alternative implementation of a VAE

[joepareti54]

I have mdified this code as follows:

import keras from keras.layers import Conv2D, Conv2DTranspose, Input, Flatten, Dense, Lambda, Reshape

from keras.layers import BatchNormalization

from keras.models import Model from keras.datasets import mnist from keras import backend as K import numpy as np from tensorflow.python.framework.ops import disable_eager_execution disable_eager_execution() (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train = x_train / 255 x_test = x_test / 255 img_width = x_train.shape[1] img_height = x_train.shape[2] num_channels = 1 #MNIST --> grey scale so 1 channel x_train = x_train.reshape(x_train.shape[0], img_height, img_width, num_channels) x_test = x_test.reshape(x_test.shape[0], img_height, img_width, num_channels) input_shape = (img_height, img_width, num_channels)

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latent_dim = 2 # Number of latent dim parameters

input_img = Input(shape=input_shape, name='encoder_input') x = Conv2D(32, 3, padding='same', activation='relu')(input_img) x = Conv2D(64, 3, padding='same', activation='relu',strides=(2, 2))(x) x = Conv2D(64, 3, padding='same', activation='relu')(x) x = Conv2D(64, 3, padding='same', activation='relu')(x)

conv_shape = K.int_shape(x) #Shape of conv to be provided to decoder x = Flatten()(x) x = Dense(32, activation='relu')(x) z_mu = Dense(latent_dim, name='z_mu')(x) z_sigma = Dense(latent_dim, name='z_sigma')(x)

def sample_z(args): z_mu, z_sigma = args eps = K.random_normal(shape=(K.shape(z_mu)[0], K.int_shape(z_mu)[1])) return z_mu + K.exp(z_sigma / 2) * eps

z = Lambda(sample_z, output_shape=(latent_dim, ), name='z')([z_mu, z_sigma])

encoder = Model(input_img, [z_mu, z_sigma, z], name='encoder') print(encoder.summary())

decoder_input = Input(shape=(latent_dim, ), name='decoder_input')

x = Dense(conv_shape[1]conv_shape[2]conv_shape[3], activation='relu')(decoder_input) x = Reshape((conv_shape[1], conv_shape[2], conv_shape[3]))(x) x = Conv2DTranspose(32, 3, padding='same', activation='relu',strides=(2, 2))(x) x = Conv2DTranspose(num_channels, 3, padding='same', activation='sigmoid', name='decoder_output')(x)

decoder = Model(decoder_input, x, name='decoder') decoder.summary()

z_decoded = decoder(z)

def vae_loss(x, z_decoded): x = K.flatten(x) z_decoded = K.flatten(z_decoded) recon_loss = keras.metrics.binary_crossentropy(x, z_decoded) kl_loss = -5e-4 * K.mean(1 + z_sigma - K.square(z_mu) - K.exp(z_sigma), axis=-1) return K.mean(recon_loss + kl_loss)

**y = decoder(z) print('y ' ,y)

vae = Model(input_img, y, name='vae')

Compile VAE

vae.compile(optimizer='adam', loss=vae_loss)** vae.summary()

vae.fit(x_train, None, epochs = 10, batch_size = 32, validation_split = 0.2)

`` It ends with the followíng exception:

tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'decoder_target' with dtype float and shape [?,?,?,?] [[{{node decoder_target}}]]