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stochastic_layer.py
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stochastic_layer.py
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import theano
import theano.tensor as T
from lasagne.utils import unroll_scan
from lasagne.layers import MergeLayer, helper, get_output
from lasagne.random import get_rng
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from utils import norm_weight, _p, ortho_weight, tanh, linear, rectifier, get_two_rngs
from normal_layer import normal_layer
from rnn_layer import gru
from rnn_layer import gru_cond
from rnn_layer import lstm
from rnn_layer import lstm_cond
from normal_layer import normal_layer
import numpy as np
rng_numpy, rng_theano = get_two_rngs()
layers = {
'ff': ('normal_layer.param_init_fflayer', 'normal_layer.fflayer'),
'lstm': ('lstm.param_init_lstm', 'lstm.lstm_layer'),
'lstm_cond': ('lstm_cond.param_init_lstm_cond', 'lstm_cond.lstm_cond_layer'),
'gru': ('gru.param_init_gru', 'gru.gru_layer'),
'gru_cond': ('gru_cond.param_init_gru_cond', 'gru_cond.gru_cond_layer'),
}
rng_numpy, rng_theano = get_two_rngs()
gradient_steps=-1
def get_layer(name):
"""
Part of the reason the init is very slow is because,
the layer's constructor is called even when it isn't needed
"""
fns = layers[name]
return eval(fns[0]), eval(fns[1])
def param_init_stochastic( options, params):
# Define MLPs to be used in StochsticRecurrentLayer
mlp_prior_input_dim = options['rnn_cond_wv_dim'] + options['latent_size_z']
params = get_layer('ff')[0](params,nin=mlp_prior_input_dim,nout=options['num_hidden_mlp'],
prefix='mean_prior_dense1',scale = options['stochastic_scale'])
params = get_layer('ff')[0](params,nin=options['num_hidden_mlp'],nout=options['latent_size_z'],
prefix='mean_prior_dense2',scale = options['stochastic_scale'])
params = get_layer('ff')[0](params,nin=mlp_prior_input_dim,nout=options['num_hidden_mlp'],
prefix='log_var_prior_dense1',scale = options['stochastic_scale'])
params = get_layer('ff')[0](params,nin=options['num_hidden_mlp'],nout=options['latent_size_z'],
prefix='log_var_prior_dense2',scale = options['stochastic_scale'])
mlp_q_input_dim = options['latent_size_a'] + options['latent_size_z']
params = get_layer('ff')[0](params,nin=mlp_q_input_dim,nout=options['num_hidden_mlp'],
prefix='mean_q_dense1',scale = options['stochastic_scale'])
params = get_layer('ff')[0](params,nin=options['num_hidden_mlp'],nout=options['latent_size_z'],
prefix='mean_q_dense2',scale = options['stochastic_scale'])
params = get_layer('ff')[0](params,nin=mlp_q_input_dim,nout=options['num_hidden_mlp'],
prefix='log_var_q_dense1',scale = options['stochastic_scale'])
params = get_layer('ff')[0](params,nin=options['num_hidden_mlp'],nout=options['latent_size_z'],
prefix='log_var_q_dense2',scale = options['stochastic_scale'])
return params
def stochastic_layer(options,tparams,
input_p,input_q,
z_init,mu_p_init,
num_units,unroll_scan,
use_mu_residual_q,only_return_final=False,
mask_input=None,
backwards=False,
name='stochastic_layer') :
debug_print = []
if options['cons'] == 0 :
cons=0
elif options['cons'] < 0 :
cons=10 ** options['cons']
else :
raise ValueError()
#debug_print.append( theano.printing.Print('input_p.shapa')(input_p.shape))
#debug_print.append( theano.printing.Print('input_q.shapa')(input_q.shape))
mask = mask_input
seq_len, num_batch, _ = input_p.shape
if z_init is None :
z_init = T.alloc(0., num_batch, options['latent_size_z'])
if mu_p_init is None :
mu_p_init = T.alloc(0., num_batch, options['latent_size_z'])
