def nn_fprop(x, y, recurrent_in_size, out_size, hidden_size,
num_recurrent_layers, train_flag):
if task_ID_type == 'feedforward':
x, recurrent_in_size = task_ID_layers(x, recurrent_in_size)
recurrent_input = x
cells = []
h = []
if dropout > 0:
recurrent_input = Dropout(name='dropout_recurrent_in',
train_flag=train_flag).apply(recurrent_input)
if linear_before_recurrent_size > 0:
linear = Linear(input_dim=2,
output_dim=linear_before_recurrent_size,
name='linear_befor_recurrent')
initialize([linear])
recurrent_input = linear.apply(recurrent_input[:, :, -2:])
recurrent_in_size = linear_before_recurrent_size
if single_dim_out:
recurrent_input = T.extra_ops.repeat(recurrent_input, out_size, axis=0)
p_components_size = components_size
for i in range(num_recurrent_layers):
model = layer_models[i]
h, cells = add_layer(model,
i,
recurrent_in_size,
hidden_size,
recurrent_input,
h,
cells,
train_flag,
first_layer=True if i == 0 else False)
return output_layer(recurrent_input, h, y, recurrent_in_size, out_size,
hidden_size, p_components_size) + (cells, )
def build_trainer(self, src, src_mask, trg, trg_mask, ite,
l1_reg_weight=1e-6,
l2_reg_weight=1e-6,
softmax_output_num_sampled=100000):
src_mask_3d = K.expand_dims(src_mask)
trg_mask_3d = K.expand_dims(trg_mask)
annotations = self.encoder.apply(src, src_mask_3d)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = K.sum(annotations * src_mask_3d, axis=0) / K.sum(src_mask_3d, axis=0)
trg_emb = self.table_trg.apply(trg)
# shift_right assumes a 3D tensor, and time steps is dimension one
trg_emb_shifted = K.permute_dimensions(K.shift_right(K.permute_dimensions(trg_emb, [1, 0, 2])),
[1, 0, 2])
hiddens, readout, _ = self.decoder.run_pipeline(state_below=trg_emb_shifted,
mask_below=trg_mask_3d,
init_context=init_context,
c=annotations,
c_mask=src_mask_3d)
# apply dropout
if self.dropout > 0.:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(readout, self.dropout)
self.cost = calc_loss_from_readout(readout=readout,
targets=trg,
targets_mask=trg_mask_3d,
logisticRegressionLayer=self.logistic_layer,
softmax_output_num_sampled=softmax_output_num_sampled)
# for reconstruction
self.L1 = sum([K.sum(K.abs(param)) for param in self.params])
self.L2 = sum([K.sum(K.square(param)) for param in self.params])
params_regular = self.L1 * l1_reg_weight + self.L2 * l2_reg_weight
# train cost
train_cost = self.cost + params_regular
# gradients
grads = K.gradients(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
inps = [src, src_mask, trg, trg_mask]
self.train_fn = K.function(inps, [train_cost], updates=updates, name='train_func')
def add_layer(model,
i,
in_size,
h_size,
x,
h,
cells,
train_flag,
first_layer=False):
if dropout > 0 and not first_layer:
h[i - 1] = Dropout(name='dropout__recurrent_hidden{}'.format(i),
train_flag=train_flag).apply(h[i - 1])
print h_size
if model == 'rnn' or model == 'mt_rnn':
h.append(rnn_layer(in_size, h_size, x, h, i, first_layer))
if model == 'lstm' or model == 'mt_lstm':
state, cell = lstm_layer(in_size, h_size, x, h, i, first_layer)
h.append(state)
cells.append(cell)
if model == 'feedforward':
h.append(linear_layer(in_size, h_size, x, h, i, first_layer))
return h, cells
def build_sampler(self):
# added by Longyue
x_hist = T.ltensor3()
x_hist_mask = T.tensor3()
annotations_1 = self.encoder_hist_1.apply_1(x_hist, x_hist_mask)
annotations_1 = annotations_1[-1]
annotations_2 = self.encoder_hist_2.apply_2(annotations_1)
annotations_3 = annotations_2[-1]
x = T.lmatrix()
# Build Networks
# src_mask is None
c = self.encoder.apply(x, None, annotations_3)
#init_context = ctx[0, :, -self.n_hids_src:]
# mean pooling
init_context = c.mean(0)
# added by Longyue
init_context = concatenate([init_context, annotations_3],
axis=annotations_3.ndim - 1)
init_state = self.decoder.create_init_state(init_context)
outs = [init_state, c, annotations_3]
if not self.with_attention:
outs.append(init_context)
# compile function
print 'Building compile_init_state_and_context function ...'
