def build_model(self, tparams, options):
trng = RandomStreams(1234)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
xW = tensor.matrix('xW', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
n_timesteps = xW.shape[0]
n_samples = xW.shape[1]
embW = tparams['Wemb'][xW.flatten()].reshape(
[n_timesteps, n_samples, options['word_encoding_size']])
embW_rev = tparams['Wemb'][xW[::-1, :].flatten()].reshape(
[n_timesteps, n_samples, options['word_encoding_size']])
xI = tensor.matrix('xI', dtype=config.floatX)
xAux = tensor.matrix('xAux', dtype=config.floatX)
if options.get('swap_aux', 0):
xAuxEmb = tensor.dot(xAux,
tparams['WIemb_aux']) + tparams['b_Img_aux']
else:
xAuxEmb = xAux
embImg = (tensor.dot(xI, tparams['WIemb']) + tparams['b_Img']).reshape(
[1, n_samples, options['image_encoding_size']])
emb = tensor.concatenate([embImg, embW], axis=0)
emb_rev = tensor.set_subtensor(
embW_rev[mask[::-1, :].argmax(axis=0) - 1,
tensor.arange(n_samples), :], embImg[0, :, :])
#This is implementation of input dropout !!
if options['use_dropout']:
emb = dropout_layer(emb,
use_noise,
trng,
options['drop_prob_encoder'],
shp=emb.shape)
if options.get('en_aux_inp', 0):
xAuxEmb = dropout_layer(xAuxEmb,
use_noise,
trng,
options['drop_prob_aux'],
shp=xAuxEmb.shape)
#############################################################################################################################
# This implements core lstm
rval, updatesLSTM = basic_lstm_layer(tparams,
emb[:n_timesteps, :, :],
xAuxEmb,
use_noise,
options,
prefix='lstm',
sched_prob_mask=[])
#############################################################################################################################
# This implements core reverse lstm
rev_rval, rev_updatesLSTM = basic_lstm_layer(
tparams,
emb_rev[:n_timesteps, :, :],
xAuxEmb,
use_noise,
options,
prefix='rev_lstm',
sched_prob_mask=[])
#############################################################################################################################
# NOTE1: we are leaving out the first prediction, which was made for the image and is meaningless.
if options['use_dropout']:
# XXX : Size given to dropout is missing one dimension. This keeps the dropped units consistent across time!?.
# ### Is this a good bug ?
p = dropout_layer(
sliceT(rval[0][1:, :, :], options.get('hidden_depth', 1),
options['hidden_size']), use_noise, trng,
options['drop_prob_decoder'],
(n_samples, options['hidden_size']))
rev_p = dropout_layer(
sliceT(rev_rval[0][:, :, :], options.get('hidden_depth', 1),
options['hidden_size']), use_noise, trng,
options['drop_prob_decoder'],
(n_samples, options['hidden_size']))
else:
p = sliceT(rval[0][1:, :, :], options.get('hidden_depth', 1),
options['hidden_size'])
rev_p = sliceT(rev_rval[0][:, :, :],
options.get('hidden_depth',
1), options['hidden_size'])
n_out_samps = (n_timesteps - 2) * n_samples
if options.get('class_out_factoring', 0) == 0:
pW = (tensor.dot(p[:-1, :, :] + rev_p[::-1, :, :][2:, :, :],
tparams['Wd']) + tparams['bd']).reshape(
[n_out_samps, options['output_size']])
pWSft = tensor.nnet.softmax(pW)
totProb = pWSft[tensor.arange(n_out_samps), xW[1:-1, :].flatten()]
out_list = [pWSft, totProb, p]
else:
ixtoclsinfo_t = tensor.as_tensor_variable(options['ixtoclsinfo'])
xC = ixtoclsinfo_t[xW[1:, :].flatten(), 0]
pW = ((tparams['Wd'][:, xC, :].T *
(p.reshape([1, n_out_samps, options['hidden_size']]))).sum(
axis=-1).T + tparams['bd'][:, xC, :])
pWSft = tensor.nnet.softmax(pW[0, :, :])
pC = (tensor.dot(p, tparams['WdCls']) + tparams['bdCls']).reshape(
[n_out_samps, options['nClasses']])
