Fashion_MNIST.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Seeding for reproducibility**"
      ],
      "metadata": {
        "id": "c8VSns6fO6Pg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set seeds for reproducibility\n",
        "import random\n",
        "random.seed(0)\n",
        "\n",
        "import numpy as np\n",
        "np.random.seed(0)\n",
        "\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(0)"
      ],
      "metadata": {
        "id": "JSu8kpnEHDPB"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the dependencies**"
      ],
      "metadata": {
        "id": "Eyqk5_8AO1Kr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "16dILovOOFy0"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Curation**"
      ],
      "metadata": {
        "id": "7gAnTOlEPR8a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and prepare the Fashion MNIST dataset\n",
        "fashion_mnist = datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "id": "GbRhu0PWOMoT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ed10b0d-dc50-4471-893d-9a3f27cf4c51"
      },
      "execution_count": 3,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Processing**"
      ],
      "metadata": {
        "id": "uQ9oyUVCPVmp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(train_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "en_1bUeuz5Hw",
        "outputId": "8e3dbe19-ed38-4aae-c0d3-392057ebf38b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-odR6mbz_5I",
        "outputId": "1dae5b2e-a73b-40d3-f0f8-0554f4c443b2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_images))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vg8Wvzrp2aDY",
        "outputId": "098906ec-c014-4abd-d49c-88a14a956404"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXLP0wWM2ahk",
        "outputId": "7090fcd6-3306-4b83-b3d3-2551714b027a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(test_images))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYj72I41Vskz",
        "outputId": "89d812fa-d3ef-44de-ff70-f35a4d120be3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_images[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idy8IY4p0Cxh",
        "outputId": "c6971606-2c3c-4933-af4c-3c819b62f96e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(train_images[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pP3N4Ys5UYhi",
        "outputId": "abe05156-e200-4dee-e6f4-c034691ea471"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_images[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNv8vDin0DQ4",
        "outputId": "f43a12e4-e15e-4dfb-ed99-4332ef49db33"
      },
      "execution_count": 9,
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    {
      "cell_type": "code",
      "source": [
        "# Display an image from the dataset\n",
        "plt.imshow(train_images[0], cmap='gray')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "k6KK_nGa0SGX",
        "outputId": "c2d6ef18-4cd5-4faa-c122-9a8dd08af493"
      },
      "execution_count": 11,
      "outputs": [
        {
          "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": [
        "print(train_labels[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qhn6E5My0cAk",
        "outputId": "38bafdc6-2160-4b51-da03-4bdce671de4c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']**"
      ],
      "metadata": {
        "id": "wE35B-o90mqA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize pixel values to be between 0 and 1\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0"
      ],
      "metadata": {
        "id": "cDtZ0wSYORT_"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_images[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pUwvdK0V0Te",
        "outputId": "cf87b10d-a398-46e8-9eb3-b75257c706b4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
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            "  0.00392157 0.         0.         0.05098039 0.28627451 0.\n",
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            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.30196078 0.50980392 0.28235294 0.05882353]\n",
            " [0.         0.         0.         0.         0.         0.\n",
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            "  0.55294118 0.34509804 0.6745098  0.25882353]\n",
            " [0.         0.         0.         0.         0.         0.\n",
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            "  0.00784314 0.         0.         0.         0.         0.\n",
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            "  0.85490196 1.         0.30196078 0.        ]\n",
            " [0.         0.01176471 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.24313725 0.56862745 0.8\n",
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            "  0.82745098 0.85490196 0.87843137 0.8745098  0.85882353 0.84313725\n",
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            "  0.87843137 0.91764706 0.69019608 0.7372549  0.98039216 0.97254902\n",
            "  0.91372549 0.93333333 0.84313725 0.        ]\n",
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            "  0.85098039 0.94509804 0.25490196 0.28627451 0.41568627 0.45882353\n",
