98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
'''This script demonstrates how to build a variational autoencoder with Keras.
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Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
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'''
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import numpy as np
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import matplotlib.pyplot as plt
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from keras.layers import Input, Dense, Lambda
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from keras.models import Model
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from keras import backend as K
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from keras import objectives
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from keras.datasets import mnist
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batch_size = 100
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original_dim = 784
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latent_dim = 2
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intermediate_dim = 256
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nb_epoch = 50
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x = Input(batch_shape=(batch_size, original_dim))
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h = Dense(intermediate_dim, activation='relu')(x)
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z_mean = Dense(latent_dim)(h)
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z_log_var = Dense(latent_dim)(h)
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def sampling(args):
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z_mean, z_log_var = args
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epsilon = K.random_normal(shape=(batch_size, latent_dim), mean=0.)
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return z_mean + K.exp(z_log_var / 2) * epsilon
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# note that "output_shape" isn't necessary with the TensorFlow backend
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z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
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# we instantiate these layers separately so as to reuse them later
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decoder_h = Dense(intermediate_dim, activation='relu')
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decoder_mean = Dense(original_dim, activation='sigmoid')
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h_decoded = decoder_h(z)
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x_decoded_mean = decoder_mean(h_decoded)
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def vae_loss(x, x_decoded_mean):
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xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
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kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
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return xent_loss + kl_loss
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vae = Model(x, x_decoded_mean)
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vae.compile(optimizer='rmsprop', loss=vae_loss)
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# train the VAE on MNIST digits
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train = x_train.astype('float32') / 255.
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x_test = x_test.astype('float32') / 255.
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x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
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x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
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vae.fit(x_train, x_train,
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shuffle=True,
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nb_epoch=nb_epoch,
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batch_size=batch_size,
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validation_data=(x_test, x_test))
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# build a model to project inputs on the latent space
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encoder = Model(x, z_mean)
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# display a 2D plot of the digit classes in the latent space
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x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
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plt.figure(figsize=(6, 6))
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plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)
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plt.colorbar()
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plt.show()
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# build a digit generator that can sample from the learned distribution
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decoder_input = Input(shape=(latent_dim,))
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_h_decoded = decoder_h(decoder_input)
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_x_decoded_mean = decoder_mean(_h_decoded)
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generator = Model(decoder_input, _x_decoded_mean)
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# display a 2D manifold of the digits
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n = 15 # figure with 15x15 digits
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digit_size = 28
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figure = np.zeros((digit_size * n, digit_size * n))
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# we will sample n points within [-15, 15] standard deviations
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grid_x = np.linspace(-15, 15, n)
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grid_y = np.linspace(-15, 15, n)
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for i, yi in enumerate(grid_x):
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for j, xi in enumerate(grid_y):
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z_sample = np.array([[xi, yi]])
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x_decoded = generator.predict(z_sample)
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digit = x_decoded[0].reshape(digit_size, digit_size)
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figure[i * digit_size: (i + 1) * digit_size,
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j * digit_size: (j + 1) * digit_size] = digit
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plt.figure(figsize=(10, 10))
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plt.imshow(figure)
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plt.show()
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