TF2_0_Fashion_MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyNX95CXgNhLlB8Qy7q3ag0Y",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sreya-rc/TF2.0/blob/main/TF2_0_Fashion_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrhUusqcPntF",
        "outputId": "0b60559c-9664-47a2-dc1b-3b2f3bad8f62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "2.12.0\n"
          ]
        }
      ],
      "source": [
        "# Install TensorFlow\n",
        "try:\n",
        "  %tensorflow_version 2.x # Colab only.\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Additional imports\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Dropout\n",
        "from tensorflow.keras.models import Model"
      ],
      "metadata": {
        "id": "Zfr0wJGmP1-9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in the data\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
        "x_train, x_test = x_train/255.0, x_test /255.0\n",
        "print(\"x_train.shape:\", x_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsDblTbpO4yA",
        "outputId": "8e18176d-2713-4d70-b5ee-015e7920201c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n",
            "x_train.shape: (60000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data is only 2D!\n",
        "# Convolution expects height x width x color\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "print(x_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZteDZQvTM3I",
        "outputId": "f184cff3-07d3-47d0-84a6-d32fca735305"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of classes\n",
        "K = len(set(y_train))\n",
        "print(\"number of classes:\", K)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpUQc80nTvSu",
        "outputId": "717b668e-5a14-4fe7-cc76-6039d8dd79d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of classes: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the model using the functional API\n",
        "\n",
        "i = Input(shape=x_train[0].shape)\n",
        "x = Conv2D(32, (3, 3), strides=2, activation='relu')(i)\n",
        "x = Conv2D(64, (3, 3), strides=2, activation='relu')(x)\n",
        "x = Conv2D(128, (3, 3), strides=2, activation='relu')(x)\n",
        "x = Flatten()(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(K, activation='softmax')(x)\n",
        "\n",
        "model = Model(i, x)"
      ],
      "metadata": {
        "id": "LE6nbKuuElER"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile and fit\n",
        "# Note: make sure you use the GPU for this!\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "r = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yhxagVRIdnk",
        "outputId": "91e6127f-81d1-4e79-d7ec-759adf9014af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "1875/1875 [==============================] - 22s 6ms/step - loss: 0.5300 - accuracy: 0.8041 - val_loss: 0.4239 - val_accuracy: 0.8448\n",
            "Epoch 2/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3675 - accuracy: 0.8613 - val_loss: 0.3660 - val_accuracy: 0.8649\n",
            "Epoch 3/15\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.3201 - accuracy: 0.8791 - val_loss: 0.3339 - val_accuracy: 0.8766\n",
            "Epoch 4/15\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.2859 - accuracy: 0.8918 - val_loss: 0.3173 - val_accuracy: 0.8801\n",
            "Epoch 5/15\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.2624 - accuracy: 0.9009 - val_loss: 0.3074 - val_accuracy: 0.8895\n",
            "Epoch 6/15\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.2389 - accuracy: 0.9087 - val_loss: 0.2899 - val_accuracy: 0.8980\n",
            "Epoch 7/15\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2229 - accuracy: 0.9158 - val_loss: 0.3124 - val_accuracy: 0.8944\n",
            "Epoch 8/15\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2077 - accuracy: 0.9194 - val_loss: 0.2892 - val_accuracy: 0.8973\n",
            "Epoch 9/15\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1949 - accuracy: 0.9253 - val_loss: 0.2999 - val_accuracy: 0.8986\n",
            "Epoch 10/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1796 - accuracy: 0.9316 - val_loss: 0.3225 - val_accuracy: 0.8999\n",
            "Epoch 11/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1686 - accuracy: 0.9349 - val_loss: 0.3072 - val_accuracy: 0.8994\n",
            "Epoch 12/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1588 - accuracy: 0.9391 - val_loss: 0.3219 - val_accuracy: 0.8954\n",
            "Epoch 13/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1498 - accuracy: 0.9425 - val_loss: 0.3392 - val_accuracy: 0.8968\n",
            "Epoch 14/15\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1427 - accuracy: 0.9444 - val_loss: 0.3387 - val_accuracy: 0.9037\n",
            "Epoch 15/15\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1364 - accuracy: 0.9474 - val_loss: 0.3468 - val_accuracy: 0.9006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot loss per iteration\n",
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.plot(r.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "Yxrx0U30JngR",
        "outputId": "13e73d65-73cb-403a-9813-eb78359ac481"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f236b94a860>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot accuracy per iteration\n",
        "plt.plot(r.history['accuracy'], label='acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val_acc')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "3o6pBkE5KskO",
        "outputId": "f5fda0e5-f1e6-4f0c-c34d-05d738dc52ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2350776a10>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot confusion matrix\n",
        "\n",
        "# Plot confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "import itertools\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title=\"Confusion Matrix\",\n",
        "                          cmap=plt.cm.Blues):\n",
        "  \"\"\"\n",
        "  This function prints and plots the confusion matrix.\n",
        "  Normalization can be applied by setting 'normalize=True'.\n",
        "  \"\"\"\n",
        "\n",
        "  if normalize:\n",
        "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    print(\"Normalized Confusion Matrix\")\n",
        "  else:\n",
        "    print(\"Confusion matrix, without normalization\")\n",
        "\n",
        "  print(cm)\n",
        "\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  fmt = '.2f' if normalize else 'd'\n",
        "  thresh = cm.max() / 2.\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, format(cm[i, j], fmt),\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  plt.show()\n",
        "\n",
        "p_test = model.predict(x_test).argmax(axis=1)\n",
        "cm = confusion_matrix(y_test, p_test)\n",
        "plot_confusion_matrix(cm, list(range(10)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "l1nlginyKyQh",
        "outputId": "8c778c57-e3bb-42a0-e7bc-ae8f4351b78c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n",
            "Confusion matrix, without normalization\n",
            "[[828   1  28  21   2   1 116   0   3   0]\n",
            " [  2 975   0  14   3   0   6   0   0   0]\n",
            " [ 10   0 852  12  52   0  74   0   0   0]\n",
            " [ 14   4  11 923  21   3  23   0   1   0]\n",
            " [  1   1  54  31 838   0  75   0   0   0]\n",
            " [  0   0   0   0   0 968   0  22   1   9]\n",
            " [ 98   1  52  27  87   0 728   0   7   0]\n",
            " [  0   0   0   0   0  12   0 959   0  29]\n",
            " [  9   1   3   4   2   2   9   1 967   2]\n",
            " [  1   0   0   0   0   6   0  25   0 968]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Label mapping\n",
        "labels = '''T-shirt/top\n",
        "Trouser\n",
        "Pullover\n",
        "Dress\n",
        "Coat\n",
        "Sandal\n",
        "Shirt\n",
        "Sneaker\n",
        "Bag\n",
        "Ankle boot'''.split()"
      ],
      "metadata": {
        "id": "1QUtpel-MDtR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Show some misclassified samples\n",
        "misclassified_idx = np.where(p_test != y_test)[0]\n",
        "i = np.random.choice(misclassified_idx)\n",
        "plt.imshow(x_test[i], cmap='gray')\n",
        "plt.title(\"True label: %s Predicted: %s\" % (y_test[i], p_test[i]));   # Show true label vs. what it was mistaken as"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "ctJ5tAZRMcQs",
        "outputId": "11347b70-132e-4633-b905-89c5a49ff29c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}