Simple_Autoencoder - Copy.py

import numpy as np
# Use Matplotlib (don't ask)
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

# This is the size of our encoded representations
encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats


input_img = keras.Input(shape=(784,))
encoded = layers.Dense(encoding_dim, activation='relu')(input_img)
decoded = layers.Dense(784, activation='sigmoid')(encoded)

autoencoder = keras.Model(input_img, decoded)

# create the encoder model
encoder = keras.Model(input_img, encoded)


# Create the decoder model
encoded_input = keras.Input(shape=(encoding_dim,))
print(encoded_input)
decoder_layer = autoencoder.layers[-1]
decoder = keras.Model(encoded_input, decoder_layer(encoded_input))

# execute the code
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')


(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.  # This will make the values of the gray-scale vary from 0 to 1
x_test = x_test.astype('float32') / 255.
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:])))
print(x_train.shape)
print(x_test.shape)

autoencoder.fit(x_train, x_train,
                epochs=10,
                batch_size=256,
                shuffle=True,
                validation_data=(x_test, x_test))

# Encode and decode some digits
# Note that we take them from the *test* set
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)


n = 10  # How many digits we will display
plt.figure(figsize=(20, 4))  # figsize defines the width and height of the total number of figures in inches
for i in range(n):
    # Display original
    ax = plt.subplot(2, n, i + 1 + n)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

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