z = encoder + epsilon * srng.uniform(size=encoder.shape, low=-1., high=1.)
decoder_network = MLP(activations=[Tanh(), Logistic()],
dims=[n_latent, n_hidden, n_vis],
biases_init=Constant(0),
name='decoder_network')
decoder_p = decoder_network.apply(z)
# Define the cost
prior_term = -0.5 * (n_latent * T.log(2 * 3.14159) + (z ** 2).sum(axis=1))
reconstruction_term = (x * T.log(decoder_p) +
(1 - x) * T.log(1 - decoder_p)).sum(axis=1)
log_ball_volume = n_latent * (0.5 * T.log(3.14159)
+ T.log(epsilon)) - gammaln(0.5 * n_latent + 1)
cost = -(prior_term + reconstruction_term).mean()
cost.name = 'negative_log_likelihood'
# Initialize the parameters
encoder_network._push_initialization_config()
for layer in encoder_network.linear_transformations:
layer.weights_init = Uniform(
width=12. / (layer.input_dim + layer.output_dim))
encoder_network.initialize()
decoder_network._push_initialization_config()
for layer in decoder_network.linear_transformations:
layer.weights_init = Uniform(
#valid_set_x = theano.shared(numpy.asarray(valid_set_x > 0.5,
# dtype=theano.config.floatX),
# borrow=True)
#n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
#n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
# Define cost and train and validate functions #####
log_ball_vol_D = n_latent * (0.5 * T.log(3.14159)
+ T.log(epsilon)) - T.log(gammaln(0.5 * n_latent + 1))
log_ball_vol_D_minus_1 = (n_latent - 1) * (0.5 * T.log(3.14159)
+ T.log(epsilon)) - T.log(gammaln(0.5 * n_latent + 0.5))
KL_term = T.cast(-log_ball_vol_D + 0.5 * T.log(2 * 3.14159) +
epsilon * n_latent / 3 * T.exp(log_ball_vol_D_minus_1 - log_ball_vol_D) *
(epsilon **2 + 3 * (encoder_2 * encoder_2).sum(axis=1)),
theano.config.floatX)
#KL_divergence = T.cast(-0.5*(1 + encoder_lognu - encoder_mu**2
# - T.exp(encoder_lognu)).sum(axis=1), 'float32')
reconstruction_term = T.cast((x*T.log(decoder_p) +
(1-x)*T.log(1 - decoder_p)).sum(axis=1), 'float32')
cost = (KL_term - reconstruction_term).mean()