self.compile_init_and_context = theano.function(
[x, x_hist, x_hist_mask], outs, name='compile_init_and_context')
print 'Done'
y = T.lvector()
cur_state = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
trg_emb = T.switch(y[:, None] < 0, T.alloc(0., 1, self.n_in_trg),
self.table_trg.apply(y))
# added by Zhaopeng Tu, 2016-06-09
# for with_attention=False
if self.with_attention and self.with_coverage:
cov_before = T.tensor3()
if self.coverage_type is 'linguistic':
print 'Building compile_fertility ...'
fertility = self.decoder._get_fertility(c)
fertility = T.addbroadcast(fertility, 1)
self.compile_fertility = theano.function(
[c], [fertility], name='compile_fertility')
print 'Done'
else:
fertility = None
else:
cov_before = None
fertility = None
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
# [next_state, ctxs] = self.decoder.apply(state_below=trg_emb,
results = self.decoder.apply(
state_below=trg_emb,
init_state=cur_state,
# added by Zhaopeng Tu, 2016-06-09
init_context=None if self.with_attention else init_context,
c=c if self.with_attention else None,
hist=annotations_3, # added by Longyue
one_step=True,
# added by Zhaopeng Tu, 2016-04-27
cov_before=cov_before,
fertility=fertility)
next_state = results[0]
if self.with_attention:
ctxs, alignment = results[1], results[2]
if self.with_coverage:
cov = results[3]
else:
# if with_attention=False, we always use init_context as the source representation
ctxs = init_context
readout = self.decoder.readout(next_state, ctxs, trg_emb)
# maxout
if self.maxout_part > 1:
readout = self.decoder.one_step_maxout(readout)
# apply dropout
if self.dropout < 1.0:
readout = Dropout(self.trng, readout, 0, self.dropout)
# compute the softmax probability
next_probs = self.logistic_layer.get_probs(readout)
# sample from softmax distribution to get the sample
next_sample = self.trng.multinomial(pvals=next_probs).argmax(1)
# compile function
print 'Building compile_next_state_and_probs function ...'
inps = [y, cur_state]
if self.with_attention:
inps.append(c)
else:
inps.append(init_context)
# added by Longyue
inps.append(annotations_3)
outs = [next_probs, next_state, next_sample]
# added by Zhaopeng Tu, 2016-06-09
if self.with_attention:
outs.append(alignment)
# added by Zhaopeng Tu, 2016-04-29
if self.with_coverage:
inps.append(cov_before)
if self.coverage_type is 'linguistic':
inps.append(fertility)
outs.append(cov)
self.compile_next_state_and_probs = theano.function(
inps, outs, name='compile_next_state_and_probs')
print 'Done'
# added by Zhaopeng Tu, 2016-07-18
# for reconstruction
if self.with_reconstruction:
# Build Networks
# trg_mask is None
inverse_c = T.tensor3()
# mean pooling
inverse_init_context = inverse_c.mean(0)
inverse_init_state = self.inverse_decoder.create_init_state(
inverse_init_context)
outs = [inverse_init_state]
if not self.with_attention:
outs.append(inverse_init_context)
# compile function
print 'Building compile_inverse_init_state_and_context function ...'