            "  0.65882353 0.85882353 0.86666667 0.84313725 0.85098039 0.8745098\n",
            "  0.8745098  0.87843137 0.89803922 0.11372549]\n",
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            "  0.77647059 0.83529412 0.94117647 0.76470588 0.89019608 0.96078431\n",
            "  0.9372549  0.8745098  0.85490196 0.83137255 0.81960784 0.87058824\n",
            "  0.8627451  0.86666667 0.90196078 0.2627451 ]\n",
            " [0.18823529 0.79607843 0.71764706 0.76078431 0.83529412 0.77254902\n",
            "  0.7254902  0.74509804 0.76078431 0.75294118 0.79215686 0.83921569\n",
            "  0.85882353 0.86666667 0.8627451  0.9254902  0.88235294 0.84705882\n",
            "  0.78039216 0.80784314 0.72941176 0.70980392 0.69411765 0.6745098\n",
            "  0.70980392 0.80392157 0.80784314 0.45098039]\n",
            " [0.         0.47843137 0.85882353 0.75686275 0.70196078 0.67058824\n",
            "  0.71764706 0.76862745 0.8        0.82352941 0.83529412 0.81176471\n",
            "  0.82745098 0.82352941 0.78431373 0.76862745 0.76078431 0.74901961\n",
            "  0.76470588 0.74901961 0.77647059 0.75294118 0.69019608 0.61176471\n",
            "  0.65490196 0.69411765 0.82352941 0.36078431]\n",
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            "  0.68627451 0.6745098  0.68627451 0.70980392 0.7254902  0.7372549\n",
            "  0.74117647 0.7372549  0.75686275 0.77647059 0.8        0.81960784\n",
            "  0.82352941 0.82352941 0.82745098 0.7372549  0.7372549  0.76078431\n",
            "  0.75294118 0.84705882 0.66666667 0.        ]\n",
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            "  0.95686275 0.86666667 0.8627451  0.75686275 0.74901961 0.70196078\n",
            "  0.71372549 0.71372549 0.70980392 0.69019608 0.65098039 0.65882353\n",
            "  0.38823529 0.22745098 0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.15686275 0.23921569 0.17254902 0.28235294 0.16078431\n",
            "  0.1372549  0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape images to specify that it's a single channel (grayscale)\n",
        "train_images = train_images.reshape((train_images.shape[0], 28, 28, 1))\n",
        "test_images = test_images.reshape((test_images.shape[0], 28, 28, 1))"
      ],
      "metadata": {
        "id": "QYB6sSX3OT7X"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwlwMAo4WEE_",
        "outputId": "2d47d9f0-0591-426e-b08e-7c5ff9b4d467"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TD9E6sdkWKJx",
        "outputId": "994c8f53-7e14-4963-b454-d4f033bb4141"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Convolutional Neural Network**"
      ],
      "metadata": {
        "id": "9QB6t17IPY5S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the convolutional base\n",
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "\n",
        "# Add Dense layers on top\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(10))"
      ],
      "metadata": {
        "id": "wj7IwvP1OWj8"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile and train the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "gwzRf4upOYeY"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Training**"
      ],
      "metadata": {
        "id": "dgc9IT9ZPggE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_images, train_labels, epochs=5,\n",
        "                    validation_data=(test_images, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JHWNhFxOaQC",
        "outputId": "cad1add8-4cbc-4e56-f7f7-18a200b95dea"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 14s 5ms/step - loss: 0.4902 - accuracy: 0.8215 - val_loss: 0.3585 - val_accuracy: 0.8724\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3193 - accuracy: 0.8827 - val_loss: 0.3102 - val_accuracy: 0.8903\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.2704 - accuracy: 0.8999 - val_loss: 0.2964 - val_accuracy: 0.8920\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2402 - accuracy: 0.9105 - val_loss: 0.2806 - val_accuracy: 0.8961\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2175 - accuracy: 0.9189 - val_loss: 0.2760 - val_accuracy: 0.9009\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation**"
      ],
      "metadata": {
        "id": "YION8Aa5PjYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4io9hAvXOg_O",
        "outputId": "0491728f-91ca-42bf-a3e8-91fa90ac5bb9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - loss: 0.2760 - accuracy: 0.9009 - 1s/epoch - 4ms/step\n",
            "\n",
            "Test accuracy: 0.9009000062942505\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation accuracy values\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "h06_sX59OjJj",
        "outputId": "c565be96-4578-411b-d7d3-bef72c554372"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "model.save('trained_fashion_mnist_model.h5')"
      ],
      "metadata": {
        "id": "iYM9EsEcOk2n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8cd1b05-158c-42a0-e7bb-ce63e73c2c7a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "igbGY12COm2Z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}