def _create_decoding_layers(self):
logger.debug("_create_decoding_layers")
self.decode_inputers = [lambda x : 0] * self.num_levels
self.decode_reseters = [lambda x : 0] * self.num_levels
self.decode_updaters = [lambda x : 0] * self.num_levels
self.back_decode_inputers = [lambda x : 0] * self.num_levels
self.back_decode_reseters = [lambda x : 0] * self.num_levels
self.back_decode_updaters = [lambda x : 0] * self.num_levels
decoding_kwargs = dict(self.default_kwargs)
decoding_kwargs.update(dict(
n_in=self.state['c_dim'],
n_hids=self.state['dim'] * self.state['dim_mult'],
activation=['lambda x:x'],
learn_bias=False))
if self.state['decoding_inputs']:
for level in range(self.num_levels):
# Input contributions
self.decode_inputers[level] = MultiLayer(
self.rng,
name='{}_dec_inputter_{}'.format(self.prefix, level),
**decoding_kwargs)
# Update gate contributions
if prefix_lookup(self.state, 'dec', 'rec_gating'):
self.decode_updaters[level] = MultiLayer(
self.rng,
name='{}_dec_updater_{}'.format(self.prefix, level),
**decoding_kwargs)
# Reset gate contributions
if prefix_lookup(self.state, 'dec', 'rec_reseting'):
self.decode_reseters[level] = MultiLayer(
self.rng,
name='{}_dec_reseter_{}'.format(self.prefix, level),
**decoding_kwargs)
def _create_embedding_layers(self):
logger.debug("_create_embedding_layers")
self.approx_embedder = MultiLayer(
#word embedder is a MLP that maps words of n_sym dimensions to a vector of rank_n_approx dimensions
self.rng,
n_in=self.state['n_sym_source']
if self.prefix.find("enc") >= 0
else self.state['n_sym_target'],
n_hids=[self.state['rank_n_approx']],
activation=[self.state['rank_n_activ']],
name='{}_approx_embdr'.format(self.prefix),
**self.default_kwargs)
# We have 3 embeddings for each word in each level,
# input_embedder - the one used as input,
# reset_embedder - the one used to control resetting gate,
# update_embedder - the one used to control update gate.
self.input_embedders = [lambda x : 0] * self.num_levels
self.reset_embedders = [lambda x : 0] * self.num_levels
self.update_embedders = [lambda x : 0] * self.num_levels
embedder_kwargs = dict(self.default_kwargs)
embedder_kwargs.update(dict(
n_in=self.state['rank_n_approx'],
n_hids=[self.state['dim'] * self.state['dim_mult']],
activation=['lambda x:x']))
for level in range(self.num_levels):
self.input_embedders[level] = MultiLayer(
self.rng,
name='{}_input_embdr_{}'.format(self.prefix, level),
**embedder_kwargs)
if prefix_lookup(self.state, self.prefix, 'rec_gating'):
self.update_embedders[level] = MultiLayer(
self.rng,
learn_bias=False,
name='{}_update_embdr_{}'.format(self.prefix, level),
**embedder_kwargs)
if prefix_lookup(self.state, self.prefix, 'rec_reseting'):
self.reset_embedders[level] = MultiLayer(
self.rng,
learn_bias=False,
name='{}_reset_embdr_{}'.format(self.prefix, level),
**embedder_kwargs)
def _create_initialization_layers(self):
logger.debug("_create_initialization_layers")
self.initializers = [ZeroLayer()] * self.num_levels
if self.state['bias_code']:
for level in range(self.num_levels):
self.initializers[level] = MultiLayer(
self.rng,
n_in=self.state['dim'],
n_hids=[self.state['dim'] * self.state['hid_mult']],
activation=[prefix_lookup(self.state, 'dec', 'activ')],
bias_scale=[self.state['bias']],
name='{}_initializer_{}'.format(self.prefix, level),
**self.default_kwargs)
def _create_inter_level_layers(self):
logger.debug("_create_inter_level_layers")
inter_level_kwargs = dict(self.default_kwargs)
inter_level_kwargs.update(
n_in=self.state['dim'],
n_hids=self.state['dim'] * self.state['dim_mult'],
activation=['lambda x:x'])
self.inputers = [0] * self.num_levels
self.reseters = [0] * self.num_levels
self.updaters = [0] * self.num_levels
for level in range(1, self.num_levels):
self.inputers[level] = MultiLayer(self.rng,
name="{}_inputer_{}".format(self.prefix, level),
**inter_level_kwargs)
if prefix_lookup(self.state, self.prefix, 'rec_reseting'):
self.reseters[level] = MultiLayer(self.rng,
name="{}_reseter_{}".format(self.prefix, level),
**inter_level_kwargs)
if prefix_lookup(self.state, self.prefix, 'rec_gating'):
self.updaters[level] = MultiLayer(self.rng,
name="{}_updater_{}".format(self.prefix, level),
**inter_level_kwargs)
def _create_representation_layers(self):
logger.debug("_create_representation_layers")