self.compile_inverse_init_and_context = theano.function(
[inverse_c], outs, name='compile_inverse_init_and_context')
print 'Done'
src = T.lvector()
inverse_cur_state = T.matrix()
trg_mask = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
src_emb = T.switch(src[:, None] < 0, T.alloc(0., 1, self.n_in_src),
self.table_src.apply(src))
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
inverse_results = self.inverse_decoder.apply(
state_below=src_emb,
init_state=inverse_cur_state,
# added by Zhaopeng Tu, 2016-06-09
init_context=None
if self.with_attention else inverse_init_context,
c=inverse_c if self.with_attention else None,
c_mask=trg_mask,
one_step=True)
inverse_next_state = inverse_results[0]
if self.with_attention:
inverse_ctxs, inverse_alignment = inverse_results[
1], inverse_results[2]
else:
# if with_attention=False, we always use init_context as the source representation
inverse_ctxs = init_context
inverse_readout = self.inverse_decoder.readout(
inverse_next_state, inverse_ctxs, src_emb)
# maxout
if self.maxout_part > 1:
inverse_readout = self.inverse_decoder.one_step_maxout(
inverse_readout)
# apply dropout
if self.dropout < 1.0:
inverse_readout = Dropout(self.srng, inverse_readout, 0,
self.dropout)
# compute the softmax probability
inverse_next_probs = self.inverse_logistic_layer.get_probs(
inverse_readout)
# sample from softmax distribution to get the sample
inverse_next_sample = self.srng.multinomial(
pvals=inverse_next_probs).argmax(1)
# compile function
print 'Building compile_inverse_next_state_and_probs function ...'
inps = [src, trg_mask, inverse_cur_state]
if self.with_attention:
inps.append(inverse_c)
else:
inps.append(inverse_init_context)
outs = [
inverse_next_probs, inverse_next_state, inverse_next_sample
]
# added by Zhaopeng Tu, 2016-06-09
if self.with_attention:
outs.append(inverse_alignment)
self.compile_inverse_next_state_and_probs = theano.function(
inps, outs, name='compile_inverse_next_state_and_probs')
print 'Done'
def build_trainer(self, src, src_mask, src_hist, src_hist_mask, trg,
trg_mask, ite):
# added by Longyue
# checked by Zhaopeng: sentence dim = n_steps, hist_len, batch_size (4, 3, 25)
# hist = (bath_size, sent_num, sent_len) --.T-->
# hist = (sent_len, sent_num, bath_size) --lookup table-->
# (sent_len, sent_num, bath_size, word_emb) --reshape-->
# (sent_len, sent_num*bath_size, word_emb) --word-level rnn-->
# (sent_len, sent_num*bath_size, hidden_size) --reshape-->
# (sent_len, sent_num, bath_size, hidden_size) --[-1]-->
# (sent_num, bath_size, hidden_size) --sent-level rnn-->
# (sent_num, bath_size, hidden_size) --[-1]-->
# (bath_size, hidden_size) = cross-sent context vector
annotations_1 = self.encoder_hist_1.apply_1(src_hist, src_hist_mask)
annotations_1 = annotations_1[-1] # get last hidden states
annotations_2 = self.encoder_hist_2.apply_2(annotations_1)
annotations_3 = annotations_2[-1] # get last hidden states
#modified by Longyue
annotations = self.encoder.apply(src, src_mask, annotations_3)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
#added by Longyue
init_context = concatenate([init_context, annotations_3],
axis=annotations_3.ndim - 1)
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
# modified by Longyue
hiddens, readout, alignment = self.decoder.run_pipeline(
state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask,
hist=annotations_3)
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
# modified by Longyue
inps = [src, src_mask, src_hist, src_hist_mask, trg, trg_mask]
self.train_fn = theano.function(inps, [train_cost],
updates=updates,
name='train_function')
def build_sampler(self):
# time steps, nb_samples
x = K.placeholder((None, None), dtype='int32')
c = self.encoder.apply(x, None) # None,None,None
init_context = K.mean(c, axis=0) # None,None
init_state = self.decoder.create_init_state(init_context)
outs = [init_state, c]
if not self.with_attention:
outs.append(init_context)
# compile function
logger.info('Building compile_init_state_and_context function ...')