# Create single recurrent computation step function
# input__n is the n'th vector of the input
#debug_print.append( theano.printing.Print('z_init.shapa')(z_init.shape))
#debug_print.append( theano.printing.Print('mu_p_init.shapa')(mu_p_init.shape))
stochastic_rs = RandomStreams(get_rng().randint(1, 2147462579))
def log_sum_exp(a, b):
return T.log(T.exp(a) + T.exp(b))
def step(noise_n, input_p_n, input_q_n,
z_previous,
mu_p_previous, logvar_p_previous,
mu_q_previous, logvar_q_previous, *args):
####about p ####
input_p = T.concatenate([input_p_n, z_previous], axis=1)
mu_p_1 = get_layer('ff')[1](tparams, input_p, activ=options['nonlin_decoder'],
prefix='mean_prior_dense1')
mu_p = get_layer('ff')[1](tparams,mu_p_1,activ='linear',
prefix='mean_prior_dense2')
logvar_p_1 = get_layer('ff')[1](tparams, input_p, activ=options['nonlin_decoder'],
prefix='log_var_prior_dense1')
logvar_p = get_layer('ff')[1](tparams,logvar_p_1,activ='linear',
prefix='log_var_prior_dense2')
logvar_p = T.log(T.exp(logvar_p)+cons)
####about q ####
input_q_n = T.concatenate([input_q_n,z_previous],axis=1)
mu_q_1 = get_layer('ff')[1](tparams, input_q_n, activ=options['nonlin_decoder'],
prefix='mean_q_dense1')
mu_q = get_layer('ff')[1](tparams,mu_q_1,activ='linear',
prefix='mean_q_dense2')
if use_mu_residual_q :
print "Using residuals for mean_q"
mu_q += mu_p
logvar_q_1 = get_layer('ff')[1](tparams, input_q_n, activ=options['nonlin_decoder'],
prefix='log_var_q_dense1')
logvar_q = get_layer('ff')[1](tparams,logvar_q_1,activ='linear',
prefix='log_var_q_dense2')
#z_n_print = theano.printing.Print('\n logvar_q info \n)')(logvar_q)
logvar_q = T.log(T.exp(logvar_q)+cons)
z_n = mu_q + T.exp(0.5*logvar_q)*noise_n
return z_n, mu_p, logvar_p, mu_q, logvar_q
def step_masked(noise_n, input_p_n, input_q_n, mask_n,
z_previous,
mu_p_previous, logvar_p_previous,
mu_q_previous, logvar_q_previous, *args):
z_n, mu_p, logvar_p, mu_q, logvar_q = step(
noise_n, input_p_n, input_q_n,
z_previous, mu_p_previous, logvar_p_previous,
mu_q_previous, logvar_q_previous, *args)
z_n = T.switch(mask_n, z_n, z_previous)
mu_p = T.switch(mask_n, mu_p, mu_p_previous)
logvar_p = T.switch(mask_n, logvar_p, logvar_p_previous)
mu_q = T.switch(mask_n, mu_q, mu_q_previous)
logvar_q = T.switch(mask_n, logvar_q, logvar_q_previous)
return z_n, mu_p, logvar_p, mu_q, logvar_q
eps = stochastic_rs.normal(
size=(seq_len, num_batch, num_units), avg=0.0, std=1.0)
logvar_init = T.zeros((num_batch,num_units))
if mask is not None :
mask = mask.dimshuffle(0, 1, 'x')
sequences = [eps, input_p, input_q, mask]
step_fun = step_masked
#debug_print.append( theano.printing.Print('mask.shapa')(mask.shape))
else:
sequences = [eps, input_p, input_q]
step_fun = step
if unroll_scan:
# Retrieve the dimensionality of the incoming layer
input_shape = eps.shape[0]
# Explicitly unroll the recurrence instead of using scan
scan_out = unroll_scan(
fn=step_fun,
sequences=sequences,
outputs_info=[z_init, mu_p_init, logvar_init, mu_p_init, logvar_init],
go_backwards=backwards,
n_steps=input_shape)
else:
# Scan op iterates over first dimension of input and repeatedly
# applies the step function
scan_out = theano.scan(
fn=step_fun,
sequences=sequences,
go_backwards=backwards,
outputs_info=[z_init, mu_p_init, logvar_init, mu_p_init, logvar_init],
truncate_gradient=gradient_steps,
n_steps=seq_len,
)[0]
z, mu_p, logvar_p, mu_q, logvar_q = scan_out
#debug_print.append(z_n_print)
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if only_return_final:
assert False
'''
else:
# dimshuffle back to (n_batch, n_time_steps, n_features))
z = z.dimshuffle(1, 0, 2)
mu_p = mu_p.dimshuffle(1, 0, 2)
logvar_p = logvar_p.dimshuffle(1, 0, 2)