pCSft = tensor.nnet.softmax(pC)
totProb = pWSft[tensor.arange(n_out_samps), ixtoclsinfo_t[xW[1:,:].flatten(),3]] * \
pCSft[tensor.arange(n_out_samps), xC]
out_list = [pWSft, pCSft, totProb, p]
# XXX : THIS IS VERY FISHY, CHECK THE MASK INDEXING AGAIN
probs_valid = tensor.log(totProb + 1e-10) * mask[1:-1, :].flatten()
tot_cost = -(probs_valid.sum())
tot_pplx = -(tensor.log2(totProb + 1e-10) *
mask[1:-1, :].flatten()).sum()
cost = [tot_cost / options['batch_size'], tot_pplx]
inp_list = [xW, mask, xI]
if options.get('en_aux_inp', 0):
inp_list.append(xAux)
if options.get('sched_sampling_mode', None) != None:
inp_list.append(curr_epoch)
per_sent_prob = probs_valid.reshape([n_timesteps - 2,
n_samples]).sum(axis=0)
f_per_sentLogP = theano.function(inp_list,
per_sent_prob,
name='f_pred_logprob',
updates=updatesLSTM)
f_pred_prob = ['', f_per_sentLogP, '']
return use_noise, inp_list, f_pred_prob, cost, out_list, updatesLSTM
def build_eval_other_sent(self, tparams, options, model_npy):
zipp(model_npy, self.model_th)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
xW = tensor.matrix('xW', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
n_timesteps = xW.shape[0]
n_samples = xW.shape[1]
n_out_samps = (n_timesteps - 1) * n_samples
embW = tparams['Wemb'][xW.flatten()].reshape(
[n_timesteps, n_samples, options['word_encoding_size']])
xI = tensor.matrix('xI', dtype=config.floatX)
xAux = tensor.matrix('xAux', dtype=config.floatX)
if options.get('swap_aux', 0):
xAuxEmb = tensor.dot(xAux,
tparams['WIemb_aux']) + tparams['b_Img_aux']
else:
xAuxEmb = xAux
embImg = (tensor.dot(xI, tparams['WIemb']) + tparams['b_Img']).reshape(
[1, n_samples, options['image_encoding_size']])
emb = tensor.concatenate([embImg, embW], axis=0)
rval, updatesLSTM = basic_lstm_layer(tparams,
emb[:n_timesteps, :, :],
xAuxEmb,
use_noise,
options,
prefix=options['generator'])
p = sliceT(rval[0][1:, :, :], options.get('hidden_depth', 1),
options['hidden_size'])
pW = (tensor.dot(p, tparams['Wd']) + tparams['bd']).reshape(
[n_out_samps, options['output_size']])
pWSft = tensor.nnet.softmax(pW)
totProb = pWSft[tensor.arange(n_out_samps), xW[1:, :].flatten()]
# #pred = tensor.nnet.softmax(p)
#
# #pred = rval[2]
#
# #pred = pred[1:,:,:]
#
# def accumCost(pred,xW,m,c_sum,ppl_sum):
# pred = tensor.nnet.softmax(pred)
# c_sum += (tensor.log(pred[tensor.arange(n_samples), xW]+1e-20) * m)
# ppl_sum += -(tensor.log2(pred[tensor.arange(n_samples), xW]+1e-10) * m)
# return c_sum, ppl_sum
#
# sums, upd = theano.scan(fn=accumCost,
# outputs_info=[tensor.alloc(numpy_floatX(0.), 1,n_samples),
# tensor.alloc(numpy_floatX(0.), 1,n_samples)],
# sequences = [p, xW[1:,:], mask[1:,:]])
# NOTE1: we are leaving out the first prediction, which was made for the image
# and is meaningless. Here cost[0] contains log probability (log10) and cost[1] contains
# perplexity (log2)
tot_cost = -(tensor.log(totProb + 1e-10) * mask[1:, :].flatten()).sum()
cost = tot_cost / options['batch_size']
inp_list = [xW, mask, xI]
if options.get('en_aux_inp', 0):
inp_list.append(xAux)
self.f_pred_prob_other = theano.function(inp_list,
p,
name='f_pred_prob',
updates=updatesLSTM)
#f_pred = theano.function([xW, mask], pred.argmax(axis=1), name='f_pred')
#cost = -tensor.log(pred[tensor.arange(n_timesteps),tensor.arange(n_samples), xW] + 1e-8).mean()
self.f_eval_other = theano.function(inp_list, cost, name='f_eval')
return use_noise, inp_list, self.f_pred_prob_other, cost, pW, updatesLSTM
def build_model(self, tparams, options):
trng = RandomStreams(1234)
# Used for dropout.
self.use_noise = theano.shared(numpy_floatX(0.))