self.compile_init_and_context = K.function([x], outs)
logger.info('Done')
if self.with_attention:
c = K.placeholder((None, None, None))
init_context = K.mean(c, axis=0)
else:
init_context = K.placeholder((None, None))
# nb_samples
y = K.placeholder((None,), dtype='int32')
# nb_samples, state_dim
cur_state = K.placeholder((None, None))
# if it is the first word, emb should be all zero, and it is indicated by -1
trg_emb = lookup_table(self.table_trg.W, y, name='trg_emb')
if self.with_attention and self.with_coverage:
cov_before = K.placeholder(shape=(None, None, None))
if self.coverage_type is 'linguistic':
logger.info('Building compile_fertility ...')
fertility = self.decoder._get_fertility(c)
self.compile_fertility = K.function([c], [fertility])
logger.info('Done')
else:
fertility = None
else:
cov_before = None
fertility = None
# apply one step
results = self.decoder.apply(state_below=trg_emb,
init_state=cur_state,
init_context=None if self.with_attention else init_context,
c=c if self.with_attention else None,
one_step=True,
cov_before=cov_before,
fertility=fertility)
next_state = results[0]
if self.with_attention:
ctxs, alignment = results[1], results[2]
if self.with_coverage:
cov = results[3]
else:
# if with_attention=False, we always use init_context as the source representation
ctxs = init_context
readout = self.decoder.readout(next_state, ctxs, trg_emb)
# maxout
if self.maxout_part > 1:
readout = self.decoder.one_step_maxout(readout)
# compute the softmax probability
next_probs = get_probs_from_logits(self.logistic_layer.get_logits(readout))
# sample from softmax distribution to get the sample
# TODO: batch_size* nb_classes
next_sample = K.argmax(K.random_multinomial(pvals=next_probs))
# compile function
logger.info('Building compile_next_state_and_probs function ...')
inps = [y, cur_state]
if self.with_attention:
inps.append(c)
else:
inps.append(init_context)
outs = [next_probs, next_state, next_sample]
if self.with_attention:
outs.append(alignment)
if self.with_coverage:
inps.append(cov_before)
outs.append(cov)
self.compile_next_state_and_probs = K.function(inps, outs)
logger.info('Done')
# for reconstruction
if self.with_reconstruction:
if self.with_attention:
# time steps, nb_samples, context_dim
inverse_c = K.placeholder((None, None, None))
# mean pooling
inverse_init_context = K.mean(inverse_c, axis=0)
else:
inverse_init_context = K.placeholder((None, None))
inverse_init_state = self.inverse_decoder.create_init_state(inverse_init_context)
outs = [inverse_init_state]
if not self.with_attention:
outs.append(inverse_init_context)
# compile function
logger.info('Building compile_inverse_init_state_and_context function ...')
self.compile_inverse_init_and_context = K.function([inverse_c], outs)
logger.info('Done')
# nb_samples
src = K.placeholder(shape=(None,), dtype='int32')
# nb_samples, state_dim
inverse_cur_state = K.placeholder(shape=(None, None))
# time_steps, nb_samples
trg_mask = K.placeholder(shape=(None, None))
# to 3D mask
trg_mask_3d = K.expand_dims(trg_mask)
# if it is the first word, emb should be all zero, and it is indicated by -1
src_emb = lookup_table(self.table_src.W, src, name='src_emb')
# apply one step
inverse_results = self.inverse_decoder.apply(state_below=src_emb,
init_state=inverse_cur_state,
init_context=None if self.with_attention else inverse_init_context,
c=inverse_c if self.with_attention else None,
c_mask=trg_mask_3d,
one_step=True)
inverse_next_state = inverse_results[0]
if self.with_attention:
inverse_ctxs, inverse_alignment = inverse_results[1], inverse_results[2]
else:
# if with_attention=False, we always use init_context as the source representation
inverse_ctxs = init_context
inverse_readout = self.inverse_decoder.readout(inverse_next_state, inverse_ctxs, src_emb)
# maxout
if self.maxout_part > 1:
inverse_readout = self.inverse_decoder.one_step_maxout(inverse_readout)
# apply dropout
if self.dropout > 0.:
inverse_readout = Dropout(inverse_readout, self.dropout)
# compute the softmax probability
inverse_next_probs = get_probs_from_logits(self.inverse_logistic_layer.get_logits(inverse_readout))
# sample from softmax distribution to get the sample
inverse_next_sample = K.argmax(K.random_multinomial(pvals=inverse_next_probs))
# compile function
logger.info('Building compile_inverse_next_state_and_probs function ...')
