(), \n",
        "                                keras.layers.Dense(128, activation= tf.nn.relu),\n",
        "                                keras.layers.Dense(10, activation= tf.nn.softmax)\n",
        "                               ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wjvy5HsgwDCj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "4b5c4f3e-3d33-4a38-e145-395e19df94e6"
      },
      "source": [
        "model.compile(optimizer= tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy')\n",
        "\n",
        "model.fit(train_images, train_labels, epochs=5)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.4982\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 65us/sample - loss: 0.3747\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.3357\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.3134\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 62us/sample - loss: 0.2947\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ffb127851d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8rSRL6iv_A1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "71e6f1f0-eac7-439f-ba17-b0f77ea0f4d3"
      },
      "source": [
        "model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 30us/sample - loss: 0.3474\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.34735001181960107"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXlFe8DU3DBf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "da7d66ec-1787-4f9b-f21d-eab2afeba28e"
      },
      "source": [
        "# to cancel the training in middle of epochs\n",
        "class myCall(keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('loss')<0.3):\n",
        "      print('\\nThe Training is Cancelled.....')\n",
        "      self.model.stop_training= True\n",
        "\n",
        "callbacks= myCall()\n",
        "fashion_mnist= keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels),(test_images, test_labels)= fashion_mnist.load_data()\n",
        "train_images= train_images/255.0\n",
        "test_images= test_images/255.0\n",
        "# creating the model\n",
        "model= keras.models.Sequential([keras.layers.Flatten(), \n",
        "                                keras.layers.Dense(128, activation= tf.nn.relu),\n",
        "                                keras.layers.Dense(10, activation= tf.nn.softmax)\n",
        "                               ])\n",
        "\n",
        "model.compile(optimizer= tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy')\n",
        "\n",
        "model.fit(train_images, train_labels, epochs=7, callbacks=[callbacks])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "60000/60000 [==============================] - 4s 62us/sample - loss: 0.4997\n",
            "Epoch 2/7\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.3767\n",
            "Epoch 3/7\n",
            "60000/60000 [==============================] - 4s 62us/sample - loss: 0.3365\n",
            "Epoch 4/7\n",
            "60000/60000 [==============================] - 4s 62us/sample - loss: 0.3112\n",
            "Epoch 5/7\n",
            "59264/60000 [============================>.] - ETA: 0s - loss: 0.2921\n",
            "The Training is Cancelled.....\n",
            "60000/60000 [==============================] - 4s 62us/sample - loss: 0.2924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ffb0c024828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSJWPCvq3DwD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}