A2_Q1_FashionMNISTHandwritten.ipynb

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    "colab": {
      "name": "A2-Q1-FashionMNISTHandwritten",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPq2NeYeDmQTPvCENzMDRlv",
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      "name": "python3",
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    "accelerator": "GPU"
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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/mehrotrasan16/Keras-Deep-Learning/blob/02-FashionMNISTHandCrafted-Classification/A2_Q1_FashionMNISTHandwritten.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1zl2Z7dS330",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "3908c844-e9a4-44b1-aa3c-c976a5db39cc"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import models,layers\n",
        "\n",
        "#train_data, test_data  = tf.keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "(train_images, train_labels),(test_images,test_labels) = tf.keras.datasets.fashion_mnist.load_data()\n"
      ],
      "execution_count": 1,
      "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": "L2yeUOlB4lsX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "64a13bc7-0ccc-4e2d-9e9e-4db303b82f15"
      },
      "source": [
        "#train_images, train_labels = train_data\n",
        "train_images.shape, train_labels.shape"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPjMExMZ4WOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b89ce739-832b-48d8-abbd-5f78eba27370"
      },
      "source": [
        "#test_images, test_labels = test_data\n",
        "test_images.shape, test_labels.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdYB8YKiTDl_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalize pixel values to be between 0 and 1\n",
        "train_images,test_images  = train_images / 255.0, test_images / 255.0"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UtlFqEsTm8B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cbd0e4b2-891f-4d54-cbf3-07a8036c156d"
      },
      "source": [
        "train_images.shape[1:], test_images.shape[1:]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), (28, 28))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-j1fRFKkTsGZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXaQL7e84Rnh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "101d168a-b97c-445b-ebdc-3ae1366b5639"
      },
      "source": [
        "\n",
        "classes =(np.unique(train_labels))\n",
        "classes"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
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      "metadata": {
        "id": "Rg2Y-qrB5Bm2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8bad0f1e-c896-497d-93b7-2b04b60611c9"
      },
      "source": [
        "classnames = np.array(['T-shirt','Trouser','Pullover','Dress','Coat','Sandal','Shirt', 'sneaker', 'bag','ankle-boot'])\n",
        "classnames"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['T-shirt', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal',\n",
              "       'Shirt', 'sneaker', 'bag', 'ankle-boot'], dtype='<U10')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
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      "metadata": {
        "id": "JuHZf1Qs5nYv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes = classes.reshape((-1,1))\n",
        "classnames = classnames.reshape((-1,1))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5e8oeCTHifC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "arrayclass = np.hstack((classes,classnames))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rj6SIng5HxJI",
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          "height": 348
        },
        "outputId": "787932e9-9462-45b8-e466-2008f7d99cda"
      },
      "source": [
        "ClassDistribution = pd.DataFrame(arrayclass,columns=['Class','Class-name'])\n",
        "ClassDistribution"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Trouser</td>\n",
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              "      <td>Pullover</td>\n",
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              "      <td>Dress</td>\n",
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              "      <td>ankle-boot</td>\n",
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              "  Class  Class-name\n",
              "0     0     T-shirt\n",
              "1     1     Trouser\n",
              "2     2    Pullover\n",
              "3     3       Dress\n",
              "4     4        Coat\n",
              "5     5      Sandal\n",
              "6     6       Shirt\n",
              "7     7     sneaker\n",
              "8     8         bag\n",
              "9     9  ankle-boot"
            ]
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      "source": [
        "freqs = [list(train_labels).count(x) for x in classes]"
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      "execution_count": 13,
      "outputs": []
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      "source": [
        "ClassDistribution['Train Freq'] = freqs"
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              "      <td>Shirt</td>\n",
              "      <td>6000</td>\n",
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              "      <td>7</td>\n",
              "      <td>sneaker</td>\n",
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              "      <td>9</td>\n",
              "      <td>ankle-boot</td>\n",
              "      <td>6000</td>\n",
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              "  Class  Class-name  Train Freq\n",
              "0     0     T-shirt        6000\n",
              "1     1     Trouser        6000\n",
              "2     2    Pullover        6000\n",
              "3     3       Dress        6000\n",
              "4     4        Coat        6000\n",
              "5     5      Sandal        6000\n",