# If we have a stack of RNN, then their last hidden states
# are combined with a maxout layer.
self.repr_contributors = [None] * self.num_levels
for level in range(self.num_levels):
self.repr_contributors[level] = MultiLayer(
self.rng,
n_in=self.state['dim'],
n_hids=[self.state['dim'] * self.state['maxout_part']],
activation=['lambda x: x'],
name="{}_repr_contrib_{}".format(self.prefix, level),
**self.default_kwargs)
self.repr_calculator = UnaryOp(
activation=eval(self.state['unary_activ']),
name="{}_repr_calc".format(self.prefix))
def jobman(state, channel):
# load dataset
rng = numpy.random.RandomState(state['seed'])
# declare the dimensionalies of the input and output
if state['chunks'] == 'words':
state['n_in'] = 10000
state['n_out'] = 10000
else:
state['n_in'] = 50
state['n_out'] = 50
train_data, valid_data, test_data = get_text_data(state)
## BEGIN Tutorial
### Define Theano Input Variables
x = TT.lvector('x')
y = TT.lvector('y')
h0 = theano.shared(
numpy.zeros((eval(state['nhids'])[-1], ), dtype='float32'))
### Neural Implementation of the Operators: \oplus
#### Word Embedding
emb_words = MultiLayer(rng,
n_in=state['n_in'],
n_hids=eval(state['inp_nhids']),
activation=eval(state['inp_activ']),
init_fn='sample_weights_classic',
weight_noise=state['weight_noise'],
rank_n_approx=state['rank_n_approx'],
scale=state['inp_scale'],
sparsity=state['inp_sparse'],
learn_bias=True,
bias_scale=eval(state['inp_bias']),
name='emb_words')
#### Deep Transition Recurrent Layer
rec = eval(state['rec_layer'])(
rng,
eval(state['nhids']),
activation=eval(state['rec_activ']),
#activation = 'TT.nnet.sigmoid',
bias_scale=eval(state['rec_bias']),
scale=eval(state['rec_scale']),
sparsity=eval(state['rec_sparse']),
init_fn=eval(state['rec_init']),
weight_noise=state['weight_noise'],
name='rec')
#### Stiching them together
##### (1) Get the embedding of a word
x_emb = emb_words(x, no_noise_bias=state['no_noise_bias'])
##### (2) Embedding + Hidden State via DT Recurrent Layer
reset = TT.scalar('reset')
rec_layer = rec(x_emb,
n_steps=x.shape[0],
init_state=h0 * reset,
no_noise_bias=state['no_noise_bias'],
truncate_gradient=state['truncate_gradient'],
batch_size=1)
## BEGIN Exercise: DOT-RNN
### Neural Implementation of the Operators: \lhd
#### Exercise (1)
#### TODO: Define a layer from the hidden state to the intermediate layer
emb_layer = MultiLayer(rng, )
#### Exercise (1)
#### TODO: Define a layer from the input to the intermediate Layer
#### Hidden State: Combine emb_state and emb_words_out
#### Exercise (1)
#### TODO: Define an activation layer
#### Exercise (2)
#### TODO: Define a dropout layer
#### Softmax Layer
output_layer = SoftmaxLayer(rng,
eval(state['dout_nhid']),
state['n_out'],
scale=state['out_scale'],
bias_scale=state['out_bias_scale'],
init_fn="sample_weights_classic",
weight_noise=state['weight_noise'],
sparsity=state['out_sparse'],
sum_over_time=True,
name='out')
### Few Optional Things
#### Direct shortcut from x to y
if state['shortcut_inpout']:
shortcut = MultiLayer(rng,
n_in=state['n_in'],
n_hids=eval(state['inpout_nhids']),
activations=eval(state['inpout_activ']),
init_fn='sample_weights_classic',
weight_noise=state['weight_noise'],
scale=eval(state['inpout_scale']),
sparsity=eval(state['inpout_sparse']),
learn_bias=eval(state['inpout_learn_bias']),
bias_scale=eval(state['inpout_bias']),
name='shortcut')
#### Learning rate scheduling (1/(1+n/beta))
state['clr'] = state['lr']
def update_lr(obj, cost):
stp = obj.step
if isinstance(obj.state['lr_start'],
int) and stp > obj.state['lr_start']:
time = float(stp - obj.state['lr_start'])
new_lr = obj.state['clr'] / (1 + time / obj.state['lr_beta'])
obj.lr = new_lr
if state['lr_adapt']:
rec.add_schedule(update_lr)
### Neural Implementations of the Language Model
#### Training
if state['shortcut_inpout']:
additional_inputs = [rec_layer, shortcut(x)]
else:
additional_inputs = [rec_layer]
##### Exercise (1): Compute the output intermediate layer
##### TODO: Compute the output intermediate layer
##### Exercise (2): Apply Dropout
##### TODO: Apply the dropout layer
train_model = output_layer(outhid,
no_noise_bias=state['no_noise_bias'],
additional_inputs=additional_inputs).train(
target=y,
scale=numpy.float32(1. / state['seqlen']))
nw_h0 = rec_layer.out[rec_layer.out.shape[0] - 1]
if state['carry_h0']:
train_model.updates += [(h0, nw_h0)]
#### Validation
h0val = theano.shared(
numpy.zeros((eval(state['nhids'])[-1], ), dtype='float32'))
rec_layer = rec(emb_words(x, use_noise=False),
n_steps=x.shape[0],
batch_size=1,
init_state=h0val * reset,
use_noise=False)
nw_h0 = rec_layer.out[rec_layer.out.shape[0] - 1]
##### Exercise (1):
##### TODO: Compute the output intermediate layer
##### Exercise (2): Apply Dropout
##### TODO: Apply the dropout layer without noise
if state['shortcut_inpout']:
additional_inputs = [rec_layer, shortcut(x, use_noise=False)]
else:
additional_inputs = [rec_layer]
valid_model = output_layer(outhid,
additional_inputs=additional_inputs,
use_noise=False).validate(target=y,
sum_over_time=True)
valid_updates = []
if state['carry_h0']:
valid_updates = [(h0val, nw_h0)]
valid_fn = theano.function([x, y, reset],
valid_model.cost,
name='valid_fn',
updates=valid_updates)
#### Sampling
##### single-step sampling
def sample_fn(word_tm1, h_tm1):
x_emb = emb_words(word_tm1, use_noise=False, one_step=True)
h0 = rec(x_emb, state_before=h_tm1, one_step=True, use_noise=False)[-1]
outhid = outhid_dropout(outhid_activ(
emb_state(h0, use_noise=False, one_step=True) +
emb_words_out(word_tm1, use_noise=False, one_step=True),
one_step=True),
use_noise=False,
one_step=True)
word = output_layer.get_sample(state_below=outhid,
additional_inputs=[h0],
temp=1.)