if self.use_shared_features == False:
xI = tensor.tensor3('xI', dtype=config.floatX)
xIemb = xI
n_timesteps = xI.shape[0]
n_samples = xI.shape[1]
else:
xI = tensor.matrix('xI', dtype='int64')
n_timesteps = xI.shape[0]
n_samples = xI.shape[1]
#feats = tensor.concatenate([self.features,tensor.alloc(numpy_floatX(0.),self.image_feat_size,1)],axis=1).T
xIemb = self.features[xI.flatten(), :].reshape(
[n_timesteps, n_samples, self.image_feat_size])
samp_lens = tensor.vector('sL', dtype='int64')
#This is implementation of input dropout !!
if options['use_dropout']:
emb = dropout_layer(xIemb,
self.use_noise,
trng,
options['drop_prob_encoder'],
shp=xIemb.shape)
#############################################################################################################################
# This implements core lstm
rval, updatesLSTM = self.lstm_enc_layer(tparams,
emb,
prefix=self.mp + 'lstm')
#############################################################################################################################
# This implements core reverse lstm
if self.encoder == 'bilstm':
rev_rval, rev_updatesLSTM = basic_lstm_layer(tparams,
emb[::-1, :, :],
prefix=self.mp +
'rev_lstm')
#############################################################################################################################
# NOTE1: we are leaving out the first prediction, which was made for the image and is meaningless.
p = sliceT(rval[0][samp_lens, tensor.arange(n_samples), :],
self.hidden_depth, self.hidden_size)
if self.encoder == 'bilstm':
rev_p = sliceT(rev_rval[0][-1, :, :], self.hidden_depth,
self.hidden_size)
feat_enc = p + rev_p if self.encoder == 'bilstm' else p
if options.get('encoder_add_mean', 0):
feat_enc = feat_enc + (sliceT(rval[0], self.hidden_depth,
self.hidden_size).sum(axis=0) /
samp_lens[:, None])
inp_list = [xI, samp_lens]
return self.use_noise, inp_list, feat_enc, updatesLSTM
def build_eval_other_sent(self, tparams, options, model_npy):
zipp(model_npy, self.model_th)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
xW = tensor.matrix('xW', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
n_timesteps = xW.shape[0]
n_samples = xW.shape[1]
n_out_samps = (n_timesteps - 1) * n_samples
embW = tparams['Wemb'][xW.flatten()].reshape(
[n_timesteps, n_samples, options['word_encoding_size']])
xI = tensor.matrix('xI', dtype=config.floatX)
xAux = tensor.matrix('xAux', dtype=config.floatX)
if options.get('swap_aux', 0):
xAuxEmb = tensor.dot(xAux,
tparams['WIemb_aux']) + tparams['b_Img_aux']
else:
xAuxEmb = xAux
embImg = (tensor.dot(xI, tparams['WIemb']) + tparams['b_Img']).reshape(
[1, n_samples, options['image_encoding_size']])
emb = tensor.concatenate([embImg, embW], axis=0)
rval, updatesLSTM = basic_lstm_layer(tparams,
emb[:n_timesteps, :, :],
xAuxEmb,
use_noise,
options,
prefix=options['generator'])
p = sliceT(rval[0][1:, :, :], options.get('hidden_depth', 1),
options['hidden_size'])
