Mnist_task.ipynb

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  "metadata": {
    "colab": {
      "name": "Mnist_task.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN1ntyLJY9FMe5SfE6J4CwE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NahianAlindo/ml_practice/blob/master/Mnist_task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnbyXo0lT1Bb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importing dependencies\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.datasets import mnist"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-3Enr4nUYzJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e778dfd8-00e0-4d4e-e35b-6d835d1747d5"
      },
      "source": [
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHByZQDdUuVr",
        "colab_type": "text"
      },
      "source": [
        "**Loaded dataset in the code cell above**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmCwuCidUn8B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "36ed1c37-0ef0-42cf-c539-faff574202d6"
      },
      "source": [
        "# checking the shape of the data\n",
        "train_images.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4P26KuznU-sw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d9d1ceb9-d1c8-4093-8b6d-924a5cffe16a"
      },
      "source": [
        "len(train_labels)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzlZVTfNVE3S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "16ba7b22-e8c5-4dd8-aaa6-8d5500b6be38"
      },
      "source": [
        "train_labels"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjvGpkpAVJdb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a33973d7-5a78-47fd-ae06-3a43a3f1936f"
      },
      "source": [
        "# checking shape of the test data\n",
        "test_images.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5d78ofSVReQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2390834a-e5c3-4df9-d943-ccc406ce9117"
      },
      "source": [
        "# length of the test label set\n",
        "len(test_labels)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNVoT1tmVqhp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importing the models\n",
        "from tensorflow.keras import models"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7U5cTrHIWPxc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# the model\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'))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wopY_bc6X_EI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "bc499364-b129-45bf-9b7d-bc1b9cdf0775"
      },
      "source": [
        "# display architecture of the convnet model\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "=================================================================\n",
            "Total params: 55,744\n",
            "Trainable params: 55,744\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rl0NrdDfYRol",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# flatten the output of the last conv2d layer into a densely connected layer nn\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(10, activation='softmax')) # 10 for the softmax layer to classify 0-9"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdX8Yi2jcqkp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "4dd54052-9c5e-4a1f-9996-fd0b76e49371"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                36928     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 93,322\n",
            "Trainable params: 93,322\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gnf-JofScslL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.utils import to_categorical"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El0YGomHdWW4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e80d9f67-a0d1-4aff-b866-3ebf7cc29026"
      },
      "source": [
        "train_images = train_images.reshape((60000, 28, 28, 1))\n",
        "train_images = train_images.astype('float32') / 255\n",
        "\n",
        "test_images = test_images.reshape((10000, 28, 28, 1))\n",
        "test_images = test_images.astype('float32') / 255\n",
        "\n",
        "train_labels = to_categorical(train_labels)\n",
        "test_labels = to_categorical(test_labels)\n",
        "\n",
        "# compiling and running the model\n",
        "\n",
        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=64)\n",
        "\n",
        "# evaluation of the model on test set\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "938/938 [==============================] - 50s 53ms/step - loss: 0.1751 - accuracy: 0.9450\n",
            "Epoch 2/5\n",
            "938/938 [==============================] - 50s 53ms/step - loss: 0.0463 - accuracy: 0.9862\n",
            "Epoch 3/5\n",
            "938/938 [==============================] - 50s 53ms/step - loss: 0.0317 - accuracy: 0.9904\n",
            "Epoch 4/5\n",
            "938/938 [==============================] - 49s 52ms/step - loss: 0.0250 - accuracy: 0.9919\n",
            "Epoch 5/5\n",
            "938/938 [==============================] - 48s 52ms/step - loss: 0.0188 - accuracy: 0.9939\n",
            "313/313 [==============================] - 3s 9ms/step - loss: 0.0253 - accuracy: 0.9922\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxY_VyZmey0R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "470da28d-0f66-4155-b2e7-4a6a03528c5d"
      },
      "source": [
        "test_acc"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9922000169754028"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mqsr3PmfvtO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    }
  ]
}