Simple_MNIST_CNN.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3vonj70VQya",
        "outputId": "1f0491ee-25cd-44f4-ddfe-2bdd84ab8e3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "# Model / data parameters\n",
        "num_classes = 10\n",
        "input_shape = (28, 28, 1)\n",
        "\n",
        "# the data, split between train and test sts\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "\n",
        "# Image greyscaling should be between 0 and 1 not 0 and 255\n",
        "x_train = x_train.astype(\"float32\") / 255\n",
        "x_test = x_test.astype(\"float32\") / 255\n",
        "\n",
        "# Make sure images have shape (28, 28, 1)\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "\n",
        "print(\"x_train shape:\", x_train.shape)\n",
        "print(x_train.shape[0], \"train samples\")\n",
        "print(x_test.shape[0], \"test samples\")\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mA72BaVVU_DT",
        "outputId": "07de2474-815a-40e9-bed9-0c6b49a8ecea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_7 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 13, 13, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 1600)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                16010     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,826\n",
            "Trainable params: 34,826\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Define the model\n",
        "model = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=input_shape),\n",
        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dropout(0.5),\n",
        "        layers.Dense(num_classes, activation=\"softmax\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNDl39YWVJwA",
        "outputId": "28ffb9c0-dc10-46a1-e473-08e4958d01f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "422/422 [==============================] - 30s 71ms/step - loss: 0.3730 - accuracy: 0.8846 - val_loss: 0.0816 - val_accuracy: 0.9785\n",
            "Epoch 2/15\n",
            "422/422 [==============================] - 29s 70ms/step - loss: 0.1138 - accuracy: 0.9645 - val_loss: 0.0549 - val_accuracy: 0.9848\n",
            "Epoch 3/15\n",
            "422/422 [==============================] - 29s 70ms/step - loss: 0.0841 - accuracy: 0.9744 - val_loss: 0.0514 - val_accuracy: 0.9872\n",
            "Epoch 4/15\n",
            "422/422 [==============================] - 29s 68ms/step - loss: 0.0718 - accuracy: 0.9776 - val_loss: 0.0471 - val_accuracy: 0.9882\n",
            "Epoch 5/15\n",
            "422/422 [==============================] - 30s 70ms/step - loss: 0.0619 - accuracy: 0.9813 - val_loss: 0.0398 - val_accuracy: 0.9900\n",
            "Epoch 6/15\n",
            "422/422 [==============================] - 29s 68ms/step - loss: 0.0568 - accuracy: 0.9827 - val_loss: 0.0390 - val_accuracy: 0.9882\n",
            "Epoch 7/15\n",
            "422/422 [==============================] - 29s 68ms/step - loss: 0.0509 - accuracy: 0.9842 - val_loss: 0.0359 - val_accuracy: 0.9903\n",
            "Epoch 8/15\n",
            "422/422 [==============================] - 29s 70ms/step - loss: 0.0470 - accuracy: 0.9855 - val_loss: 0.0356 - val_accuracy: 0.9903\n",
            "Epoch 9/15\n",
            "422/422 [==============================] - 29s 68ms/step - loss: 0.0447 - accuracy: 0.9857 - val_loss: 0.0324 - val_accuracy: 0.9917\n",
            "Epoch 10/15\n",
            "422/422 [==============================] - 29s 69ms/step - loss: 0.0417 - accuracy: 0.9871 - val_loss: 0.0305 - val_accuracy: 0.9915\n",
            "Epoch 11/15\n",
            "422/422 [==============================] - 29s 68ms/step - loss: 0.0386 - accuracy: 0.9876 - val_loss: 0.0334 - val_accuracy: 0.9912\n",
            "Epoch 12/15\n",
            "422/422 [==============================] - 29s 69ms/step - loss: 0.0376 - accuracy: 0.9879 - val_loss: 0.0297 - val_accuracy: 0.9905\n",
            "Epoch 13/15\n",
            "422/422 [==============================] - 29s 69ms/step - loss: 0.0354 - accuracy: 0.9888 - val_loss: 0.0309 - val_accuracy: 0.9918\n",
            "Epoch 14/15\n",
            "422/422 [==============================] - 29s 68ms/step - loss: 0.0344 - accuracy: 0.9890 - val_loss: 0.0309 - val_accuracy: 0.9913\n",
            "Epoch 15/15\n",
            "422/422 [==============================] - 29s 69ms/step - loss: 0.0327 - accuracy: 0.9894 - val_loss: 0.0294 - val_accuracy: 0.9917\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f4d313bc250>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "batch_size = 128\n",
        "epochs = 15\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjrBkYQRW7wx",
        "outputId": "d3577d3f-3a53-45e7-bb0c-8bec1f2677e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test loss: 0.02503575012087822\n",
            "Test accuracy: 0.9912999868392944\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the trained model\n",
        "score = model.evaluate(x_test, y_test, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYmatMhrXIrV",
        "outputId": "1321d716-c1bc-4b90-8f86-ba9de64a1a61"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting shap\n",
            "  Downloading shap-0.41.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (572 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m572.4/572.4 KB\u001b[0m \u001b[31m33.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (from shap) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from shap) (1.4.4)\n",
            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.9/dist-packages (from shap) (4.65.0)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.9/dist-packages (from shap) (0.56.4)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.9/dist-packages (from shap) (2.2.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from shap) (1.10.1)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.9/dist-packages (from shap) (23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from shap) (1.22.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from numba->shap) (67.6.1)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn->shap) (3.1.0)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.41.0 slicer-0.0.7\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "keras is no longer supported, please use tf.keras instead.\n",
            "Your TensorFlow version is newer than 2.4.0 and so graph support has been removed in eager mode and some static graphs may not be supported. See PR #1483 for discussion.\n",
            "`tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n"
          ]
        }
      ],
      "source": [
        "!pip install shap\n",
        "import shap\n",
        "\n",
        "# select a set of background examples to take an expectation over\n",
        "background = x_train[np.random.choice(x_train.shape[0], 100, replace=False)]\n",
        "\n",
        "# explain predictions of the model on four images\n",
        "e = shap.DeepExplainer(model, background)\n",
        "shap_values = e.shap_values(x_test[1:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "u8Wim6bzXVlo",
        "outputId": "50532f4d-7d55-42c1-fdd6-e02d93d27221"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 2000x757.576 with 45 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot the feature attributions\n",
        "shap.image_plot(shap_values, -x_test[1:5])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}