if options.get('class_out_factoring', 0) == 0:
pW = (tensor.dot(p, tparams['Wd']) + tparams['bd']).reshape(
[n_out_samps, options['output_size']])
pWSft = tensor.nnet.softmax(pW)
totProb = pWSft[tensor.arange(n_out_samps), xW[1:, :].flatten()]
out_list = [pWSft, totProb, p]
else:
ixtoclsinfo_t = tensor.as_tensor_variable(self.clsinfo)
xC = ixtoclsinfo_t[xW[1:, :].flatten(), 0]
pW = ((tparams['Wd'][:, xC, :].T *
((p.reshape([1, n_out_samps, options['hidden_size']]) -
tparams['WdCls'][:, xC].T))).sum(axis=-1).T +
tparams['bd'][:, xC, :])
pWSft = tensor.nnet.softmax(pW[0, :, :])
pC = (tensor.dot(p, tparams['WdCls']) + tparams['bdCls']).reshape(
[n_out_samps, options['nClasses']])
pCSft = tensor.nnet.softmax(pC)
totProb = pWSft[tensor.arange(n_out_samps), ixtoclsinfo_t[xW[1:,:].flatten(),3]] * \
pCSft[tensor.arange(n_out_samps), xC]
tot_cost = -(tensor.log(totProb + 1e-10) * mask[1:, :].flatten()
).reshape([n_timesteps - 1, n_samples])
cost = tot_cost.sum(axis=0)
inp_list = [xW, mask, xI]
if options.get('en_aux_inp', 0):
inp_list.append(xAux)
self.f_pred_prob_other = theano.function([xW, xI, xAux],
pWSft,
name='f_pred_prob',
updates=updatesLSTM)
#f_pred = theano.function([xW, mask], pred.argmax(axis=1), name='f_pred')
#cost = -tensor.log(pred[tensor.arange(n_timesteps),tensor.arange(n_samples), xW] + 1e-8).mean()
self.f_eval_other = theano.function(inp_list, cost, name='f_eval')
return use_noise, inp_list, self.f_pred_prob_other, cost, pW, updatesLSTM
def build_model(self, tparams, options, xI=None, xAux=None, attn_nw=None):
self.trng = RandomStreams(int(time.time()))
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
xW = tensor.matrix('xW', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
n_timesteps = xW.shape[0]
n_samples = xW.shape[1]
embW = tparams['Wemb'][xW.flatten()].reshape(
[n_timesteps, n_samples, options['word_encoding_size']])
if xI == None:
xI = tensor.matrix('xI', dtype=config.floatX)
embImg = (tensor.dot(xI, tparams['WIemb']) + tparams['b_Img'])
xI_is_inp = True
else:
embImg = xI
xI_is_inp = False
if xAux == None:
xAux = tensor.matrix(
'xAux',
dtype=config.floatX) if attn_nw == None else tensor.tensor3(
'xAux', dtype=config.floatX)
if (options.get('swap_aux', 1)) and (attn_nw == None):
xAuxEmb = tensor.dot(
xAux, tparams['WIemb_aux']) + tparams['b_Img_aux']
else:
xAuxEmb = xAux
xA_is_inp = True
else:
xA_is_inp = False
if options.get('encode_gt_sentences', 0):
xAuxEmb = tensor.dot(
xAux, tparams['WIemb_aux']) + tparams['b_Img_aux']
else:
xAuxEmb = xAux
embImg = embImg.reshape([1, n_samples, options['image_encoding_size']])
emb = tensor.concatenate([embImg, embW], axis=0)
#This is implementation of input dropout !!
if options['use_dropout']:
emb = dropout_layer(emb,
use_noise,
self.trng,
options['drop_prob_encoder'],
shp=emb.shape)
if (options.get('en_aux_inp', 0)) and (attn_nw == None):
xAuxEmb = dropout_layer(xAuxEmb,
use_noise,
self.trng,
options['drop_prob_aux'],
shp=xAuxEmb.shape)
