adversarial-autoencoder-master.zip_adversarial_autoencoder_furni

import theano import theano.tensor as T from lasagne.layers import get_output from lasagne.layers import get_all_params from lasagne.updates import nesterov_momentum # forward pass for the encoder, q(z|x) def create_encoder_func(layers): X = T.fmatrix('X') X_batch = T.fmatrix('X_batch') Z = get_output(layers['l_encoder_out'], X, deterministic=True) encoder_func = theano.function( inputs=[theano.In(X_batch)], outputs=Z, givens={ X: X_batch, }, ) return encoder_func # forward pass for the decoder, p(x|z) def create_decoder_func(layers): Z = T.fmatrix('Z') Z_batch = T.fmatrix('Z_batch') X = get_output( layers['l_decoder_out'], inputs={ layers['l_encoder_out']: Z }, deterministic=True ) decoder_func = theano.function( inputs=[theano.In(Z_batch)], outputs=X, givens={ Z: Z_batch, }, ) return decoder_func # forward/backward (optional) pass for the encoder/decoder pair def create_encoder_decoder_func(layers, apply_updates=False): X = T.fmatrix('X') X_batch = T.fmatrix('X_batch') X_hat = get_output(layers['l_decoder_out'], X, deterministic=False) # reconstruction loss encoder_decoder_loss = T.mean( T.mean(T.sqr(X - X_hat), axis=1) ) if apply_updates: # all layers that participate in the forward pass should be updated encoder_decoder_params = get_all_params( layers['l_decoder_out'], trainable=True) encoder_decoder_updates = nesterov_momentum( encoder_decoder_loss, encoder_decoder_params, 0.01, 0.9) else: encoder_decoder_updates = None encoder_decoder_func = theano.function( inputs=[theano.In(X_batch)], outputs=encoder_decoder_loss, updates=encoder_decoder_updates, givens={ X: X_batch, }, ) return encoder_decoder_func # forward/backward (optional) pass for discriminator def create_discriminator_func(layers, apply_updates=False): X = T.fmatrix('X') pz = T.fmatrix('pz') X_batch = T.fmatrix('X_batch') pz_batch = T.fmatrix('pz_batch') # the discriminator receives samples from q(z|x) and p(z) # and should predict to which distribution each sample belongs discriminator_outputs = get_output( layers['l_discriminator_out'], inputs={ layers['l_prior_in']: pz, layers['l_encoder_in']: X, }, deterministic=False, ) # label samples from q(z|x) as 1 and samples from p(z) as 0 discriminator_targets = T.vertical_stack( T.ones((X_batch.shape[0], 1)), T.zeros((pz_batch.shape[0], 1)) ) discriminator_loss = T.mean( T.nnet.binary_crossentropy( discriminator_outputs, discriminator_targets, ) ) if apply_updates: # only layers that are part of the discriminator should be updated discriminator_params = get_all_params( layers['l_discriminator_out'], trainable=True, discriminator=True) discriminator_updates = nesterov_momentum( discriminator_loss, discriminator_params, 0.1, 0.0) else: discriminator_updates = None discriminator_func = theano.function( inputs=[ theano.In(X_batch), theano.In(pz_batch), ], outputs=discriminator_loss, updates=discriminator_updates, givens={ X: X_batch, pz: pz_batch, }, ) return discriminator_func # forward/backward (optional) pass for the generator # note that the generator is the same network as the encoder, # but updated separately def create_generator_func(layers, apply_updates=False): X = T.fmatrix('X') X_batch = T.fmatrix('X_batch') # no need to pass an input to l_prior_in here generator_outputs = get_output( layers['l_encoder_out'], X, deterministic=False) # so pass the output of the generator as the output of the concat layer discriminator_outputs = get_output( layers['l_discriminator_out'], inputs={ layers['l_prior_encoder_concat']: generator_outputs, }, deterministic=False ) # the discriminator learns to predict 1 for q(z|x), # so the generator should fool it into predicting 0 generator_targets = T.zeros_like(X_batch.shape[0]) # so the generator needs to push the discriminator's output to 0 generator_loss = T.mean( T.nnet.binary_crossentropy( discriminator_outputs, generator_targets, ) ) if apply_updates: # only layers that are part of the generator (i.e., encoder) # should be updated generator_params = get_all_params( layers['l_discriminator_out'], trainable=True, generator=True) generator_updates = nesterov_momentum( generator_loss, generator_params, 0.1, 0.0) else: generator_updates = None generator_func = theano.function( inputs=[ theano.In(X_batch), ], outputs=generator_loss, updates=generator_updates, givens={ X: X_batch, }, ) return generator_func if __name__ == '__main__': import model print('building model') layers = model.build_model() print('compiling theano functions') encoder_decoder_func = create_encoder_decoder_func(layers) discriminator_func = create_discriminator_func(layers) generator_func = create_generator_func(layers) import numpy as np X = np.random.random((16, 28 * 28)).astype(np.float32) pz = np.random.uniform(-2, 2, size=(16, 2)).astype(np.float32) print('X.shape = %r' % (X.shape,)) print('pz.shape = %r' % (pz.shape,)) print('running the three forward passes') print encoder_decoder_func(X) print discriminator_func(X, pz) print generator_func(X)