inps = [src, trg_mask, inverse_cur_state]
if self.with_attention:
inps.append(inverse_c)
else:
inps.append(inverse_init_context)
outs = [inverse_next_probs, inverse_next_state, inverse_next_sample]
if self.with_attention:
outs.append(inverse_alignment)
self.compile_inverse_next_state_and_probs = K.function(inps, outs)
logger.info('Done')
def calc_loss_with_model_parallel(self, src, src_mask_3d, trg, trg_mask_3d, ps_device, devices, l1_reg_weight=1e-6,
l2_reg_weight=1e-6):
assert K._BACKEND == 'tensorflow'
with tf.device(devices[0]):
annotations = self.encoder.apply(src, src_mask_3d)
init_context = K.sum(annotations * src_mask_3d, axis=0) / K.sum(src_mask_3d, axis=0)
trg_emb = self.table_trg.apply(trg)
# shift_right assumes a 3D tensor, and time steps is dimension one
trg_emb_shifted = K.permute_dimensions(K.shift_right(K.permute_dimensions(trg_emb, [1, 0, 2])),
[1, 0, 2])
hiddens, readout, alignment = self.decoder.run_pipeline(
state_below=trg_emb_shifted,
mask_below=trg_mask_3d,
init_context=init_context,
c=annotations,
c_mask=src_mask_3d)
if self.dropout > 0.:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(readout, self.dropout)
logits = self.logistic_layer.get_logits_with_multiple_devices(readout, ps_device, devices)
with tf.device(devices[0]):
logits_flat = K.reshape(logits, shape=(-1, self.logistic_layer.n_out))
cost = get_category_cross_entropy_from_flat_logits(logits_flat, trg, trg_mask_3d)
if self.with_reconstruction:
with tf.device(devices[0]):
inverse_init_context = K.sum(hiddens * trg_mask_3d, axis=0) / K.sum(trg_mask_3d, axis=0)
src_emb = self.table_src.apply(src)
src_emb_shifted = K.permute_dimensions(K.shift_right(K.permute_dimensions(
src_emb, [1, 0, 2])), [1, 0, 2])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask_3d,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask_3d)
with tf.device(devices[0]):
if self.dropout > 0.:
inverse_readout = Dropout(inverse_readout, self.dropout)
inverse_logits = self.inverse_logistic_layer.get_logits_with_multiple_devices(inverse_readout, ps_device,
devices)
with tf.device(devices[0]):
inverse_logits_flat = K.reshape(inverse_logits, shape=(-1, self.inverse_logistic_layer.n_out))
reconstruction_cost = get_category_cross_entropy_from_flat_logits(inverse_logits_flat, src, src_mask_3d)
with tf.device(devices[0]):
cost += reconstruction_cost * self.reconstruction_weight
L1 = sum([K.sum(K.abs(param)) for param in self.params])
L2 = sum([K.sum(K.square(param)) for param in self.params])
params_regular = L1 * l1_reg_weight + L2 * l2_reg_weight
cost += params_regular
return cost
def calc_loss(self, src, src_mask_3d, trg, trg_mask_3d,
l1_reg_weight=1e-6,
l2_reg_weight=1e-6,
softmax_output_num_sampled=100000):
annotations = self.encoder.apply(src, src_mask_3d)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = K.sum(annotations * src_mask_3d, axis=0) / K.sum(src_mask_3d, axis=0)
trg_emb = self.table_trg.apply(trg)
# shift_right assumes a 3D tensor, and time steps is dimension one
trg_emb_shifted = K.permute_dimensions(K.shift_right(K.permute_dimensions(trg_emb, [1, 0, 2])),
[1, 0, 2])
hiddens, readout, alignment = self.decoder.run_pipeline(state_below=trg_emb_shifted,
mask_below=trg_mask_3d,
init_context=init_context,
c=annotations,
c_mask=src_mask_3d)
# apply dropout
if self.dropout > 0.:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(readout, self.dropout)
cost = calc_loss_from_readout(readout=readout,
targets=trg,
targets_mask=trg_mask_3d,
