encoder.py

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import fashion_mnist
from keras.models import Model
from keras.layers import Input, Dense
from keras.utils import plot_model

# Load Fashion MNIST dataset
(x_train, _), (x_test, _) = fashion_mnist.load_data()

# Normalize the data
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# Flatten the images
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))

# Define the autoencoder model
input_img = Input(shape=(784,))
encoded = Dense(256, activation='relu')(input_img)
encoded = Dense(128, activation='relu')(encoded)
encoded = Dense(64, activation='relu')(encoded)
encoded = Dense(32, activation='relu')(encoded)

decoded = Dense(64, activation='relu')(encoded)
decoded = Dense(128, activation='relu')(decoded)
decoded = Dense(256, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)

autoencoder = Model(input_img, decoded)

# Compile the model
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# Visualize the architecture of the autoencoder
plot_model(autoencoder, to_file='autoencoder.png', show_shapes=True, show_layer_names=True)

# Train the model
autoencoder.fit(x_train, x_train,
                epochs=50,
                batch_size=256,
                shuffle=True,
                validation_data=(x_test, x_test))

# Encode and decode some images
encoded_imgs = autoencoder.predict(x_test)
decoded_imgs = autoencoder.predict(x_test)

# Display original and reconstructed images
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
    # Original images
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # Reconstructed images
    ax = plt.subplot(2, n, i + 1 + n)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()