MLP

import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sn
import numpy as np
import pandas as pd
import math
import datetime
import platform
import keras

mnist_dataset = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist_dataset.load_data()

print('x_train:', x_train.shape)
print('y_train:', y_train.shape)
print('x_test:', x_test.shape)
print('y_test:', y_test.shape)

pd.DataFrame(x_train[0])
plt.imshow(x_train[0], cmap=plt.cm.binary)
plt.show()

numbers_to_display = 25
num_cells = math.ceil(math.sqrt(numbers_to_display))
plt.figure(figsize=(10,10))
for i in range(numbers_to_display):
    plt.subplot(num_cells, num_cells, i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i], cmap=plt.cm.binary)
    plt.xlabel(y_train[i])
plt.show()
x_train_normalized = x_train / 255
x_test_normalized = x_test / 255

plt.imshow(x_train_normalized[0], cmap=plt.cm.binary)
plt.show()

model = tf.keras.models.Sequential()

# Input layers.
model.add(tf.keras.layers.Flatten(input_shape=x_train_normalized.shape[1:]))
model.add(tf.keras.layers.Dense(
    units=128,
    activation=tf.keras.activations.relu,
    kernel_regularizer=tf.keras.regularizers.l2(0.002)
))

# Hidden layers.
model.add(tf.keras.layers.Dense(
    units=128,
    activation=tf.keras.activations.relu,
    kernel_regularizer=tf.keras.regularizers.l2(0.002)
))

# Output layers.
model.add(tf.keras.layers.Dense(
    units=10,
    activation=tf.keras.activations.softmax
))

model.summary()

tf.keras.utils.plot_model(
    model,
    show_shapes=True,
    show_layer_names=True,
)

adam_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

model.compile(
    optimizer=adam_optimizer,
    loss=tf.keras.losses.sparse_categorical_crossentropy,
    metrics=['accuracy']
)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

training_history = model.fit(
    x_train_normalized,
    y_train,
    epochs=10,
    validation_data=(x_test_normalized, y_test),
    callbacks=[tensorboard_callback]
)
plt.xlabel('Epoch Number')
plt.ylabel('Accuracy')
plt.plot(training_history.history['accuracy'], label='training set')
plt.plot(training_history.history['val_accuracy'], label='test set')
plt.legend()
train_loss, train_accuracy = model.evaluate(x_train_normalized, y_train)
validation_loss, validation_accuracy = model.evaluate(x_test_normalized, y_test)