logisticRegressionLayer=self.logistic_layer,
softmax_output_num_sampled=softmax_output_num_sampled)
if self.with_reconstruction:
inverse_init_context = K.sum(hiddens * trg_mask_3d, axis=0) / K.sum(trg_mask_3d, axis=0)
src_emb = self.table_src.apply(src)
src_emb_shifted = K.permute_dimensions(K.shift_right(K.permute_dimensions(src_emb, [1, 0, 2])),
[1, 0, 2])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask_3d,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask_3d)
if self.dropout > 0.:
inverse_readout = Dropout(inverse_readout, self.dropout)
inverse_logits = self.inverse_logistic_layer.get_logits(inverse_readout)
inverse_logits_flat = K.reshape(inverse_logits, shape=(-1, self.inverse_logistic_layer.n_out))
reconstruction_cost = get_category_cross_entropy_from_flat_logits(inverse_logits_flat, src, src_mask_3d)
cost += reconstruction_cost * self.reconstruction_weight
L1 = sum([K.sum(K.abs(param)) for param in self.params])
L2 = sum([K.sum(K.square(param)) for param in self.params])
params_regular = L1 * l1_reg_weight + L2 * l2_reg_weight
cost += params_regular
return cost
def build_trainer(self, src, src_mask, trg, trg_mask, ite):
annotations = self.encoder.apply(src, src_mask)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
hiddens, readout, alignment = self.decoder.run_pipeline(
state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask)
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
inps = [src, src_mask, trg, trg_mask]
self.train_fn = theano.function(inps, [train_cost],
updates=updates,
name='train_function')
def build_sampler(self):
x = T.lmatrix()
# Build Networks
# src_mask is None
c = self.encoder.apply(x, None)
#init_context = ctx[0, :, -self.n_hids_src:]
# mean pooling
init_context = c.mean(0)
init_state = self.decoder.create_init_state(init_context)
# compile function
print 'Building compile_init_state_and_context function ...'
self.compile_init_and_context = theano.function(
[x], [init_state, c], name='compile_init_and_context')
print 'Done'
y = T.lvector()
cur_state = T.matrix()
# if it is the first word, emb should be a1l zero, and it is indicated by -1
trg_emb = T.switch(y[:, None] < 0, T.alloc(0., 1, self.n_in_trg),
self.table_trg.apply(y))
# added by Zhaopeng Tu, 2016-06-09
if self.with_coverage:
cov_before = T.tensor3()
if self.coverage_type is 'linguistic':
print 'Building compile_fertility ...'
fertility = self.decoder._get_fertility(c)
fertility = T.addbroadcast(fertility, 1)
self.compile_fertility = theano.function(
[c], [fertility], name='compile_fertility')
print 'Done'
else:
fertility = None
else:
cov_before = None
fertility = None
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
results = self.decoder.apply(
state_below=trg_emb,
init_state=cur_state,
c=c,
one_step=True,
# added by Zhaopeng Tu, 2016-04-27
cov_before=cov_before,
fertility=fertility)
next_state, ctxs, alignment = results[:3]
idx = 3
if self.with_coverage:
cov = results[idx]
idx += 1
readout = self.decoder.readout(next_state, ctxs, trg_emb)
# maxout
if self.maxout_part > 1:
readout = self.decoder.one_step_maxout(readout)
# apply dropout
if self.dropout < 1.0:
readout = Dropout(self.trng, readout, 0, self.dropout)
# compute the softmax probability
next_probs = self.logistic_layer.get_probs(readout)
# sample from softmax distribution to get the sample
next_sample = self.trng.multinomial(pvals=next_probs).argmax(1)
# compile function
print 'Building compile_next_state_and_probs function ...'