# Implement scehduled sampling!
if options.get('sched_sampling_mode', None) != None:
curr_epoch = tensor.scalar(name='curr_epoch', dtype=config.floatX)
# Assign the probabilies according to the scheduling mode
if options['sched_sampling_mode'] == 'linear':
prob = tensor.maximum(
options['sslin_min'], options['sched_sampling_const'] -
options['sslin_slope'] * curr_epoch)
elif options['sched_sampling_mode'] == 'exp':
raise ValueError(
'ERROR: %s --> This solver type is not yet supported' %
(options['sched_sampling_mode']))
elif options['sched_sampling_mode'] == 'invsig':
raise ValueError(
'ERROR: %s --> This solver type is not yet supported' %
(options['sched_sampling_mode']))
else:
raise ValueError(
'ERROR: %s --> This scheduling type is unknown' %
(options['sched_sampling_mode']))
# Now to build the mask. We don't want to do this coin toss when
# feeding in image feature and the start symbol
sched_mask = self.trng.binomial((n_timesteps - 2, n_samples),
p=prob,
n=1,
dtype='int64')
sched_mask = tensor.concatenate(
[sched_mask, tensor.alloc(1, 2, n_samples)], axis=0)
else:
sched_mask = []
#############################################################################################################################
# This implements core lstm
rval, updatesLSTM = basic_lstm_layer(tparams,
emb[:n_timesteps, :, :],
xAuxEmb,
use_noise,
options,
prefix=options['generator'],
sched_prob_mask=sched_mask,
attn_nw=attn_nw)
#############################################################################################################################
# NOTE1: we are leaving out the first prediction, which was made for the image and is meaningless.
if options['use_dropout']:
# XXX : Size given to dropout is missing one dimension. This keeps the dropped units consistent across time!?.
# ### Is this a good bug ?
p = dropout_layer(
sliceT(rval[0][1:, :, :], options.get('hidden_depth', 1),
options['hidden_size']), use_noise, self.trng,
options['drop_prob_decoder'],
(n_samples, options['hidden_size']))
else:
p = sliceT(rval[0][1:, :, :], options.get('hidden_depth', 1),
options['hidden_size'])
if options.get('class_out_factoring', 0) == 1:
if options.get('cls_diff_layer', 0) == 1:
pC_inp = dropout_layer(
sliceT(rval[0][1:, :, :],
options.get('hidden_depth', 1) - 2,
options['hidden_size']), use_noise, self.trng,
options['drop_prob_decoder'],
(n_samples, options['hidden_size']))
else:
pC_inp = p
n_out_samps = (n_timesteps - 1) * n_samples
if options.get('class_out_factoring', 0) == 0:
pW = (tensor.dot(p, tparams['Wd']) + tparams['bd']).reshape(
[n_out_samps, options['output_size']])
if options.get('use_gumbel_mse', 0) == 0:
pWSft = tensor.nnet.softmax(pW)
else:
w_out = ifelse(
self.usegumbel,
gumbel_softmax_sample(self.trng,
pW,
self.gumb_temp,
hard=options.get(
'use_gumbel_hard', False)),
tensor.nnet.softmax(pW))
# This is not exactly right, but just testing
pWSft = w_out
totProb = pWSft[tensor.arange(n_out_samps), xW[1:, :].flatten()]
out_list = [pWSft, totProb, pW]
else:
ixtoclsinfo_t = tensor.as_tensor_variable(self.clsinfo)
xC = ixtoclsinfo_t[xW[1:, :].flatten(), 0]
if options.get('cls_zmean', 1):
pW = ((tparams['Wd'][:, xC, :].T *
((p.reshape([1, n_out_samps, options['hidden_size']]) -
tparams['WdCls'][:, xC].T))).sum(axis=-1).T +
tparams['bd'][:, xC, :])
else:
pW = ((tparams['Wd'][:, xC, :].T *
(p.reshape([1, n_out_samps, options['hidden_size']]))
).sum(axis=-1).T + tparams['bd'][:, xC, :])
pWSft = tensor.nnet.softmax(pW[0, :, :])
pC = (tensor.dot(pC_inp, tparams['WdCls']) +