              "6     6       Shirt        6000\n",
              "7     7     sneaker        6000\n",
              "8     8         bag        6000\n",
              "9     9  ankle-boot        6000"
            ]
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          "execution_count": 15
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      "source": [
        "test_freqs = [list(test_labels).count(x) for x in classes]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
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      "source": [
        "ClassDistribution['Test Freq'] = freqs"
      ],
      "execution_count": 17,
      "outputs": []
    },
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      "execution_count": 18,
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          "data": {
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              "      <th>Class-name</th>\n",
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              "      <th>0</th>\n",
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              "      <td>T-shirt</td>\n",
              "      <td>6000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Trouser</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Pullover</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Dress</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Coat</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>Sandal</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>Shirt</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>sneaker</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>bag</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>ankle-boot</td>\n",
              "      <td>6000</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "  Class  Class-name  Train Freq  Test Freq\n",
              "0     0     T-shirt        6000       6000\n",
              "1     1     Trouser        6000       6000\n",
              "2     2    Pullover        6000       6000\n",
              "3     3       Dress        6000       6000\n",
              "4     4        Coat        6000       6000\n",
              "5     5      Sandal        6000       6000\n",
              "6     6       Shirt        6000       6000\n",
              "7     7     sneaker        6000       6000\n",
              "8     8         bag        6000       6000\n",
              "9     9  ankle-boot        6000       6000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
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        "outputId": "c5ac1314-57ab-4d03-c823-db5298e8067c"
      },
      "source": [
        "train_images[0].shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQBg68byeGKQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#INSERT YOUR MODEL CODE HERE\n",
        "#YOU CAN ALSO CHANGE THE CODE BELOW TO COMPILE AND RUN THE MODEL\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(keras.layers.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1))),\n",
        "model.add(layers.Conv2D(32, kernel_size=(3,3), padding='same', activation='relu', input_shape=(28,28)))\n",
        "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Conv2D(64, kernel_size=(3,3), padding='same', activation='relu'))\n",
        "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(512, activation='relu'))\n",
        "#model.add(layers.BatchNormalization(center=True, scale=False))\n",
        "model.add(layers.Dropout(0.50))\n",
        "model.add(layers.Dense(10))\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1Qi75gfehGe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "59b35b66-f6fa-424a-8a4a-c03b768b2923"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, train_labels, epochs=10, \n",
        "                    validation_data=(test_images, test_labels))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (32, 28, 28).\n",
            "1855/1875 [============================>.] - ETA: 0s - loss: 0.4276 - accuracy: 0.8447WARNING:tensorflow:Model was constructed with shape (None, 784) for input Tensor(\"reshape_input:0\", shape=(None, 784), dtype=float32), but it was called on an input with incompatible shape (None, 28, 28).\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4262 - accuracy: 0.8452 - val_loss: 0.3023 - val_accuracy: 0.8893\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2818 - accuracy: 0.8961 - val_loss: 0.2772 - val_accuracy: 0.8984\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2384 - accuracy: 0.9126 - val_loss: 0.2552 - val_accuracy: 0.9066\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2086 - accuracy: 0.9235 - val_loss: 0.2341 - val_accuracy: 0.9143\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1838 - accuracy: 0.9322 - val_loss: 0.2407 - val_accuracy: 0.9164\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1641 - accuracy: 0.9391 - val_loss: 0.2585 - val_accuracy: 0.9087\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1474 - accuracy: 0.9440 - val_loss: 0.2361 - val_accuracy: 0.9209\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1295 - accuracy: 0.9513 - val_loss: 0.2521 - val_accuracy: 0.9199\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1163 - accuracy: 0.9571 - val_loss: 0.2469 - val_accuracy: 0.9208\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1058 - accuracy: 0.9602 - val_loss: 0.2526 - val_accuracy: 0.9174\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hY4vjzIUeo5D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "c723b492-3c27-4ad0-90ee-b7f3984cfeb8"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 1s - loss: 0.2526 - accuracy: 0.9174\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4i1JBYqkX5u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 953
        },
        "outputId": "41a6972f-8334-4034-9077-2f374daae3c4"
      },
      "source": [
        "keras.utils.plot_model(model)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRV3mzFnDHwE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}