def __init__(self, dim, **kwargs):
super(FeedbackRNN, self).__init__(**kwargs)
self.dim = dim
self.first_recurrent_layer = SimpleRecurrent(
dim=self.dim,
activation=Identity(),
name='first_recurrent_layer',
weights_init=initialization.Identity())
self.second_recurrent_layer = SimpleRecurrent(
dim=self.dim,
activation=Identity(),
name='second_recurrent_layer',
weights_init=initialization.Identity())
self.children = [
self.first_recurrent_layer, self.second_recurrent_layer
]
def example():
""" Simple reccurent example. Taken from : https://github.com/mdda/pycon.sg-2015_deep-learning/blob/master/ipynb/blocks-recurrent-docs.ipynb """
x = tensor.tensor3('x')
rnn = SimpleRecurrent(dim=3,
activation=Identity(),
weights_init=initialization.Identity())
rnn.initialize()
h = rnn.apply(x)
f = theano.function([x], h)
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX)))
doubler = Linear(input_dim=3,
output_dim=3,
weights_init=initialization.Identity(2),
biases_init=initialization.Constant(0))
doubler.initialize()
h_doubler = rnn.apply(doubler.apply(x))
f = theano.function([x], h_doubler)
print(f(np.ones((3, 1, 3), dtype=theano.config.floatX)))
#Initial State
h0 = tensor.matrix('h0')
h = rnn.apply(inputs=x, states=h0)
f = theano.function([x, h0], h)
print(
f(np.ones((3, 1, 3), dtype=theano.config.floatX),
np.ones((1, 3), dtype=theano.config.floatX)))
def __init__(self, dim, depth, **kwargs):
super(FeedbackRNNStack, self).__init__(**kwargs)
self.dim = dim
self.depth = depth
self.children = []
FeedbackRNNStack.depth = depth
for i in range(depth):
self.children.append(
SimpleRecurrent(dim=self.dim,
activation=Identity(),
name=str(i) + 'th_recurrent_layer',
weights_init=initialization.Identity()))
def setUp(self):
prototype = SimpleRecurrent(dim=3, activation=Tanh())
self.layers = [
Bidirectional(weights_init=Orthogonal(), prototype=prototype)
for _ in range(3)
]
self.stack = RecurrentStack(self.layers)
for fork in self.stack.forks:
fork.weights_init = Identity(1)
fork.biases_init = Constant(0)
self.stack.initialize()
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))), dtype=theano.config.floatX)
self.x_val = (numpy.ones(
(24, 4, 3), dtype=theano.config.floatX) * self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
self.mask_val[12:24, 3] = 0
class CustomLSTMWeights(NdarrayInitialization):
# Identity in the diagonal and IsotropicGaussian everywhere else
def __init__(self, std=1, mean=0):
self.gaussian_init = IsotropicGaussian(std = std, mean = mean)
self.identity = Identity()
def generate(self, rng, shape):
if len(shape) != 2:
raise ValueError
assert shape[0] == shape[1]
size = shape/4
assert size*4 == shape[0]
result = numpy.array([])
for i in range(4):
row = numpy.array([])
for j in range(4):
if i == j:
square = self.gaussian_init.generate(rng, (size,size))
else:
square = self.identity.generate(rng, (size,size))
row = numpy.hstack(row,square)
result.vstack(row)
return result.astype(theano.config.floatX)
class CustomLSTMWeights(NdarrayInitialization):
# Identity in the diagonal and IsotropicGaussian everywhere else
def __init__(self, std=1, mean=0):
self.gaussian_init = IsotropicGaussian(std=std, mean=mean)
self.identity = Identity()
def generate(self, rng, shape):
if len(shape) != 2:
raise ValueError
assert shape[0] == shape[1]
size = shape / 4
assert size * 4 == shape[0]
result = numpy.array([])
for i in range(4):
row = numpy.array([])
for j in range(4):
if i == j:
square = self.gaussian_init.generate(rng, (size, size))
else:
square = self.identity.generate(rng, (size, size))
row = numpy.hstack(row, square)
result.vstack(row)
return result.astype(theano.config.floatX)
def __init__(self, std=1, mean=0):
self.gaussian_init = IsotropicGaussian(std = std, mean = mean)
self.identity = Identity()
def main(config, tr_stream, dev_stream, use_bokeh=False, the_task=None, the_track=None):
config['the_task'] = the_task
# Create Theano variables
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
sampling_input = tensor.lmatrix('input')
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(
# end_embed is dimension of word embedding matrix in encoder; enc_nhids number of hidden units in encoder GRU
config['src_vocab_size'], config['enc_embed'], config['enc_nhids'])
decoder = Decoder(
config['trg_vocab_size'], config['dec_embed'], config['dec_nhids'],
config['enc_nhids'] * 2, config['use_attention'], cost_type=config['error_fct'])
cost = decoder.cost(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask)
testVar = decoder.getTestVar(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# Initialize model
