simple_autoencoder.py

#!/usr/bin/env python

from tensorflow import keras
from keras import layers

from keras import regularizers

from keras.datasets import mnist
import numpy as np

# This is the size of our encoded representations
encoding_dim = 64  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats

# This is our input image
input_img = keras.Input(shape=(1024,))
# "encoded" is the encoded representation of the input
#encoded = layers.Dense(encoding_dim, activation='relu')(input_img)

# Add a Dense layer with a L1 activity regularizer
encoded = layers.Dense(encoding_dim, activation='relu',
                activity_regularizer=regularizers.l1(10e-5))(input_img)

# "decoded" is the lossy reconstruction of the input
decoded = layers.Dense(1024, activation='sigmoid')(encoded)

# This model maps an input to its reconstruction
autoencoder = keras.Model(input_img, decoded)

# This model maps an input to its encoded representation
encoder = keras.Model(input_img, encoded)

# This is our encoded (32-dimensional) input
encoded_input = keras.Input(shape=(encoding_dim,))
# Retrieve the last layer of the autoencoder model
decoder_layer = autoencoder.layers[-1]
# Create the decoder model
decoder = keras.Model(encoded_input, decoder_layer(encoded_input))

autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
#autoencoder.compile(optimizer='rmsprop', loss='mean_squared_error')

#(x_train, _), (x_test, _) = mnist.load_data()

data = np.load( 'pieces_2021-01-02_1625.npy' )
validation_split = int(len(data)*0.8)
x_train = data[:validation_split]
x_test = data[validation_split:]

# We will normalize all values between 0 and 1 and we will flatten the 28x28 images into vectors of size 784.

x_train = x_train.astype('float32') / 255.
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)

# Now let's train our autoencoder for 50 epochs:

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

autoencoder.save_weights( 'autoencoder.h5' )

# After 50 epochs, the autoencoder seems to reach a stable train/validation loss value of about 0.09. We can try to visualize the reconstructed inputs and the encoded representations. We will use Matplotlib.

# 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)

# Use Matplotlib (don't ask)
import matplotlib.pyplot as plt

n = 10  # How many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
    # Display original
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(x_test[i].reshape(32, 32))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # Display reconstruction
    ax = plt.subplot(2, n, i + 1 + n)
    plt.imshow(decoded_imgs[i].reshape(32, 32))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()