tparams['bdCls']).reshape([n_out_samps, options['nClasses']])
pCSft = tensor.nnet.softmax(pC)
totProb = pWSft[tensor.arange(n_out_samps), ixtoclsinfo_t[xW[1:,:].flatten(),3]] * \
pCSft[tensor.arange(n_out_samps), xC]
out_list = [pWSft, pCSft, totProb, p]
tot_cost = -(tensor.log(totProb + 1e-10) * mask[1:, :].flatten()).sum()
tot_pplx = -(tensor.log2(totProb + 1e-10) *
mask[1:, :].flatten()).sum()
cost = [
tot_cost / tensor.cast(n_samples, dtype=config.floatX), tot_pplx
]
inp_list = [xW, mask]
if xI_is_inp:
inp_list.append(xI)
if options.get('en_aux_inp', 0) and xA_is_inp:
inp_list.append(xAux)
if options.get('sched_sampling_mode', None) != None:
inp_list.append(curr_epoch)
f_pred_prob = theano.function([xW, xI, xAux],
out_list,
name='f_pred_prob',
updates=updatesLSTM)
return use_noise, inp_list, f_pred_prob, cost, out_list, updatesLSTM
def build_model(self, tparams, options):
trng = RandomStreams(1234)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
xW = tensor.matrix('xW', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
n_timesteps = xW.shape[0]
n_samples = xW.shape[1]
embW = tparams['Wemb'][xW.flatten()].reshape([n_timesteps,
n_samples,
options['word_encoding_size']])
xI = tensor.matrix('xI', dtype=config.floatX)
xAux = tensor.matrix('xAux', dtype=config.floatX)
if options.get('swap_aux',0):
xAuxEmb = tensor.dot(xAux,tparams['WIemb_aux']) + tparams['b_Img_aux']
else:
xAuxEmb = xAux
embImg = (tensor.dot(xI, tparams['WIemb']) + tparams['b_Img']).reshape([1,n_samples,options['image_encoding_size']]);
emb = tensor.concatenate([embImg, embW], axis=0)
#This is implementation of input dropout !!
if options['use_dropout']:
emb = dropout_layer(emb, use_noise, trng, options['drop_prob_encoder'], shp = emb.shape)
if options.get('en_aux_inp',0):
xAuxEmb = dropout_layer(xAuxEmb, use_noise, trng, options['drop_prob_aux'], shp = xAuxEmb.shape)
# Implement scehduled sampling!
if options.get('sched_sampling_mode',None) != None:
curr_epoch = tensor.scalar(name='curr_epoch',dtype=config.floatX)
# Assign the probabilies according to the scheduling mode
if options['sched_sampling_mode'] == 'linear':
prob = tensor.maximum(options['sslin_min'],options['sched_sampling_const'] - options['sslin_slope'] * curr_epoch)
elif options['sched_sampling_mode'] == 'exp':
raise ValueError('ERROR: %s --> This solver type is not yet supported'%(options['sched_sampling_mode']))
elif options['sched_sampling_mode'] == 'invsig':
raise ValueError('ERROR: %s --> This solver type is not yet supported'%(options['sched_sampling_mode']))
else:
raise ValueError('ERROR: %s --> This scheduling type is unknown'%(options['sched_sampling_mode']))
# Now to build the mask. We don't want to do this coin toss when
# feeding in image feature and the start symbol
sched_mask = trng.binomial((n_timesteps - 2, n_samples), p=prob, n=1, dtype='int64')
sched_mask = tensor.concatenate([sched_mask, tensor.alloc(1, 2, n_samples)],axis=0)
else:
sched_mask = []
#############################################################################################################################
# This implements core lstm
rval, updatesLSTM = basic_lstm_layer(tparams, emb[:n_timesteps,:,:], xAuxEmb, use_noise, options,
prefix=options['generator'], sched_prob_mask = sched_mask)
#############################################################################################################################
# NOTE1: we are leaving out the first prediction, which was made for the image and is meaningless.
if options['use_dropout']:
