Neural_Network/fashion_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "fashion_mnist.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "L7clasnAWOjP"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sL0bDawmWVT8",
        "outputId": "a3331b52-b86b-43d8-9241-89cae8de2566"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 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",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 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",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "HZhhDtv5WY3G"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_images.shape)\n",
        "print(test_images.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPJAFERrWfCs",
        "outputId": "cb6358de-eadf-4deb-8226-06ad16a0af48"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_labels))\n",
        "print(len(test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaqoRinnWsoG",
        "outputId": "8ca4f914-b99d-4104-9478-1a2d89b5ebdd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000\n",
            "10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pre-process and visualization"
      ],
      "metadata": {
        "id": "L0gDW6ViXIdz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "hDhntGQZWy5Z",
        "outputId": "9fd5ac2f-5e08-4102-f2b8-d343ddf4513e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale pixel values to a range of 0 to 1\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "metadata": {
        "id": "pSU54IENW8Wc"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "hP7I1ZC1XDc_",
        "outputId": "604b97e7-67db-450c-8a8a-4adb7ba0975d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Build the model\n",
        "\n",
        "\n",
        "1.   Setup the layers\n",
        "2.   Compile the model\n",
        "\n"
      ],
      "metadata": {
        "id": "JKvCFY0gXZHF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "metadata": {
        "id": "3yG8Oc14XFJE"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "5yeqcn73XkEV"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train the model\n",
        "\n",
        "\n",
        "1.   Feed the model\n",
        "2.   Evaluate\n",
        "3.   Verify Prediction\n",
        "\n"
      ],
      "metadata": {
        "id": "kdMBmGnQXosx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfrctsFEXreu",
        "outputId": "6b19ecc1-5b70-4aec-adcc-839a8477f41b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 7s 2ms/step - loss: 0.4924 - accuracy: 0.8239\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3645 - accuracy: 0.8660\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3292 - accuracy: 0.8789\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3093 - accuracy: 0.8852\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2901 - accuracy: 0.8923\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2764 - accuracy: 0.8973\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2646 - accuracy: 0.9002\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2544 - accuracy: 0.9040\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2437 - accuracy: 0.9085\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2351 - accuracy: 0.9113\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7690165510>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test accuracy"
      ],
      "metadata": {
        "id": "I-2-zD0ea9BE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "print('Test accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVIoX9KZYZk5",
        "outputId": "c5cfcb76-c37a-44a2-c4a1-84f626072d40"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - loss: 0.3573 - accuracy: 0.8821 - 678ms/epoch - 2ms/step\n",
            "Test accuracy: 0.882099986076355\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Confusion matrix"
      ],
      "metadata": {
        "id": "8r6YXtKWY_SX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_images)\n",
        "matrix = confusion_matrix(test_labels, predictions.argmax(axis=1))\n",
        "print(\"Confusion matrix:\")\n",
        "print(matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNR_a9uqY8wG",
        "outputId": "ee47e25a-3f6a-447c-f04c-0fe210efc2f6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:\n",
            "[[795   2  10  25   3   2 160   0   3   0]\n",
            " [  1 972   1  17   4   0   4   0   1   0]\n",
            " [ 23   1 851  12  57   0  55   0   1   0]\n",
            " [  9  11   9 893  28   1  45   0   4   0]\n",
            " [  3   1 138  39 749   0  68   0   2   0]\n",
            " [  0   0   0   0   0 944   0  21   1  34]\n",
            " [ 86   0  97  25  54   0 727   0  11   0]\n",
            " [  0   0   0   0   0  13   0 947   0  40]\n",
            " [  5   0   5   5   1   6   4   3 971   0]\n",
            " [  0   0   0   0   0   4   1  23   0 972]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cmp = ConfusionMatrixDisplay(confusion_matrix=matrix, display_labels=class_names)\n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "cmp.plot(ax=ax)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "n4PKYKhzRGuk",
        "outputId": "2bf04f9e-d11b-4b3d-dc5a-50a50bfae3ee"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "DERAUhqDRS-K"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}