mu_q = mu_q.dimshuffle(1, 0, 2)
logvar_q = logvar_q.dimshuffle(1, 0, 2)
'''
# if scan is backward reverse the output
if backwards:
z = z[:, ::-1]
mu_p = mu_p[:, ::-1]
logvar_p = logvar_p[:, ::-1]
mu_q = mu_q[:, ::-1]
logvar_q = logvar_q[:, ::-1]
out_put_res = []
out_put_res.append(z)
out_put_res.append(mu_p)
out_put_res.append(logvar_p)
out_put_res.append(mu_q)
out_put_res.append(logvar_q)
out_put_res.append(debug_print)
return out_put_res
def stochastic_layer_onestep_q(options,tparams,
input_p,input_q,
z_init,mu_p_init,
num_units,unroll_scan,
use_mu_residual_q,only_return_final=False,
backwards=False,
name='stochastic_layer') :
if options['cons'] == 0 :
cons=0
elif options['cons'] < 0 :
cons=10**options['cons']
else :
raise ValueError()
debug_print = []
seq_len, _ = input_p.shape
stochastic_rs = RandomStreams(get_rng().randint(1, 2147462579))
# Create single recurrent computation step function
# input__n is the n'th vector of the input
def log_sum_exp(a, b):
return T.log(T.exp(a) + T.exp(b))
def step(noise_n, input_p_n, input_q_n,
z_previous,
mu_p_previous=None, logvar_p_previous=None,
mu_q_previous=None, logvar_q_previous=None, *args):
####about p ####
input_p = T.concatenate([input_p_n, z_previous], axis=1)
mu_p_1 = get_layer('ff')[1](tparams, input_p, activ=options['nonlin_decoder'],
prefix='mean_prior_dense1')
mu_p = get_layer('ff')[1](tparams,mu_p_1,activ='linear',
prefix='mean_prior_dense2')
logvar_p_1 = get_layer('ff')[1](tparams, input_p, activ=options['nonlin_decoder'],
prefix='log_var_prior_dense1')
logvar_p = get_layer('ff')[1](tparams,logvar_p_1,activ='linear',
prefix='log_var_prior_dense2')
logvar_p = T.log(T.exp(logvar_p)+cons)
####about q ####
input_q_n = T.concatenate([input_q_n,z_previous],axis=1)
mu_q_1 = get_layer('ff')[1](tparams, input_q_n, activ=options['nonlin_decoder'],
prefix='mean_q_dense1')
mu_q = get_layer('ff')[1](tparams,mu_q_1,activ='linear',
prefix='mean_q_dense2')
if use_mu_residual_q :
print "Using residuals for mean_q"
mu_q += mu_p
logvar_q_1 = get_layer('ff')[1](tparams, input_q_n, activ=options['nonlin_decoder'],
prefix='log_var_q_dense1')
logvar_q = get_layer('ff')[1](tparams,logvar_q_1,activ='linear',
prefix='log_var_q_dense2')
logvar_q = T.log(T.exp(logvar_q)+cons)
z_n = mu_q + T.exp(0.5*logvar_q)*noise_n
return z_n, mu_p, logvar_p, mu_q, logvar_q
def step_masked(noise_n, input_p_n, input_q_n, mask_n,
z_previous,
mu_p_previous, logvar_p_previous,
mu_q_previous, logvar_q_previous, *args):
z_n, mu_p, logvar_p, mu_q, logvar_q = step(
noise_n, input_p_n, input_q_n,
z_previous, mu_p_previous, logvar_p_previous,
mu_q_previous, logvar_q_previous, *args)
z_n = T.switch(mask_n, z_n, z_previous)
mu_p = T.switch(mask_n, mu_p, mu_p_previous)
logvar_p = T.switch(mask_n, logvar_p, logvar_p_previous)
mu_q = T.switch(mask_n, mu_q, mu_q_previous)
logvar_q = T.switch(mask_n, logvar_q, logvar_q_previous)
return z_n, mu_p, logvar_p, mu_q, logvar_q
eps = stochastic_rs.normal(
size=( 1,num_units), avg=0.0, std=1.0)
logvar_init = T.zeros((num_units))
z, mu_p, logvar_p, mu_q, logvar_q = step(eps, input_p, input_q,z_init)
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if only_return_final:
assert False
'''
else:
# dimshuffle back to (n_batch, n_time_steps, n_features))
z = z.dimshuffle(1, 0, 2)
mu_p = mu_p.dimshuffle(1, 0, 2)
logvar_p = logvar_p.dimshuffle(1, 0, 2)
mu_q = mu_q.dimshuffle(1, 0, 2)
logvar_q = logvar_q.dimshuffle(1, 0, 2)
'''
# if scan is backward reverse the output
if backwards:
z = z[:, ::-1]
mu_p = mu_p[:, ::-1]
logvar_p = logvar_p[:, ::-1]
mu_q = mu_q[:, ::-1]
logvar_q = logvar_q[:, ::-1]