return word, h0
##### scan for iterating the single-step sampling multiple times
[samples, summaries], updates = scan(sample_fn,
states=[
TT.alloc(numpy.int64(0),
state['sample_steps']),
TT.alloc(numpy.float32(0), 1,
eval(state['nhids'])[-1])
],
n_steps=state['sample_steps'],
name='sampler_scan')
##### build a Theano function for sampling
sample_fn = theano.function([], [samples],
updates=updates,
profile=False,
name='sample_fn')
##### Load a dictionary
dictionary = numpy.load(state['dictionary'])
if state['chunks'] == 'chars':
dictionary = dictionary['unique_chars']
else:
dictionary = dictionary['unique_words']
def hook_fn():
sample = sample_fn()[0]
print 'Sample:',
if state['chunks'] == 'chars':
print "".join(dictionary[sample])
else:
for si in sample:
print dictionary[si],
print
### Build and Train a Model
#### Define a model
model = LM_Model(cost_layer=train_model,
weight_noise_amount=state['weight_noise_amount'],
valid_fn=valid_fn,
clean_before_noise_fn=False,
noise_fn=None,
rng=rng)
if state['reload']:
model.load(state['prefix'] + 'model.npz')
#### Define a trainer
##### Training algorithm (SGD)
if state['moment'] < 0:
algo = SGD(model, state, train_data)
else:
algo = SGD_m(model, state, train_data)
##### Main loop of the trainer
main = MainLoop(train_data,
valid_data,
test_data,
model,
algo,
state,
channel,
train_cost=False,
hooks=hook_fn,
validate_postprocess=eval(state['validate_postprocess']))
## Run!
main.main()
y = TT.lvector('y')
h0_f = theano.shared(numpy.zeros((eval(state['nhids'])[-1], ),
dtype='float32'))
h0_b = theano.shared(numpy.zeros((eval(state['nhids'])[-1], ),
dtype='float32'))
h0 = theano.shared(numpy.zeros((eval(state['nhids'])[-1], ), dtype='float32'))
### Neural Implementation of the Operators: \oplus
#### Word Embedding
emb_words = MultiLayer(rng,
n_in=state['n_in'],
n_hids=eval(state['inp_nhids']),
activation=eval(state['inp_activ']),
init_fn='sample_weights_classic',
weight_noise=state['weight_noise'],
rank_n_approx=state['rank_n_approx'],
scale=state['inp_scale'],
sparsity=state['inp_sparse'],
learn_bias=True,
bias_scale=eval(state['inp_bias']),
name='emb_words')
#### Deep Transition Recurrent Layer
rec_f = eval(state['rec_layer'])(rng,
eval(state['nhids']),
activation=eval(state['rec_activ']),
bias_scale=eval(state['rec_bias']),
scale=eval(state['rec_scale']),
sparsity=eval(state['rec_sparse']),
init_fn=eval(state['rec_init']),
weight_noise=state['weight_noise'],
def _create_readout_layers(self):
softmax_layer = self.state['softmax_layer'] if 'softmax_layer' in self.state \
else 'SoftmaxLayer'
logger.debug("_create_readout_layers")
readout_kwargs = dict(self.default_kwargs)
readout_kwargs.update(dict(
n_hids=self.state['dim'],
activation='lambda x: x',
))