inps = [y, cur_state, c]
outs = [next_probs, next_state, next_sample, alignment]
# added by Zhaopeng Tu, 2016-04-29
if self.with_coverage:
inps.append(cov_before)
if self.coverage_type is 'linguistic':
inps.append(fertility)
outs.append(cov)
# mode=theano.Mode(linker='vm') for ifelse
# Unless linker='vm' or linker='cvm' are used, ifelse will compute both variables and take the same computation time as switch.
self.compile_next_state_and_probs = theano.function(
inps,
outs,
name='compile_next_state_and_probs',
mode=theano.Mode(linker='vm'))
print 'Done'
# added by Zhaopeng Tu, 2016-07-18
# for reconstruction
if self.with_reconstruction:
# Build Networks
# trg_mask is None
inverse_c = T.tensor3()
# mean pooling
inverse_init_context = inverse_c.mean(0)
inverse_init_state = self.inverse_decoder.create_init_state(
inverse_init_context)
outs = [inverse_init_state]
# compile function
print 'Building compile_inverse_init_state_and_context function ...'
self.compile_inverse_init_and_context = theano.function(
[inverse_c], outs, name='compile_inverse_init_and_context')
print 'Done'
src = T.lvector()
inverse_cur_state = T.matrix()
trg_mask = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
src_emb = T.switch(src[:, None] < 0, T.alloc(0., 1, self.n_in_src),
self.table_src.apply(src))
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
inverse_results = self.inverse_decoder.apply(
state_below=src_emb,
init_state=inverse_cur_state,
c=inverse_c,
c_mask=trg_mask,
one_step=True)
inverse_next_state, inverse_ctxs, inverse_alignment = inverse_results[:
3]
inverse_readout = self.inverse_decoder.readout(
inverse_next_state, inverse_ctxs, src_emb)
# maxout
if self.maxout_part > 1:
inverse_readout = self.inverse_decoder.one_step_maxout(
inverse_readout)
# apply dropout
if self.dropout < 1.0:
inverse_readout = Dropout(self.srng, inverse_readout, 0,
self.dropout)
# compute the softmax probability
inverse_next_probs, inverse_next_energy = self.inverse_logistic_layer.get_probs(
inverse_readout)
# sample from softmax distribution to get the sample
inverse_next_sample = self.srng.multinomial(
pvals=inverse_next_probs).argmax(1)
# compile function
print 'Building compile_inverse_next_state_and_probs function ...'
inps = [src, trg_mask, inverse_cur_state, inverse_c]
outs = [
inverse_next_probs, inverse_next_state, inverse_next_sample,
inverse_alignment
]
self.compile_inverse_next_state_and_probs = theano.function(
inps, outs, name='compile_inverse_next_state_and_probs')
print 'Done'
def build_trainer(self, src, src_mask, trg, trg_mask):
annotations = self.encoder.apply(src, src_mask)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
results = self.decoder.run_pipeline(state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask)
hiddens, ctxs, readout, alignment = results[:4]
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_results = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
inverse_hiddens, inverse_ctxs, inverse_readout, inverse_alignment = inverse_results[:
4]
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# train function
inps = [src, src_mask, trg, trg_mask]
outs = [train_cost]
if self.with_layernorm:
inps = [src, src_mask, trg, trg_mask]
lr = T.scalar(name='lr')
print 'Building optimizers...',
self.train_fn, self.update_fn = adam(lr, self.params, grads, inps,
outs)
else:
# updates
updates = adadelta(self.params, grads)
# mode=theano.Mode(linker='vm') for ifelse
# Unless linker='vm' or linker='cvm' are used, ifelse will compute both variables and take the same computation time as switch.
self.train_fn = theano.function(inps,
outs,
updates=updates,
name='train_function',
mode=theano.Mode(linker='vm'))