logger.info('Initializing model')
my_rng = numpy.random.RandomState(config['rng_value'])
if config['identity_init']:
encoder.weights_init = decoder.weights_init = Identity()
else:
encoder.weights_init = decoder.weights_init = IsotropicGaussian(
config['weight_scale'])
encoder.rng = decoder.rng = my_rng
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
encoder.bidir.prototype.rng = my_rng
decoder.transition.weights_init = Orthogonal()
decoder.transition.rng = my_rng
encoder.initialize()
decoder.initialize()
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logger.info('Applying dropout')
dropout_inputs = [x for x in cg.intermediary_variables
if x.name == 'maxout_apply_output']
cg = apply_dropout(cg, dropout_inputs, config['dropout'])
# Apply weight noise for regularization
if config['weight_noise_ff'] > 0.0:
logger.info('Applying weight noise to ff layers')
enc_params = Selector(encoder.lookup).get_params().values()
enc_params += Selector(encoder.fwd_fork).get_params().values()
enc_params += Selector(encoder.back_fork).get_params().values()
dec_params = Selector(
decoder.sequence_generator.readout).get_params().values()
dec_params += Selector(
decoder.sequence_generator.fork).get_params().values()
dec_params += Selector(decoder.state_init).get_params().values()
cg = apply_noise(cg, enc_params+dec_params, config['weight_noise_ff'], seed=my_rng)
cost = cg.outputs[0]
# Print shapes
shapes = [param.get_value().shape for param in cg.parameters]
logger.info("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logger.info(' {:15}: {}'.format(shape, count))
logger.info("Total number of parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logger.info("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logger.info(' {:15}: {}'.format(value.get_value().shape, name))
logger.info("Total number of parameters: {}"
.format(len(enc_dec_param_dict)))
# Set up training model
logger.info("Building model")
training_model = Model(cost)
# Set extensions
logger.info("Initializing extensions")
# this is ugly code and done, because I am not sure if the order of the extensions is important
if 'track2' in config['saveto']: # less epochs for track 2, because of more data
if config['early_stopping']:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']/2),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
else:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']/2),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
else:
if config['early_stopping']:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
else:
extensions = [
FinishAfter(after_n_epochs=config['finish_after']),
#FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
Printing(after_batch=True),
CheckpointNMT(config['saveto'],
every_n_batches=config['save_freq'])
]
# Set up beam search and sampling computation graphs if necessary
if config['hook_samples'] >= 1:
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(sampling_input, sampling_representation)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
# Add sampling
if config['hook_samples'] >= 1:
logger.info("Building sampler")
extensions.append(
Sampler(model=search_model, data_stream=tr_stream,
hook_samples=config['hook_samples'],
#every_n_batches=1,
every_n_batches=config['sampling_freq'],
src_vocab_size=8))
#src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
if config['val_set'] is not None:
logger.info("Building accuracy validator")
extensions.append(
AccuracyValidator(sampling_input, samples=samples, config=config,
model=search_model, data_stream=dev_stream,
after_training=True,
#after_epoch=True))
every_n_epochs=5))
else:
logger.info("No validation set given for this language")
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(config['step_clipping']),
eval(config['step_rule'])()])
)
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(
model=training_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions
)
# Train!
main_loop.run()
softmax_out = softmax.apply(pre_softmax.reshape((-1, o_dim)))
softmax_out = softmax_out.reshape(shape)
softmax_out.name = 'softmax_out'
# comparing only last time-step
cost = CategoricalCrossEntropy().apply(y[-1, :, 0], softmax_out[-1])
cost.name = 'CrossEntropy'
error_rate = MisclassificationRate().apply(y[-1, :, 0], softmax_out[-1])
error_rate.name = 'error_rate'
# Initialization
for brick in (x_to_h1, h1_to_o):
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0)
brick.initialize()
rnn.weights_init = Identity()
rnn.biases_init = Constant(0)
rnn.initialize()
print 'Bulding training process...'
