variational_autoencoder_old.py (Source)

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 '''This script demonstrates how to build a variational autoencoder with Keras. Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114 ''' import numpy as np import matplotlib.pyplot as plt from scipy.stats import norm from keras.layers import Input, Dense, Lambda, Layer from keras.models import Model from keras import backend as K from keras import metrics from keras.datasets import mnist batch_size = 100 original_dim = 784 latent_dim = 2 intermediate_dim = 256 epochs = 50 epsilon_std = 1.0 x = Input(shape=(original_dim,)) h = Dense(intermediate_dim, activation='relu')(x) z_mean = Dense(latent_dim)(h) z_log_var = Dense(latent_dim)(h) def sampling(args): z_mean, z_log_var = args epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=epsilon_std) return z_mean + K.exp(z_log_var / 2) * epsilon # note that "output_shape" isn't necessary with the TensorFlow backend z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var]) # we instantiate these layers separately so as to reuse them later decoder_h = Dense(intermediate_dim, activation='relu') decoder_mean = Dense(original_dim, activation='sigmoid') h_decoded = decoder_h(z) x_decoded_mean = decoder_mean(h_decoded) # Custom loss layer class CustomVariationalLayer(Layer): def __init__(self, **kwargs): self.is_placeholder = True super(CustomVariationalLayer, self).__init__(**kwargs) def vae_loss(self, x, x_decoded_mean): xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean) kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1) return K.mean(xent_loss + kl_loss) def call(self, inputs): x = inputs[0] x_decoded_mean = inputs[1] loss = self.vae_loss(x, x_decoded_mean) self.add_loss(loss, inputs=inputs) # We won't actually use the output. return x y = CustomVariationalLayer()([x, x_decoded_mean]) vae = Model(x, y) vae.compile(optimizer='rmsprop', loss=None) # train the VAE on MNIST digits (x_train, y_train), (x_test, y_test) = mnist.load_data() 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:]))) vae.fit(x_train, shuffle=True, epochs=epochs, batch_size=batch_size, validation_data=(x_test, None)) # build a model to project inputs on the latent space encoder = Model(x, z_mean) # display a 2D plot of the digit classes in the latent space x_test_encoded = encoder.predict(x_test, batch_size=batch_size) plt.figure(figsize=(6, 6)) plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test) plt.colorbar() plt.show() # build a digit generator that can sample from the learned distribution decoder_input = Input(shape=(latent_dim,)) _h_decoded = decoder_h(decoder_input) _x_decoded_mean = decoder_mean(_h_decoded) generator = Model(decoder_input, _x_decoded_mean) # display a 2D manifold of the digits n = 15 # figure with 15x15 digits digit_size = 28 figure = np.zeros((digit_size * n, digit_size * n)) # linearly spaced coordinates on the unit square were transformed through the inverse CDF (ppf) of the Gaussian # to produce values of the latent variables z, since the prior of the latent space is Gaussian grid_x = norm.ppf(np.linspace(0.05, 0.95, n)) grid_y = norm.ppf(np.linspace(0.05, 0.95, n)) for i, yi in enumerate(grid_x): for j, xi in enumerate(grid_y): z_sample = np.array([[xi, yi]]) x_decoded = generator.predict(z_sample) digit = x_decoded[0].reshape(digit_size, digit_size) figure[i * digit_size: (i + 1) * digit_size, j * digit_size: (j + 1) * digit_size] = digit plt.figure(figsize=(10, 10)) plt.imshow(figure, cmap='Greys_r') plt.show()