self.repr_readout = MultiLayer(
self.rng,
n_in=self.state['c_dim'],
learn_bias=False,
name='{}_repr_readout'.format(self.prefix),
**readout_kwargs)
# Attention - this is the only readout layer
# with trainable bias. Should be careful with that.
self.hidden_readouts = [None] * self.num_levels
for level in range(self.num_levels):
self.hidden_readouts[level] = MultiLayer(
self.rng,
n_in=self.state['dim'],
name='{}_hid_readout_{}'.format(self.prefix, level),
**readout_kwargs)
self.prev_word_readout = 0
if self.state['bigram']:
self.prev_word_readout = MultiLayer(
self.rng,
n_in=self.state['rank_n_approx'],
n_hids=self.state['dim'],
activation=['lambda x:x'],
learn_bias=False,
name='{}_prev_readout_{}'.format(self.prefix, level),
**self.default_kwargs)
if self.state['deep_out']:
act_layer = UnaryOp(activation=eval(self.state['unary_activ']))
drop_layer = DropOp(rng=self.rng, dropout=self.state['dropout'])
self.output_nonlinearities = [act_layer, drop_layer]
self.output_layer = eval(softmax_layer)(
self.rng,
self.state['dim'] / self.state['maxout_part'],
self.state['n_sym_target'],
sparsity=-1,
rank_n_approx=self.state['rank_n_approx'],
name='{}_deep_softmax'.format(self.prefix),
sum_over_time=True,#whether sum the cost of the whole sentence
use_nce=self.state['use_nce'] if 'use_nce' in self.state else False,
**self.default_kwargs)
else:
self.output_nonlinearities = []
self.output_layer = eval(softmax_layer)(
self.rng,
self.state['dim'],
self.state['n_sym_target'],
sparsity=-1,
rank_n_approx=self.state['rank_n_approx'],
name='dec_softmax',
sum_over_time=True,#whether sum the cost of the whole sentence
use_nce=self.state['use_nce'] if 'use_nce' in self.state else False,
**self.default_kwargs)
def jobman(state, channel):
# load dataset
state['null_sym_source'] = 15000
state['null_sym_target'] = 15000
state['n_sym_source'] = state['null_sym_source'] + 1
state['n_sym_target'] = state['null_sym_target'] + 1
state['nouts'] = state['n_sym_target']
state['nins'] = state['n_sym_source']
rng = numpy.random.RandomState(state['seed'])
if state['loopIters'] > 0:
train_data, valid_data, test_data = get_data(state)
else:
train_data = None
valid_data = None
test_data = None
########### Training graph #####################
## 1. Inputs
if state['bs'] == 1:
x = TT.lvector('x')
x_mask = TT.vector('x_mask')
y = TT.lvector('y')
y0 = y
y_mask = TT.vector('y_mask')
else:
x = TT.lmatrix('x')
x_mask = TT.matrix('x_mask')
y = TT.lmatrix('y')
y0 = y
y_mask = TT.matrix('y_mask')
# 2. Layers and Operators
bs = state['bs']
embdim = state['dim_mlp']
# Source Sentence
emb = MultiLayer(rng,
n_in=state['nins'],
n_hids=[state['rank_n_approx']],
activation=[state['rank_n_activ']],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb')
emb_words = []
if state['rec_gating']:
gater_words = []
if state['rec_reseting']:
reseter_words = []
for si in xrange(state['encoder_stack']):
emb_words.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb_words_%d' % si))
if state['rec_gating']:
gater_words.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='gater_words_%d' % si))
if state['rec_reseting']:
reseter_words.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='reseter_words_%d' % si))
add_rec_step = []
rec_proj = []
if state['rec_gating']:
rec_proj_gater = []
if state['rec_reseting']:
rec_proj_reseter = []
for si in xrange(state['encoder_stack']):
if si > 0:
rec_proj.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['rec_weight_scale'],
name='rec_proj_%d' % si))
if state['rec_gating']:
rec_proj_gater.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_gater_%d' % si))
if state['rec_reseting']:
rec_proj_reseter.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_reseter_%d' % si))
add_rec_step.append(
eval(state['rec_layer'])(rng,
n_hids=state['dim'],
activation=state['activ'],
bias_scale=state['bias'],
scale=state['rec_weight_scale'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise_rec'],
dropout=state['dropout_rec'],
gating=state['rec_gating'],
gater_activation=state['rec_gater'],
reseting=state['rec_reseting'],
reseter_activation=state['rec_reseter'],
name='add_h_%d' % si))
def _add_op(words_embeddings,
words_mask=None,
prev_val=None,
si=0,
state_below=None,
gater_below=None,
reseter_below=None,
one_step=False,
bs=1,
init_state=None,
use_noise=True):
seqlen = words_embeddings.out.shape[0] // bs
rval = words_embeddings
gater = None
reseter = None
if state['rec_gating']:
gater = gater_below
if state['rec_reseting']:
reseter = reseter_below
if si > 0:
rval += rec_proj[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if state['rec_gating']:
projg = rec_proj_gater[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if gater: gater += projg
else: gater = projg
if state['rec_reseting']:
projg = rec_proj_reseter[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if reseter: reseter += projg
else: reseter = projg
if not one_step:
rval = add_rec_step[si](rval,
nsteps=seqlen,
batch_size=bs,
mask=words_mask,
gater_below=gater,
reseter_below=reseter,
one_step=one_step,
init_state=init_state,
use_noise=use_noise)
else:
rval = add_rec_step[si](rval,
mask=words_mask,
state_before=prev_val,
gater_below=gater,
reseter_below=reseter,
one_step=one_step,
init_state=init_state,
use_noise=use_noise)
return rval
add_op = Operator(_add_op)
# Target Sentence
emb_t = MultiLayer(rng,
n_in=state['nouts'],
n_hids=[state['rank_n_approx']],
activation=[state['rank_n_activ']],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb_t')
emb_words_t = []
if state['rec_gating']:
gater_words_t = []
if state['rec_reseting']:
reseter_words_t = []
for si in xrange(state['decoder_stack']):
emb_words_t.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='emb_words_t_%d' % si))
if state['rec_gating']:
gater_words_t.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='gater_words_t_%d' % si))
if state['rec_reseting']:
reseter_words_t.append(
MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='reseter_words_t_%d' % si))
proj_everything_t = []
if state['rec_gating']:
gater_everything_t = []
if state['rec_reseting']:
reseter_everything_t = []
for si in xrange(state['decoder_stack']):
proj_everything_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='proj_everything_t_%d' % si,
learn_bias=False))
if state['rec_gating']:
gater_everything_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='gater_everything_t_%d' % si,
learn_bias=False))
if state['rec_reseting']:
reseter_everything_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='reseter_everything_t_%d' % si,
learn_bias=False))
add_rec_step_t = []
rec_proj_t = []
if state['rec_gating']:
rec_proj_t_gater = []
if state['rec_reseting']:
rec_proj_t_reseter = []
for si in xrange(state['decoder_stack']):
if si > 0:
rec_proj_t.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[embdim],
activation=['lambda x:x'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['rec_weight_scale'],
name='rec_proj_%d' % si))
if state['rec_gating']:
rec_proj_t_gater.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_t_gater_%d' % si))
if state['rec_reseting']:
rec_proj_t_reseter.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=['lambda x:x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='rec_proj_t_reseter_%d' % si))
add_rec_step_t.append(
eval(state['rec_layer'])(rng,
n_hids=state['dim'],
activation=state['activ'],
bias_scale=state['bias'],
scale=state['rec_weight_scale'],
init_fn=state['rec_weight_init_fn'],
weight_noise=state['weight_noise_rec'],
dropout=state['dropout_rec'],
gating=state['rec_gating'],
gater_activation=state['rec_gater'],
reseting=state['rec_reseting'],
reseter_activation=state['rec_reseter'],
name='add_h_t_%d' % si))
if state['encoder_stack'] > 1:
encoder_proj = []
for si in xrange(state['encoder_stack']):
encoder_proj.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim'] * state['maxout_part']],
activation=['lambda x: x'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
name='encoder_proj_%d' % si,
learn_bias=(si == 0)))
encoder_act_layer = UnaryOp(activation=eval(state['unary_activ']),
indim=indim,
pieces=pieces,
rng=rng)
def _add_t_op(words_embeddings,
everything=None,
words_mask=None,
prev_val=None,
one_step=False,
bs=1,
init_state=None,
use_noise=True,
gater_below=None,
reseter_below=None,
si=0,
state_below=None):
seqlen = words_embeddings.out.shape[0] // bs
rval = words_embeddings
gater = None
if state['rec_gating']:
gater = gater_below
reseter = None
if state['rec_reseting']:
reseter = reseter_below
if si > 0:
if isinstance(state_below, list):
state_below = state_below[-1]
rval += rec_proj_t[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if state['rec_gating']:
projg = rec_proj_t_gater[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if gater: gater += projg
else: gater = projg
if state['rec_reseting']:
projg = rec_proj_t_reseter[si - 1](state_below,
one_step=one_step,
use_noise=use_noise)
if reseter: reseter += projg
else: reseter = projg
if everything:
rval = rval + proj_everything_t[si](everything)
if state['rec_gating']:
everyg = gater_everything_t[si](everything,
one_step=one_step,
use_noise=use_noise)
if gater: gater += everyg
else: gater = everyg
if state['rec_reseting']:
everyg = reseter_everything_t[si](everything,
one_step=one_step,
use_noise=use_noise)
if reseter: reseter += everyg
else: reseter = everyg
if not one_step:
rval = add_rec_step_t[si](rval,
nsteps=seqlen,
batch_size=bs,
mask=words_mask,
one_step=one_step,
init_state=init_state,
gater_below=gater,
reseter_below=reseter,
