google_co_lab_fashion_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "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/shahkv95/Deep-learning---tf/blob/master/google_co_lab_fashion_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "JNWe2V5Kr9sA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dcb66d90-f7c1-4f7b-fcdf-4992119e1b22"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bCs8z-yosYDG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mnist = tf.keras.datasets.fashion_mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "l10r-IYXsqJn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "(training_images,training_labels),(test_images,test_labels) = mnist.load_data() "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P89A1uXata6X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1316
        },
        "outputId": "f7486b89-4d3a-4438-c604-7f9099592544"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(training_images[0])\n",
        "print(training_labels[0])\n",
        "print(training_images[0])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fv4XVfFm9erXffX/96189tgcOHBjy4//iF78w93VSQeOvguUHP/iBx3Z1dbV5zEAVXN6s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\nAGBAWAKAwf8Avly+Ah0+3gYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "O5BsyVfuty_A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "training_images = training_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QrBLYhJ-uO4z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation = tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation = tf.nn.softmax)\n",
        "]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bpHsXUOsyz6x",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OSRukmnPvmBW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "57340308-1fd3-4370-c6e6-e112ad373836"
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = tf.train.AdamOptimizer(),\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(training_images, training_labels, epochs=5)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 5s 87us/sample - loss: 0.5001 - acc: 0.8254\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 5s 80us/sample - loss: 0.3742 - acc: 0.8645\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 5s 80us/sample - loss: 0.3337 - acc: 0.8780\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 5s 80us/sample - loss: 0.3113 - acc: 0.8862\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 5s 80us/sample - loss: 0.2938 - acc: 0.8915\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8a31f3e630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "IsRfNH1cwqri",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "cd03e485-0612-4c30-8d9a-295e26ffc346"
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(test_images, test_labels)"
      ],
      [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 40us/sample - loss: 0.3487 - acc: 0.8741\n"
          