algorithm = GradientDescent(cost=cost,
parameters=ComputationGraph(cost).parameters,
step_rule=learning_algorithm(
learning_rate=1e-6,
momentum=0.0,
clipping_threshold=1.0,
algorithm='adam'))
train_stream, valid_stream = MNIST(batch_size=batch_size)
monitor_train_cost = TrainingDataMonitoring([cost, error_rate],
def test_identity():
assert str(Identity(2.0)).endswith(' mult=2.0>')
print "EVALled a thing"
res = out.shape.eval({x: np.ones((6, 9, 19), dtype=floatX)})
print res
### Identity testing
from blocks.initialization import Identity, IsotropicGaussian
from blocks import bricks
from blocks.bricks import Sigmoid
dim = 2
floatX = theano.config.floatX
x = tensor.tensor3('input')
gru = GatedRecurrentFull(
hidden_dim=dim,
state_to_state_init=Identity(1.),
#state_to_reset_init=Identity(1.),
state_to_reset_init=IsotropicGaussian(0.2),
state_to_update_init=Identity(1.0),
activation=bricks.Identity(1.0),
gate_activation=Sigmoid(),
input_to_state_transform=Linear(
input_dim=dim,
output_dim=dim,
weights_init=Identity(1.0),
#weights_init=IsotropicGaussian(0.02),
biases_init=Constant(0.0)),
input_to_update_transform=Linear(
input_dim=dim,
output_dim=dim,
#weights_init=Constant(0.0),
def initialize(self):
self.model.initialize()
Identity().initialize(self.rnn.W_state, self.rnn.rng)
def testing_init(brick):
brick.weights_init = Identity()
brick.biases_init = Constant(0)
brick.initialize()
def __init__(self, std=1, mean=0):
self.gaussian_init = IsotropicGaussian(std=std, mean=mean)
self.identity = Identity()
def set_up(self, config=None, make_prunable=False):
"""Loads and initializes all the theano variables for the
training model and the decoding model.
Args:
config (dict): NMT configuration
"""
if config:
self.config = config
else:
config = self.config
# Create Theano variables
logging.debug('Creating theano variables')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence_mask = tensor.matrix('target_mask')
# Construct model (fs439: Add NoLookup options)
if config['dec_layers'] != 1:
logging.fatal("Only dec_layers=1 supported.")
logging.debug('Building RNN encoder-decoder')
if config['src_sparse_feat_map']:
if config['enc_layers'] != 1:
logging.fatal("Only enc_layers=1 supported for sparse "
"source features.")
source_sentence = tensor.tensor3('source')
self.sampling_input = tensor.tensor3('input')
encoder = NoLookupEncoder(config['enc_embed'], config['enc_nhids'])
else:
source_sentence = tensor.lmatrix('source')
self.sampling_input = tensor.lmatrix('input')
if config['enc_layers'] > 1 and not config['enc_share_weights']:
encoder = DeepBidirectionalEncoder(
config['src_vocab_size'], config['enc_embed'],
config['enc_layers'], config['enc_skip_connections'],
config['enc_nhids'])
else:
encoder = BidirectionalEncoder(config['src_vocab_size'],
config['enc_embed'],
config['enc_layers'],
config['enc_skip_connections'],
config['enc_nhids'])
if config['trg_sparse_feat_map']:
target_sentence = tensor.tensor3('target')
decoder = NoLookupDecoder(
config['trg_vocab_size'], config['dec_embed'],
config['dec_nhids'], config['att_nhids'],
config['maxout_nhids'], config['enc_nhids'] * 2,
config['attention'], config['dec_attention_sources'],
config['dec_readout_sources'], config['memory'],
config['memory_size'], config['seq_len'], config['dec_init'])
else:
target_sentence = tensor.lmatrix('target')
decoder = Decoder(config['trg_vocab_size'],
config['dec_embed'],
config['dec_nhids'],
config['att_nhids'],
config['maxout_nhids'],
config['enc_nhids'] * 2,
config['attention'],
config['dec_attention_sources'],
config['dec_readout_sources'],
config['memory'],
config['memory_size'],
config['seq_len'],
config['dec_init'],
make_prunable=make_prunable)
if config['annotations'] != 'direct':
annotators = []
add_direct = False
for name in config['annotations'].split(','):
if name == 'direct':
add_direct = True
elif name == 'hierarchical':