use_noise=use_noise)
else:
rval = add_rec_step_t[si](rval,
mask=words_mask,
state_before=prev_val,
one_step=one_step,
gater_below=gater,
reseter_below=reseter,
use_noise=use_noise)
return rval
add_t_op = Operator(_add_t_op)
outdim = state['dim_mlp']
if not state['deep_out']:
outdim = state['rank_n_approx']
if state['bias_code']:
bias_code = []
for si in xrange(state['decoder_stack']):
bias_code.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[state['dim']],
activation=[state['activ']],
bias_scale=[state['bias']],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
name='bias_code_%d' % si))
if state['avg_word']:
word_code_nin = state['rank_n_approx']
word_code = MultiLayer(rng,
n_in=word_code_nin,
n_hids=[outdim],
activation='lambda x:x',
bias_scale=[state['bias_mlp'] / 3],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
learn_bias=False,
name='word_code')
proj_code = MultiLayer(rng,
n_in=state['dim'],
n_hids=[outdim],
activation='lambda x: x',
bias_scale=[state['bias_mlp'] / 3],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
learn_bias=False,
name='proj_code')
proj_h = []
for si in xrange(state['decoder_stack']):
proj_h.append(
MultiLayer(rng,
n_in=state['dim'],
n_hids=[outdim],
activation='lambda x: x',
bias_scale=[state['bias_mlp'] / 3],
scale=state['weight_scale'],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
name='proj_h_%d' % si))
if state['bigram']:
proj_word = MultiLayer(rng,
n_in=state['rank_n_approx'],
n_hids=[outdim],
activation=['lambda x:x'],
bias_scale=[state['bias_mlp'] / 3],
init_fn=state['weight_init_fn'],
weight_noise=state['weight_noise'],
scale=state['weight_scale'],
learn_bias=False,
name='emb_words_lm')
if state['deep_out']:
indim = 0
pieces = 0
act_layer = UnaryOp(activation=eval(state['unary_activ']))
drop_layer = DropOp(rng=rng, dropout=state['dropout'])
if state['deep_out']:
indim = state['dim_mlp'] / state['maxout_part']
rank_n_approx = state['rank_n_approx']
rank_n_activ = state['rank_n_activ']
else:
indim = state['rank_n_approx']
rank_n_approx = 0
rank_n_activ = None
output_layer = SoftmaxLayer(rng,
indim,
state['nouts'],
state['weight_scale'],
-1,
rank_n_approx=rank_n_approx,
rank_n_activ=rank_n_activ,
weight_noise=state['weight_noise'],
init_fn=state['weight_init_fn'],
name='out')
def _pop_op(everything,
accum,
everything_max=None,
everything_min=None,
word=None,
aword=None,
one_step=False,
use_noise=True):
rval = proj_h[0](accum[0], one_step=one_step, use_noise=use_noise)
for si in xrange(1, state['decoder_stack']):
rval += proj_h[si](accum[si],
one_step=one_step,
use_noise=use_noise)
if state['mult_out']:
rval = rval * everything
else:
rval = rval + everything
if aword and state['avg_word']:
wcode = aword
if one_step:
if state['mult_out']:
rval = rval * wcode
else:
rval = rval + wcode
else:
if not isinstance(wcode, TT.TensorVariable):
wcode = wcode.out
shape = wcode.shape
rshape = rval.shape
rval = rval.reshape(
[rshape[0] / shape[0], shape[0], rshape[1]])
wcode = wcode.dimshuffle('x', 0, 1)
if state['mult_out']:
rval = rval * wcode
else:
rval = rval + wcode
rval = rval.reshape(rshape)
if word and state['bigram']:
if one_step:
if state['mult_out']:
rval *= proj_word(emb_t(word, use_noise=use_noise),
one_step=one_step,
use_noise=use_noise)
else:
rval += proj_word(emb_t(word, use_noise=use_noise),
one_step=one_step,
use_noise=use_noise)
else:
if isinstance(word, TT.TensorVariable):
shape = word.shape
ndim = word.ndim
else:
shape = word.shape
ndim = word.out.ndim
pword = proj_word(emb_t(word, use_noise=use_noise),
one_step=one_step,
use_noise=use_noise)
shape_pword = pword.shape
if ndim == 1:
pword = Shift()(pword.reshape([shape[0], 1, outdim]))
else:
pword = Shift()(pword.reshape([shape[0], shape[1],
outdim]))
if state['mult_out']:
rval *= pword.reshape(shape_pword)
else:
rval += pword.reshape(shape_pword)
if state['deep_out']:
rval = drop_layer(act_layer(rval), use_noise=use_noise)
return rval
pop_op = Operator(_pop_op)
# 3. Constructing the model
gater_below = None
if state['rec_gating']:
gater_below = gater_words[0](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[0](emb(x))
encoder_acts = [
add_op(emb_words[0](emb(x)),
x_mask,
bs=x_mask.shape[1],
si=0,
gater_below=gater_below,
reseter_below=reseter_below)
]
if state['encoder_stack'] > 1:
everything = encoder_proj[0](last(encoder_acts[-1]))
for si in xrange(1, state['encoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words[si](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[si](emb(x))
encoder_acts.append(
add_op(emb_words[si](emb(x)),
x_mask,
bs=x_mask.shape[1],
si=si,
state_below=encoder_acts[-1],
gater_below=gater_below,
reseter_below=reseter_below))
if state['encoder_stack'] > 1:
everything += encoder_proj[si](last(encoder_acts[-1]))