out_put_res = []
out_put_res.append(z)
out_put_res.append(mu_p)
out_put_res.append(logvar_p)
out_put_res.append(mu_q)
out_put_res.append(logvar_q)
out_put_res.append(debug_print)
return out_put_res
def stochastic_layer_onestep_noq(options,tparams,
input_p,
z_init,mu_p_init,
num_units,unroll_scan,
use_mu_residual_q,only_return_final=False,
backwards=False,
name='stochastic_layer') :
if options['cons'] == 0 :
cons=0
elif options['cons'] < 0 :
cons=10**options['cons']
else :
raise ValueError()
debug_print = []
seq_len, _ = input_p.shape
stochastic_rs = RandomStreams(get_rng().randint(1, 2147462579))
# Create single recurrent computation step function
# input__n is the n'th vector of the input
def log_sum_exp(a, b):
return T.log(T.exp(a) + T.exp(b))
def step(noise_n, input_p_n,
z_previous,
mu_p_previous=None, logvar_p_previous=None,
mu_q_previous=None, logvar_q_previous=None, *args):
####about p ####
input_p = T.concatenate([input_p_n, z_previous], axis=1)
mu_p_1 = get_layer('ff')[1](tparams, input_p, activ=options['nonlin_decoder'],
prefix='mean_prior_dense1')
mu_p = get_layer('ff')[1](tparams,mu_p_1,activ='linear',
prefix='mean_prior_dense2')
logvar_p_1 = get_layer('ff')[1](tparams, input_p, activ=options['nonlin_decoder'],
prefix='log_var_prior_dense1')
logvar_p = get_layer('ff')[1](tparams,logvar_p_1,activ='linear',
prefix='log_var_prior_dense2')
logvar_p = T.log(T.exp(logvar_p)+cons)
'''
####about q ####
input_q_n = T.concatenate([input_q_n,z_previous],axis=1)
mu_q_1 = get_layer('ff')[1](tparams, input_q_n, activ=options['nonlin_decoder'],
prefix='mean_q_dense1')
mu_q = get_layer('ff')[1](tparams,mu_q_1,activ='linear',
prefix='mean_q_dense2')
if use_mu_residual_q :
print "Using residuals for mean_q"
mu_q += mu_p
logvar_q_1 = get_layer('ff')[1](tparams, input_q_n, activ=options['nonlin_decoder'],
prefix='log_var_q_dense1')
logvar_q = get_layer('ff')[1](tparams,logvar_q_1,activ='linear',
prefix='log_var_q_dense2')
logvar_q = T.log(T.exp(logvar_q)+cons)
'''
z_n = mu_p + T.exp(0.5*logvar_p)*noise_n
return z_n, mu_p, logvar_p #, mu_q, logvar_q
def step_masked(noise_n, input_p_n, input_q_n, mask_n,
z_previous,
mu_p_previous, logvar_p_previous,
mu_q_previous, logvar_q_previous, *args):
z_n, mu_p, logvar_p, mu_q, logvar_q = step(
noise_n, input_p_n, input_q_n,
z_previous, mu_p_previous, logvar_p_previous,
mu_q_previous, logvar_q_previous, *args)
z_n = T.switch(mask_n, z_n, z_previous)
mu_p = T.switch(mask_n, mu_p, mu_p_previous)
logvar_p = T.switch(mask_n, logvar_p, logvar_p_previous)
'''
mu_q = T.switch(mask_n, mu_q, mu_q_previous)
logvar_q = T.switch(mask_n, logvar_q, logvar_q_previous)
'''
return z_n, mu_p, logvar_p #, mu_q, logvar_q
eps = stochastic_rs.normal(
size=( 1,num_units), avg=0.0, std=1.0)
logvar_init = T.zeros((num_units))
z, mu_p, logvar_p = step(eps, input_p,z_init)
# When it is requested that we only return the final sequence step,
# we need to slice it out immediately after scan is applied
if only_return_final:
assert False
'''
else:
# dimshuffle back to (n_batch, n_time_steps, n_features))
z = z.dimshuffle(1, 0, 2)
mu_p = mu_p.dimshuffle(1, 0, 2)
logvar_p = logvar_p.dimshuffle(1, 0, 2)
mu_q = mu_q.dimshuffle(1, 0, 2)
logvar_q = logvar_q.dimshuffle(1, 0, 2)
'''
# if scan is backward reverse the output
if backwards:
z = z[:, ::-1]
mu_p = mu_p[:, ::-1]
logvar_p = logvar_p[:, ::-1]
'''
mu_q = mu_q[:, ::-1]
logvar_q = logvar_q[:, ::-1]
'''
out_put_res = []
out_put_res.append(z)
out_put_res.append(mu_p)
out_put_res.append(logvar_p)
'''
out_put_res.append(mu_q)
out_put_res.append(logvar_q)
'''
out_put_res.append(debug_print)
return out_put_res