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236 lines
6.7 KiB
236 lines
6.7 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6c180d4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Exercise 3\n",
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"# fashion mnist data\n",
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"# MLP model with two hidden layers, each with a ReLU activation function.\n",
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"# Input data is flattened to a 1D array and passed to the model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b0e31b9c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1ae1412e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the MNIST Fashion dataset\n",
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"(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f8814914",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Normalize pixel values to between 0 and 1\n",
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"x_train = x_train.astype(\"float32\") / 255.0\n",
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"x_test = x_test.astype(\"float32\") / 255.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2810da39",
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"metadata": {},
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"outputs": [],
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"source": [
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"# MNIST dataset images have a shape of (28, 28). The images are flattened\n",
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"# into a 1D array of length 784 \n",
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"x_train = x_train.reshape(-1, 784)\n",
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"x_test = x_test.reshape(-1, 784)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "96f7ff8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The model is defined here with three dense (fully connected) layers\n",
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"# The first layer is a Dense layer with 128 units and a ReLU activation\n",
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"# function with an input shape of (784,). This layer serves as the input\n",
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"# layer of the model.\n",
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"# The second layer is also a Dense layer with 64 units and a ReLU activation\n",
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"# function. This layer takes the output of the previous layer as input, and\n",
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"# applies a non-linear transformation to it to produce a new set of features\n",
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"# that the next layer can use.\n",
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"# The third is another Dense layer, one for each class in the output. The\n",
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"# output is raw scores or logits for each class since there is no activation\n",
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"# function . This layer is responsible for producing the final output of the\n",
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"# model, which can then be used to make predictions.\n",
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"# With Dropout(0.2) 20 % of the input is randomly droped, this should reduce overfitting\n",
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"model = keras.Sequential([\n",
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" keras.layers.Dense(128, activation='relu', input_shape=(784,)),\n",
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" # keras.layers.Dropout(0.2),\n",
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" keras.layers.Dense(64, activation='relu'),\n",
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" keras.layers.Dense(10)\n",
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"])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3fe609c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compile the model\n",
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"# adam = specifies the optimizer to use during training\n",
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"# loss function to use during training, SparseCategoricalCrossentropy loss\n",
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"# is commonly used for multi-class classification problems.\n",
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"# from_logits=True indicates that the model's output is a raw score\n",
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"# for each class and not a probability distribution.\n",
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"model.compile(optimizer='adam',\n",
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" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
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" metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cf6c978d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Train the model\n",
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"history = model.fit(x_train, y_train, epochs=10, validation_split=0.2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "97fc2313",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Evaluate the model on the test set\n",
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"test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)\n",
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"print(\"Test accuracy:\", test_acc)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ef5f19d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot the training and validation accuracy and loss over time\n",
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"plt.figure(figsize=(10, 4))\n",
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"plt.subplot(1, 2, 1)\n",
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"plt.plot(history.history[\"accuracy\"])\n",
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"plt.plot(history.history[\"val_accuracy\"])\n",
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"plt.title(\"Model accuracy\")\n",
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"plt.ylabel(\"Accuracy\")\n",
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"plt.xlabel(\"Epoch\")\n",
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"plt.legend([\"Train\", \"Validation\"], loc=\"lower right\")\n",
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"\n",
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"plt.subplot(1, 2, 2)\n",
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"plt.plot(history.history[\"loss\"])\n",
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"plt.plot(history.history[\"val_loss\"])\n",
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"plt.title(\"Model loss\")\n",
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"plt.ylabel(\"Loss\")\n",
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"plt.xlabel(\"Epoch\")\n",
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"plt.legend([\"Train\", \"Validation\"], loc=\"upper right\")\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c0ebddc4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot a confusion matrix of the test set predictions\n",
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"test_preds = np.argmax(model.predict(x_test), axis=1)\n",
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"conf_mat = tf.math.confusion_matrix(y_test, test_preds)\n",
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"plt.imshow(conf_mat, cmap=\"Blues\")\n",
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"plt.xlabel(\"Predicted labels\")\n",
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"plt.ylabel(\"True labels\")\n",
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"plt.xticks(np.arange(10))\n",
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"plt.yticks(np.arange(10))\n",
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"plt.colorbar()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9175d533",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Make predictions on the test set\n",
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"y_pred = model.predict(x_test)\n",
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"y_pred = np.argmax(y_pred, axis=1)\n",
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"\n",
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"# Plot some examples from the test set and their predictions\n",
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"fig, axes = plt.subplots(4, 4, figsize=(18, 18))\n",
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"for i, ax in enumerate(axes.ravel()):\n",
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" ax.matshow(x_test[i].reshape(28, 28), cmap='gray')\n",
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" ax.set_title(\"True: %d\\nPredict: %d\" % (y_test[i], y_pred[i]))\n",
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" ax.axis(\"off\")\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a6e85be",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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