if state['encoder_stack'] <= 1:
encoder = encoder_acts[-1]
everything = LastState(ntimes=True, n=y.shape[0])(encoder)
else:
everything = encoder_act_layer(everything)
everything = everything.reshape(
[1, everything.shape[0], everything.shape[1]])
everything = LastState(ntimes=True, n=y.shape[0])(everything)
if state['bias_code']:
init_state = [bc(everything[-1]) for bc in bias_code]
else:
init_state = [None for bc in bias_code]
if state['avg_word']:
shape = x.shape
pword = emb(x).out.reshape(
[shape[0], shape[1], state['rank_n_approx']])
pword = pword * x_mask.dimshuffle(0, 1, 'x')
aword = pword.sum(0) / TT.maximum(1., x_mask.sum(0).dimshuffle(0, 'x'))
aword = word_code(aword, use_noise=False)
else:
aword = None
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[0](emb_t(y0))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[0](emb_t(y0))
has_said = [
add_t_op(emb_words_t[0](emb_t(y0)),
everything,
y_mask,
bs=y_mask.shape[1],
gater_below=gater_below,
reseter_below=reseter_below,
init_state=init_state[0],
si=0)
]
for si in xrange(1, state['decoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[si](emb_t(y0))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[si](emb_t(y0))
has_said.append(
add_t_op(emb_words_t[si](emb_t(y0)),
everything,
y_mask,
bs=y_mask.shape[1],
state_below=has_said[-1],
gater_below=gater_below,
reseter_below=reseter_below,
init_state=init_state[si],
si=si))
if has_said[0].out.ndim < 3:
for si in xrange(state['decoder_stack']):
shape_hs = has_said[si].shape
if y0.ndim == 1:
shape = y0.shape
has_said[si] = Shift()(has_said[si].reshape(
[shape[0], 1, state['dim_mlp']]))
else:
shape = y0.shape
has_said[si] = Shift()(has_said[si].reshape(
[shape[0], shape[1], state['dim_mlp']]))
has_said[si].out = TT.set_subtensor(has_said[si].out[0, :, :],
init_state[si])
has_said[si] = has_said[si].reshape(shape_hs)
else:
for si in xrange(state['decoder_stack']):
has_said[si] = Shift()(has_said[si])
has_said[si].out = TT.set_subtensor(has_said[si].out[0, :, :],
init_state[si])
model = pop_op(proj_code(everything), has_said, word=y0, aword=aword)
nll = output_layer.train(
state_below=model, target=y0, mask=y_mask, reg=None) / TT.cast(
y.shape[0] * y.shape[1], 'float32')
valid_fn = None
noise_fn = None
x = TT.lvector(name='x')
n_steps = TT.iscalar('nsteps')
temp = TT.scalar('temp')
gater_below = None
if state['rec_gating']:
gater_below = gater_words[0](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[0](emb(x))
encoder_acts = [
add_op(emb_words[0](emb(x), use_noise=False),
si=0,
use_noise=False,
gater_below=gater_below,
reseter_below=reseter_below)
]
if state['encoder_stack'] > 1:
everything = encoder_proj[0](last(encoder_acts[-1]), use_noise=False)
for si in xrange(1, state['encoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words[si](emb(x))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words[si](emb(x))
encoder_acts.append(
add_op(emb_words[si](emb(x), use_noise=False),
si=si,
state_below=encoder_acts[-1],
use_noise=False,
gater_below=gater_below,
reseter_below=reseter_below))
if state['encoder_stack'] > 1:
everything += encoder_proj[si](last(encoder_acts[-1]),
use_noise=False)
if state['encoder_stack'] <= 1:
encoder = encoder_acts[-1]
everything = last(encoder)
else:
everything = encoder_act_layer(everything)
init_state = []
for si in xrange(state['decoder_stack']):
if state['bias_code']:
init_state.append(
TT.reshape(bias_code[si](everything, use_noise=False),
[1, state['dim']]))
else:
init_state.append(TT.alloc(numpy.float32(0), 1, state['dim']))
if state['avg_word']:
aword = emb(x, use_noise=False).out.mean(0)
aword = word_code(aword, use_noise=False)
else:
aword = None
def sample_fn(*args):
aidx = 0
word_tm1 = args[aidx]
aidx += 1
prob_tm1 = args[aidx]
has_said_tm1 = []
for si in xrange(state['decoder_stack']):
aidx += 1
has_said_tm1.append(args[aidx])
aidx += 1
ctx = args[aidx]
if state['avg_word']:
aidx += 1
awrd = args[aidx]
val = pop_op(proj_code(ctx),
has_said_tm1,
word=word_tm1,
aword=awrd,
one_step=True,
use_noise=False)
sample = output_layer.get_sample(state_below=val, temp=temp)
logp = output_layer.get_cost(state_below=val.out.reshape(
[1, TT.cast(output_layer.n_in, 'int64')]),
temp=temp,
target=sample.reshape([1, 1]),
use_noise=False)
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[0](emb_t(sample))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[0](emb_t(sample))
has_said_t = [
add_t_op(emb_words_t[0](emb_t(sample)),
ctx,
prev_val=has_said_tm1[0],
gater_below=gater_below,
reseter_below=reseter_below,
one_step=True,
use_noise=True,
si=0)
]
for si in xrange(1, state['decoder_stack']):
gater_below = None
if state['rec_gating']:
gater_below = gater_words_t[si](emb_t(sample))
reseter_below = None
if state['rec_reseting']:
