Image classification using CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Image classification using a CNN model.**\n",
        "\n",
        "In this application we will use the fashion MNIST dataset and a 2D convolution layer as the main feature extractor."
      ],
      "metadata": {
        "id": "0Zl5Qt8uMXoZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4IQPaigZLUe8"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading the data.**\n",
        "\n",
        "This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. \n",
        "\n",
        "The classes are:\n",
        "\n",
        "0 : T-shirt/top\n",
        "\n",
        "1 : Trouser\n",
        "\n",
        "2\t: Pullover\n",
        "\n",
        "3\t: Dress\n",
        "\n",
        "4\t: Coat\n",
        "\n",
        "5\t: Sandal\n",
        "\n",
        "6\t: Shirt\n",
        "\n",
        "7\t: Sneaker\n",
        "\n",
        "8\t: Bag\n",
        "\n",
        "9\t: Ankle boot\n"
      ],
      "metadata": {
        "id": "z1jXOtcdL2XF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3B4vKAnsLZ4_",
        "outputId": "f6dce346-1a45-4841-8410-0f315f2cc15e"
      },
      "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",
            "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 [==============================] - 2s 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": "code",
      "source": [
        "# Let's visualize some of the images.\n",
        "for i in range(4):\n",
        "  plt.imshow(x_train[i])\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NUTZZ6nPLdIl",
        "outputId": "8aab2075-ba83-4213-9d0d-9f9c12a5570e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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EPN3cfkfS+9OMt7rtCnUNRRthv0zSKwt+PqDRmu89JP3U9lO2t7RdzCLWR8Sh5varkta3WcwiqtN4D9Np04yPzLbrZvrzXvEB3UddGxGflnSjpK80L1dHUsy/Bxul3umSpvEelkWmGf9Am9uu2+nPe9VG2A9K2rjg58ubZSMhIg423w9LekSjNxX1a+/PoNt8P9xyPR8YpWm8F5tmXCOw7dqc/ryNsD8paZPtK2wvk/RFSTtaqOMjbK9qPjiR7VWSPq/Rm4p6h6Tbmtu3SXq0xVo+ZFSm8e40zbha3natT38eEUP/knST5j+R/29Jf9FGDR3q+nVJv2i+nmu7NkkPa/5l3bTmP9u4XdKFknZJekHSP0laO0K1fVfSs5Ke0XywNrRU27Waf4n+jKQ9zddNbW+7Ql1D2W4cLgskwQd0QBKEHUiCsANJEHYgCcIOJEHYgSQIO5DE/wOLvYnrqSAdTwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the examples for each class.\n",
        "pd.DataFrame(y_train, columns=['labels'])['labels'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvwfLLNWLfq-",
        "outputId": "6c6814e5-12c1-4626-e858-ef5ff29adfb4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9    6000\n",
              "0    6000\n",
              "3    6000\n",
              "2    6000\n",
              "7    6000\n",
              "5    6000\n",
              "1    6000\n",
              "6    6000\n",
              "4    6000\n",
              "8    6000\n",
              "Name: labels, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the table above we can see that each class has 6000 examples."
      ],
      "metadata": {
        "id": "IAmZv8tDL9aH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fitting the model.**"
      ],
      "metadata": {
        "id": "lOvtZCXBL-4b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining the model\n",
        "\n",
        "# Defining the input.\n",
        "inp = tf.keras.layers.Input(shape=(28,28,1))\n",
        "# Rescaling the images.\n",
        "resc = tf.keras.layers.Rescaling(1./255)(inp)\n",
        "# Extracting the features.\n",
        "conv = tf.keras.layers.Conv2D(filters=32, kernel_size=(4,4), activation='relu', input_shape=(28,28,1))(resc)\n",
        "pool = tf.keras.layers.MaxPool2D(pool_size=(2,2))(conv)\n",
        "flat = tf.keras.layers.Flatten()(pool)\n",
        "# Interpreting the extracted features.\n",
        "dense_1 = tf.keras.layers.Dense(units=100, activation='relu')(flat)\n",
        "dense_2 = tf.keras.layers.Dense(units=50, activation='relu')(dense_1)\n",
        "# Defining the output.\n",
        "output = tf.keras.layers.Dense(units=10, activation='softmax')(dense_2)\n",
        "\n",
        "model = tf.keras.Model(inputs=inp, outputs=output)\n",
        "\n",
        "# Compiling the model.\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics='accuracy')\n",
        "\n"
      ],
      "metadata": {
        "id": "z85NJMueLjVg"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train,\n",
        "          epochs=10, \n",
        "          validation_data=(x_test, y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKvtcozNLpL2",
        "outputId": "e7021809-9a1c-4170-8f3b-d6f3f06a6dec"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 40s 21ms/step - loss: 0.4222 - accuracy: 0.8478 - val_loss: 0.3559 - val_accuracy: 0.8673\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 40s 21ms/step - loss: 0.2816 - accuracy: 0.8971 - val_loss: 0.2929 - val_accuracy: 0.8941\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 40s 21ms/step - loss: 0.2357 - accuracy: 0.9118 - val_loss: 0.2699 - val_accuracy: 0.9023\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 38s 20ms/step - loss: 0.2060 - accuracy: 0.9232 - val_loss: 0.2621 - val_accuracy: 0.9069\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 41s 22ms/step - loss: 0.1796 - accuracy: 0.9322 - val_loss: 0.2640 - val_accuracy: 0.9077\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 41s 22ms/step - loss: 0.1579 - accuracy: 0.9408 - val_loss: 0.2684 - val_accuracy: 0.9110\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 41s 22ms/step - loss: 0.1360 - accuracy: 0.9485 - val_loss: 0.2778 - val_accuracy: 0.9109\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 39s 21ms/step - loss: 0.1172 - accuracy: 0.9563 - val_loss: 0.2969 - val_accuracy: 0.9132\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 39s 21ms/step - loss: 0.1027 - accuracy: 0.9618 - val_loss: 0.3161 - val_accuracy: 0.9081\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 36s 19ms/step - loss: 0.0913 - accuracy: 0.9659 - val_loss: 0.3572 - val_accuracy: 0.9086\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe241a24dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see from the different metrics that our model did a great job distinguishing the different categories in our dataset.  \n"
      ],
      "metadata": {
        "id": "tBEl1FryMLv-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CUmvLifOMM72"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}