# XXX : Size given to dropout is missing one dimension. This keeps the dropped units consistent across time!?.
# ### Is this a good bug ?
p = dropout_layer(sliceT(rval[0][1:,:,:],options.get('hidden_depth',1)-1,options['hidden_size']), use_noise, trng,
options['drop_prob_decoder'], (n_samples,options['hidden_size']))
else:
p = sliceT(rval[0][1:,:,:],options.get('hidden_depth',1)-1,options['hidden_size'])
n_out_samps = (n_timesteps-1) * n_samples
if options.get('class_out_factoring',0) == 0:
pW = (tensor.dot(p,tparams['Wd']) + tparams['bd']).reshape([n_out_samps,options['output_size']])
pWSft = tensor.nnet.softmax(pW)
totProb = pWSft[tensor.arange(n_out_samps), xW[1:,:].flatten()]
out_list = [pWSft, totProb, p]
else:
ixtoclsinfo_t = tensor.as_tensor_variable(options['ixtoclsinfo'])
xC = ixtoclsinfo_t[xW[1:,:].flatten(),0]
pW = ((tparams['Wd'][:,xC,:].T*(p.reshape([1,n_out_samps,options['hidden_size']]))).sum(axis=-1).T
+ tparams['bd'][:,xC,:])
pWSft = tensor.nnet.softmax(pW[0,:,:])
pC = (tensor.dot(p,tparams['WdCls']) + tparams['bdCls']).reshape([n_out_samps,options['nClasses']])
pCSft = tensor.nnet.softmax(pC)
totProb = pWSft[tensor.arange(n_out_samps), ixtoclsinfo_t[xW[1:,:].flatten(),3]] * \
pCSft[tensor.arange(n_out_samps), xC]
out_list = [pWSft, pCSft, totProb, p]
tot_cost = -(tensor.log(totProb + 1e-10) * mask[1:,:].flatten()).sum()
tot_pplx = -(tensor.log2(totProb + 1e-10) * mask[1:,:].flatten()).sum()
cost = [tot_cost/options['batch_size'], tot_pplx]
inp_list = [xW, mask, xI]
if options.get('en_aux_inp',0):
inp_list.append(xAux)
if options.get('sched_sampling_mode',None) != None:
inp_list.append(curr_epoch)
f_pred_prob = []
#theano.function(inp_list, p, name='f_pred_prob', updates=updatesLSTM)
return use_noise, inp_list, f_pred_prob, cost, out_list , updatesLSTM
def build_eval_other_sent(self, tparams, options,model_npy):
zipp(model_npy, self.model_th)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
xW = tensor.matrix('xW', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
n_timesteps = xW.shape[0]
n_samples = xW.shape[1]
embW = tparams['Wemb'][xW.flatten()].reshape([n_timesteps,
n_samples,
options['word_encoding_size']])
xI = tensor.matrix('xI', dtype=config.floatX)
xAux = tensor.matrix('xAux', dtype=config.floatX)
embImg = (tensor.dot(xI, tparams['WIemb']) + tparams['b_Img']).reshape([1,n_samples,options['image_encoding_size']]);
emb = tensor.concatenate([embImg, embW], axis=0)
rval, updatesLSTM = basic_lstm_layer(tparams, emb[:n_timesteps,:,:], xAux, use_noise, options, prefix=options['generator'])
p = rval[0]
p = tensor.dot(p,tparams['Wd']) + tparams['bd']
#pred = tensor.nnet.softmax(p)
#pred = rval[2]
#pred = pred[1:,:,:]
p = p[1:,:,:]
def accumCost(pred,xW,m,c_sum,ppl_sum):
pred = tensor.nnet.softmax(pred)
c_sum += (tensor.log(pred[tensor.arange(n_samples), xW]+1e-20) * m)
ppl_sum += -(tensor.log2(pred[tensor.arange(n_samples), xW]+1e-10) * m)
return c_sum, ppl_sum
sums, upd = theano.scan(fn=accumCost,
outputs_info=[tensor.alloc(numpy_floatX(0.), 1,n_samples),
tensor.alloc(numpy_floatX(0.), 1,n_samples)],
sequences = [p, xW[1:,:], mask[1:,:]])
# NOTE1: we are leaving out the first prediction, which was made for the image
# and is meaningless. Here cost[0] contains log probability (log10) and cost[1] contains
# perplexity (log2)
cost = sums[0][-1]
inp_list = [xW, mask, xI]
if options.get('en_aux_inp',0):
inp_list.append(xAux)
self.f_pred_prob_other = theano.function(inp_list, p, name='f_pred_prob', updates=updatesLSTM)
#f_pred = theano.function([xW, mask], pred.argmax(axis=1), name='f_pred')
#cost = -tensor.log(pred[tensor.arange(n_timesteps),tensor.arange(n_samples), xW] + 1e-8).mean()
self.f_eval_other = theano.function(inp_list, cost, name='f_eval')
return use_noise, inp_list, self.f_pred_prob_other, cost, p, updatesLSTM