annotators.append(HierarchicalAnnotator(encoder))
else:
logging.fatal("Annotation strategy %s unknown" % name)
encoder = EncoderWithAnnotators(encoder, annotators, add_direct)
annotations, annotations_mask = encoder.apply(source_sentence,
source_sentence_mask)
self.cost = decoder.cost(annotations, annotations_mask,
target_sentence, target_sentence_mask)
logging.info('Creating computational graph')
self.cg = ComputationGraph(self.cost)
# Initialize model
logging.info('Initializing model')
encoder.weights_init = decoder.weights_init = Identity()
# encoder.weights_init = decoder.weights_init = IsotropicGaussian(
# config['weight_scale'])
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
try:
# encoder.bidir.prototype.weights_init = Orthogonal()
encoder.bidir.prototype.weights_init = Identity()
except AttributeError:
pass # Its fine, no bidirectional encoder
decoder.transition.weights_init = Identity()
# decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
logging.info('Applying dropout')
dropout_inputs = [
x for x in self.cg.intermediary_variables
if x.name == 'maxout_apply_output'
]
self.cg = apply_dropout(self.cg, dropout_inputs, config['dropout'])
# Apply weight noise for regularization
if config['weight_noise_ff'] > 0.0:
logging.info('Applying weight noise to ff layers')
if encoder.lookup:
enc_params = Selector(encoder.lookup).get_parameters().values()
enc_params += Selector(encoder.fwd_fork).get_parameters().values()
enc_params += Selector(encoder.back_fork).get_parameters().values()
dec_params = Selector(
decoder.sequence_generator.readout).get_parameters().values()
dec_params += Selector(
decoder.sequence_generator.fork).get_parameters().values()
self.cg = apply_noise(self.cg, enc_params + dec_params,
config['weight_noise_ff'])
# Print shapes
shapes = [param.get_value().shape for param in self.cg.parameters]
logging.debug("Parameter shapes: ")
for shape, count in Counter(shapes).most_common():
logging.debug(' {:15}: {}'.format(shape, count))
logging.debug("Total number of CG parameters: {}".format(len(shapes)))
# Print parameter names
enc_dec_param_dict = merge(
Selector(encoder).get_parameters(),
Selector(decoder).get_parameters())
logging.debug("Parameter names: ")
for name, value in enc_dec_param_dict.items():
logging.debug(' {:15}: {}'.format(value.get_value().shape,
name))
logging.info("Total number of parameters: {}".format(
len(enc_dec_param_dict)))
# Set up training model
logging.info("Building model")
self.training_model = Model(self.cost)
logging.info("Building sampling model")
src_shape = (self.sampling_input.shape[-2],
self.sampling_input.shape[-1]) # batch_size x sen_length
sampling_representation, _ = encoder.apply(self.sampling_input,
tensor.ones(src_shape))
generated = decoder.generate(src_shape, sampling_representation)
self.search_model = Model(generated)
generated_outputs = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
self.samples = generated_outputs[1]
self.encoder = encoder
self.decoder = decoder
def main_run(_config, _log):
from collections import namedtuple
c = namedtuple("Config", _config.keys())(*_config.values())
_log.info("Running with" + str(_config))
import theano
from theano import tensor as T
import numpy as np
from dataset import IMDBText, GloveTransformer
from blocks.initialization import Uniform, Constant, IsotropicGaussian, NdarrayInitialization, Identity, Orthogonal
from blocks.bricks.recurrent import LSTM, SimpleRecurrent, GatedRecurrent
from blocks.bricks.parallel import Fork
from blocks.bricks import Linear, Sigmoid, Tanh, Rectifier
from blocks import bricks
from blocks.extensions import Printing, Timing
from blocks.extensions.monitoring import (DataStreamMonitoring,
TrainingDataMonitoring)
from blocks.extensions.plot import Plot
from plot import PlotHistogram
from blocks.algorithms import GradientDescent, Adam, Scale, StepClipping, CompositeRule, AdaDelta
