{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abe24003",
   "metadata": {},
   "source": [
    "Use a Convolutional Neural Net to classify the MNIST data of digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c95fefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "280c5099",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c3fc1b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the pixel values to be between 0 and 1\n",
    "x_train = x_train / 255\n",
    "x_test = x_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a9686ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the labels into one-hot encoded arrays\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80e582c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model\n",
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aff6c38a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "049f9d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model and record the history, the data is split in batches\n",
    "history = model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7a8baea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the weights of the Dense layer\n",
    "# plot the weights as a heatmap or image, where the weights are represented\n",
    "# as pixel values.\n",
    "last_layer_weights = model.layers[-1].get_weights()[0]\n",
    "# Plot the weights as a heatmap\n",
    "plt.imshow(last_layer_weights, cmap='coolwarm')\n",
    "plt.colorbar()\n",
    "plt.title('weights in the output layer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cc4d04b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot loss and accuracy\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# Plot the loss during training\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['loss'], label='training loss')\n",
    "plt.plot(history.history['val_loss'], label='validation loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcdef199",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot a confusion matrix of the test set predictions\n",
    "test_preds = np.argmax(model.predict(x_test), axis=1)\n",
    "conf_mat = tf.math.confusion_matrix(y_test.argmax(axis=1), test_preds)\n",
    "\n",
    "plt.imshow(conf_mat, cmap=\"Blues\")\n",
    "plt.xlabel(\"Predicted labels\")\n",
    "plt.ylabel(\"True labels\")\n",
    "plt.xticks(np.arange(10))\n",
    "plt.yticks(np.arange(10))\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f28f1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c0f27a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred = model.predict(x_test)\n",
    "y_pred = np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f781bef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot some examples from the test set and their predictions\n",
    "fig, axes = plt.subplots(4, 4, figsize=(10, 10))\n",
    "for i, ax in enumerate(axes.ravel()):\n",
    "    ax.imshow(x_test[i].reshape(28, 28), cmap='gray')\n",
    "    ax.set_title(\"True: %d\\nPred: %d\" % (np.argmax(y_test[i]), y_pred[i]))\n",
    "    ax.axis('off')\n",
    "plt.suptitle(\"Examples of test set images and their predictions\")\n",
    "plt.show()\n"
   ]
  }
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