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240 lines
6.8 KiB
240 lines
6.8 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": "13fa2f64",
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"metadata": {},
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"outputs": [],
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"source": [
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"# In this example, we used the TensorFlow library to load the MNIST data,\n",
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"# define an MLP model with three dense layers, compile the model, train it\n",
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"# for 10 epochs, evaluate it on the test set, and make predictions on\n",
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"# the test set. Finally, we plot some examples of the predictions made\n",
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"# by 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": "1c4405f8",
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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 numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.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": "1e7e58fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Normalize the pixel values to be between 0 and 1\n",
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"x_train = x_train / 255\n",
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"x_test = x_test / 255"
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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": "3d8a7370",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Flatten the 2D images into 1D arrays\n",
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"x_train = x_train.reshape(x_train.shape[0], -1)\n",
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"x_test = x_test.reshape(x_test.shape[0], -1)"
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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": "c2df0f54",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert the labels into one-hot encoded arrays\n",
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"y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)\n",
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"y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)"
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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": "e2b249ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define the model\n",
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"# The number of parameters depends on the shapes and sizes of the layers.\n",
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"# In the given model, the first layer Dense(512, activation='relu',\n",
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"# input_shape=(784,)) has 784 input nodes and 512 output nodes. Therefore,\n",
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"# the number of parameters in this layer would be (784 * 512) + 512 = 401920,\n",
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"# where the +512 term is for the bias terms.\n",
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"# The second layer also has 512 input nodes and 512 output nodes, which makes\n",
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"# 512512 = 262,144 parameters. The third and last layer has 512 input nodes\n",
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"# and 10 output nodes, which makes 512*10 = 5,120 parameters.\n",
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"model = tf.keras.models.Sequential()\n",
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"model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(784,)))\n",
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"model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
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"model.add(tf.keras.layers.Dense(10, activation='softmax'))"
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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": "bab8730a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# over come overfitting by regularization\n",
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"#model = tf.keras.models.Sequential([\n",
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"# tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001),input_shape=(784,)),\n",
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"# tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n",
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"# tf.keras.layers.Dense(10, activation='softmax')\n",
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"#])"
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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": "e3223c61",
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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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"model.compile(loss='categorical_crossentropy', optimizer='adam', 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": "51e7758f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Train the model and record the history\n",
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"history = model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))"
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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": "e3240069",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the weights of the Dense layer\n",
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"# plot the weights as a heatmap or image, where the weights are represented\n",
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"# as pixel values.\n",
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"# model.layers[2].get_weights()[0] returns only the weights of the third\n",
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"# layer. If you wanted to get the biases, you would use\n",
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"# model.layers[2].get_weights()[1].\n",
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"dense_weights = model.layers[2].get_weights()[0]\n",
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"\n",
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"# Plot the weights as a heatmap\n",
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"plt.imshow(dense_weights, cmap='coolwarm')\n",
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"plt.colorbar()\n",
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"plt.title('weights in the output layer')\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": "13118686",
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"metadata": {},
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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)\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": "40f35fe5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot loss and accuracy\n",
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"plt.figure(figsize=(12, 4))\n",
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"\n",
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"# Plot the loss and accuracy for training and validation data\n",
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"plt.subplot(1, 2, 1)\n",
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"plt.plot(history.history['loss'], label='training loss')\n",
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"plt.plot(history.history['val_loss'], label='validation loss')\n",
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"plt.xlabel('Epoch')\n",
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"plt.ylabel('Loss')\n",
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"plt.legend()\n",
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"\n",
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"plt.subplot(1, 2, 2)\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.xlabel('Epoch')\n",
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"plt.ylabel('Accuracy')\n",
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"plt.legend()\n",
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"\n",
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"plt.show()\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": "6176ce1e",
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"metadata": {},
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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)"
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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": "3635ded5",
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"metadata": {},
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"outputs": [],
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"source": [
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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=(14, 14))\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\" % (np.argmax(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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"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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