reseter_below = reseter_words_t[si](emb_t(sample))
has_said_t.append(
add_t_op(emb_words_t[si](emb_t(sample)),
ctx,
prev_val=has_said_tm1[si],
gater_below=gater_below,
reseter_below=reseter_below,
one_step=True,
use_noise=True,
si=si,
state_below=has_said_t[-1]))
for si in xrange(state['decoder_stack']):
if isinstance(has_said_t[si], list):
has_said_t[si] = has_said_t[si][-1]
rval = [sample, TT.cast(logp, 'float32')] + has_said_t
return rval
sampler_params = [everything]
if state['avg_word']:
sampler_params.append(aword)
states = [TT.alloc(numpy.int64(0), n_steps)]
states.append(TT.alloc(numpy.float32(0), n_steps))
states += init_state
outputs, updates = scan(sample_fn,
states=states,
params=sampler_params,
n_steps=n_steps,
name='sampler_scan')
samples = outputs[0]
probs = outputs[1]
sample_fn = theano.function([n_steps, temp, x],
[samples, probs.sum()],
updates=updates,
profile=False,
name='sample_fn')
model = LM_Model(cost_layer=nll,
weight_noise_amount=state['weight_noise_amount'],
valid_fn=valid_fn,
sample_fn=sample_fn,
clean_before_noise_fn=False,
noise_fn=noise_fn,
indx_word=state['indx_word_target'],
indx_word_src=state['indx_word'],
character_level=False,
rng=rng)
if state['loopIters'] > 0: algo = SGD(model, state, train_data)
else: algo = None
def hook_fn():
if not hasattr(model, 'word_indxs'): model.load_dict()
if not hasattr(model, 'word_indxs_src'):
model.word_indxs_src = model.word_indxs
old_offset = train_data.offset
if state['sample_reset']: train_data.reset()
ns = 0
for sidx in xrange(state['sample_n']):
while True:
batch = train_data.next()
if batch:
break
x = batch['x']
y = batch['y']
#xbow = batch['x_bow']
masks = batch['x_mask']
if x.ndim > 1:
for idx in xrange(x.shape[1]):
ns += 1
if ns > state['sample_max']:
break
print 'Input: ',
for k in xrange(x[:, idx].shape[0]):
print model.word_indxs_src[x[:, idx][k]],
if model.word_indxs_src[x[:, idx][k]] == '<eol>':
break
print ''
print 'Target: ',
for k in xrange(y[:, idx].shape[0]):
print model.word_indxs[y[:, idx][k]],
if model.word_indxs[y[:, idx][k]] == '<eol>':
break
print ''
senlen = len(x[:, idx])
if len(numpy.where(masks[:, idx] == 0)[0]) > 0:
senlen = numpy.where(masks[:, idx] == 0)[0][0]
if senlen < 1:
continue
xx = x[:senlen, idx]
#xx = xx.reshape([xx.shape[0], 1])
model.get_samples(state['seqlen'] + 1, 1, xx)
else:
ns += 1
model.get_samples(state['seqlen'] + 1, 1, x)
if ns > state['sample_max']:
break
train_data.offset = old_offset
return
main = MainLoop(train_data,
valid_data,
None,
model,
algo,
state,
channel,
reset=state['reset'],
hooks=hook_fn)
if state['reload']: main.load()
if state['loopIters'] > 0: main.main()
if state['sampler_test']:
# This is a test script: we only sample
if not hasattr(model, 'word_indxs'): model.load_dict()
if not hasattr(model, 'word_indxs_src'):
model.word_indxs_src = model.word_indxs
indx_word = pkl.load(open(state['word_indx'], 'rb'))
try:
while True:
try:
seqin = raw_input('Input Sequence: ')
n_samples = int(raw_input('How many samples? '))
alpha = float(raw_input('Inverse Temperature? '))
seqin = seqin.lower()
seqin = seqin.split()
seqlen = len(seqin)
seq = numpy.zeros(seqlen + 1, dtype='int64')
for idx, sx in enumerate(seqin):
try:
seq[idx] = indx_word[sx]
except:
seq[idx] = indx_word[state['oov']]
seq[-1] = state['null_sym_source']
except Exception:
print 'Something wrong with your input! Try again!'
continue
sentences = []
all_probs = []
for sidx in xrange(n_samples):
#import ipdb; ipdb.set_trace()
[values, probs] = model.sample_fn(seqlen * 3, alpha, seq)
sen = []
for k in xrange(values.shape[0]):
if model.word_indxs[values[k]] == '<eol>':
break
sen.append(model.word_indxs[values[k]])
sentences.append(" ".join(sen))
all_probs.append(-probs)
sprobs = numpy.argsort(all_probs)
for pidx in sprobs:
print pidx, "(%f):" % (-all_probs[pidx]), sentences[pidx]
print
except KeyboardInterrupt:
print 'Interrupted'
pass
seq_len= state['seqlen'],
mode="test",
chunks=state['chunks'],
shift = state['shift'],
output_format = out_format_valid,
can_fit=True)
if 'wiki' in state['path']:
test_data = None
return train_data, valid_data, test_data
def create_embedding_layers(rng, state)
# create embedding layers for 4 gates
# to approximate the embeddings at rank n
# first create an embedder from n_in to rank_n_approx
approx_emb = MultiLayer(
rng)
# activation should be x : x
# because based on GroundHog's design,
# the actual activation is handled by LSTM layer all together
input_embs = [lambda x : 0] * state['stack_number']
update_embs = [lambda x : 0] * state['stack_number']
forget_embs = [lambda x : 0] * state['stack_number']
output_embs = [lambda x : 0] * state['stack_number']
default_kwargs = {
n_in : state['n_in'],
n_hids : eval(state['emb_nhids']),
activation : eval(state['emb_activ']),