forked from mpezeshki/variational-autoencoders
/
model.py
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/
model.py
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import theano
import theano.tensor as T
import numpy as np
import cPickle as pickle
from theano_toolkit import utils as U
from theano_toolkit.parameters import Parameters
import feedforward
import vae
import lstm
def build(P, name,
input_size=200, z_size=200,
hidden_layer_size=2500,
x_extractor_layers=[600] * 4,
z_extractor_layers=[500] * 4,
prior_layers=[500] * 4,
generation_layers=[600] * 4,
inference_layers=[500] * 4):
def weight_init(x,y):
return np.random.uniform(-0.08, 0.08, (x,y))
X_extractor = feedforward.build_classifier(
P, "x_extractor",
input_sizes=[input_size],
hidden_sizes=x_extractor_layers[:-1],
output_size=x_extractor_layers[-1],
initial_weights=weight_init,
output_initial_weights=weight_init,
activation=T.nnet.relu,
output_activation=T.nnet.relu
)
Z_extractor = feedforward.build_classifier(
P, "z_extractor",
input_sizes=[z_size],
hidden_sizes=z_extractor_layers[:-1],
output_size=z_extractor_layers[-1],
initial_weights=weight_init,
output_initial_weights=weight_init,
activation=T.nnet.relu,
output_activation=T.nnet.relu
)
prior = vae.build_inferer(
P, "prior",
input_sizes=[hidden_layer_size],
hidden_sizes=prior_layers,
output_size=z_size,
initial_weights=weight_init,
activation=T.nnet.relu,
initialise_outputs=True
)
generate = vae.build_inferer(
P, "generator",
input_sizes=[hidden_layer_size, z_extractor_layers[-1]],
hidden_sizes=generation_layers,
output_size=input_size,
initial_weights=weight_init,
activation=T.nnet.relu,
initialise_outputs=True
)
P.init_recurrence_hidden = np.zeros((hidden_layer_size,))
P.init_recurrence_cell = np.zeros((hidden_layer_size,))
recurrence = lstm.build_step(
P, "recurrence",
input_sizes=[x_extractor_layers[-1],z_extractor_layers[-1]],
hidden_size=hidden_layer_size
)
infer = vae.build_inferer(
P, "infer",
input_sizes=[hidden_layer_size, x_extractor_layers[-1]],
hidden_sizes=generation_layers,
output_size=z_size,
initial_weights=weight_init,
activation=T.nnet.relu,
initialise_outputs=True
)
def sample():
init_hidden = T.tanh(P.init_recurrence_hidden)
init_cell = P.init_recurrence_cell
init_hidden_batch = T.alloc(init_hidden, 1, hidden_layer_size)
init_cell_batch = T.alloc(init_cell, 1, hidden_layer_size)
noise = U.theano_rng.normal(size=(40,1,z_size))
def _step(eps, prev_cell, prev_hidden):
_, z_prior_mean, z_prior_std = prior([prev_hidden])
z_sample = z_prior_mean + eps * z_prior_std
z_feat = Z_extractor([z_sample])
_, x_mean, _ = generate([prev_hidden, z_feat])
x_feat = X_extractor([x_mean])
curr_cell, curr_hidden = recurrence(x_feat, z_feat, prev_cell, prev_hidden)
return curr_cell, curr_hidden, x_mean
[cells,hiddens,x_means],_ = theano.scan(
_step,
sequences=[noise],
outputs_info=[init_cell_batch,init_hidden_batch,None],
)
return x_means
def extract(X,l):
init_hidden = T.tanh(P.init_recurrence_hidden)
init_cell = P.init_recurrence_cell
init_hidden_batch = T.alloc(init_hidden, X.shape[1], hidden_layer_size)
init_cell_batch = T.alloc(init_cell, X.shape[1], hidden_layer_size)
noise = U.theano_rng.normal(size=(X.shape[0],X.shape[1],z_size))
reset_init_mask = U.theano_rng.binomial(size=(X.shape[0],X.shape[1]),p=0.025)
X_feat = X_extractor([X])
def _step(t,x_feat, eps, reset_mask, prev_cell, prev_hidden):
reset_mask = reset_mask.dimshuffle(0,'x')
_, z_prior_mean, z_prior_std = prior([prev_hidden])
_, z_mean, z_std = infer([prev_hidden, x_feat])
z_sample = z_mean + eps * z_std
z_feat = Z_extractor([z_sample])
_, x_mean, x_std = generate([prev_hidden, z_feat])
curr_cell, curr_hidden = recurrence(x_feat, z_feat, prev_cell, prev_hidden)
curr_cell = T.switch(
reset_mask, init_cell_batch, curr_cell)
curr_hidden = T.switch(
reset_mask, init_hidden_batch, curr_hidden)
mask = (t < l).dimshuffle(0,'x')
return tuple(
T.switch(mask,out,0)
for out in (
curr_cell, curr_hidden,
z_prior_mean, z_prior_std,
z_sample, z_mean, z_std,
x_mean, x_std
))
[_, _,
Z_prior_mean, Z_prior_std,
Z_sample, Z_mean, Z_std,
X_mean, X_std], _ = theano.scan(
_step,
sequences=[T.arange(X_feat.shape[0]),X_feat,noise,reset_init_mask],
outputs_info=[init_cell_batch, init_hidden_batch] +
[None] * 7,
)
return [
Z_prior_mean, Z_prior_std,
Z_mean, Z_std,
X_mean, X_std,
]
return extract, sample
def cost(X,
Z_prior_mean, Z_prior_std,
Z_mean, Z_std,
X_mean, X_std,
lengths):
mask = T.arange(X.shape[0]).dimshuffle(0,'x')\
< lengths.dimshuffle('x',0)
encoding_cost = T.switch(mask,
vae.kl_divergence(
mean_1=Z_mean, std_1=Z_std,
mean_2=Z_prior_mean, std_2=Z_prior_std,
),
0
)
reconstruction_cost = T.switch(mask,
vae.gaussian_nll(X, X_mean, X_std),
0
)
return -T.sum(encoding_cost + reconstruction_cost)/T.sum(mask)