from blocks.graph import ComputationGraph, apply_dropout
from blocks.main_loop import MainLoop
from blocks.model import Model
from cuboid.algorithms import AdaM, NAG
from cuboid.extensions import EpochProgress
from fuel.streams import DataStream, ServerDataStream
from fuel.transformers import Padding
from fuel.schemes import ShuffledScheme
from Conv1D import Conv1D, MaxPooling1D
from schemes import BatchwiseShuffledScheme
from bricks import WeightedSigmoid, GatedRecurrentFull
from multiprocessing import Process
import fuel
import logging
from initialization import SumInitialization
from transformers import DropSources
global train_p
global test_p
x = T.tensor3('features')
#m = T.matrix('features_mask')
y = T.imatrix('targets')
#x = x+m.mean()*0
dropout_variables = []
embedding_size = 300
glove_version = "glove.6B.300d.txt"
#embedding_size = 50
#glove_version = "vectors.6B.50d.txt"
gloveMapping = Linear(
input_dim=embedding_size,
output_dim=c.rnn_input_dim,
weights_init=Orthogonal(),
#weights_init = IsotropicGaussian(c.wstd),
biases_init=Constant(0.0),
name="gloveMapping")
gloveMapping.initialize()
o = gloveMapping.apply(x)
o = Rectifier(name="gloveRec").apply(o)
dropout_variables.append(o)
summed_mapped_glove = o.sum(axis=1) # take out the sequence
glove_out = Linear(input_dim=c.rnn_input_dim,
output_dim=1.0,
weights_init=IsotropicGaussian(c.wstd),
biases_init=Constant(0.0),
name="mapping_to_output")
glove_out.initialize()
deeply_sup_0 = glove_out.apply(summed_mapped_glove)
deeply_sup_probs = Sigmoid(name="deeply_sup_softmax").apply(deeply_sup_0)
input_dim = c.rnn_input_dim
hidden_dim = c.rnn_dim
gru = GatedRecurrentFull(
hidden_dim=hidden_dim,
activation=Tanh(),
#activation=bricks.Identity(),
gate_activation=Sigmoid(),
state_to_state_init=SumInitialization(
[Identity(1.0), IsotropicGaussian(c.wstd)]),
state_to_reset_init=IsotropicGaussian(c.wstd),
state_to_update_init=IsotropicGaussian(c.wstd),
input_to_state_transform=Linear(input_dim=input_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(c.wstd),
biases_init=Constant(0.0)),
input_to_update_transform=Linear(
input_dim=input_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(c.wstd),
#biases_init=Constant(-2.0)),
biases_init=Constant(-1.0)),
input_to_reset_transform=Linear(
input_dim=input_dim,
output_dim=hidden_dim,
weights_init=IsotropicGaussian(c.wstd),
#biases_init=Constant(-3.0))
biases_init=Constant(-2.0)))
gru.initialize()
rnn_in = o.dimshuffle(1, 0, 2)
#rnn_in = o
#rnn_out = gru.apply(rnn_in, mask=m.T)
rnn_out = gru.apply(rnn_in)
state_to_state = gru.rnn.state_to_state
state_to_state.name = "state_to_state"
#o = rnn_out[-1, :, :]
o = rnn_out[-1]
#o = rnn_out[:, -1, :]
#o = rnn_out.mean(axis=1)
#print rnn_last_out.eval({
#x: np.ones((3, 101, 300), dtype=theano.config.floatX),
#m: np.ones((3, 101), dtype=theano.config.floatX)})
#raw_input()
#o = rnn_out.mean(axis=1)
dropout_variables.append(o)
score_layer = Linear(input_dim=hidden_dim,
output_dim=1,
weights_init=IsotropicGaussian(std=c.wstd),
biases_init=Constant(0.),
name="linear2")
score_layer.initialize()
o = score_layer.apply(o)
probs = Sigmoid().apply(o)
#probs = deeply_sup_probs
cost = -(y * T.log(probs) + (1 - y) * T.log(1 - probs)).mean()
#cost_deeply_sup0 = - (y * T.log(deeply_sup_probs) + (1-y) * T.log(1 - deeply_sup_probs)).mean()
# cost += cost_deeply_sup0 * c.deeply_factor
cost.name = 'cost'
misclassification = (y * (probs < 0.5) + (1 - y) * (probs > 0.5)).mean()
misclassification.name = 'misclassification'
#print rnn_in.shape.eval(
#{x : np.ones((45, 111, embedding_size), dtype=theano.config.floatX),
#})
#print rnn_out.shape.eval(
#{x : np.ones((45, 111, embedding_size), dtype=theano.config.floatX),
#m : np.ones((45, 111), dtype=theano.config.floatX)})
#print (m).sum(axis=1).shape.eval({
#m : np.ones((45, 111), dtype=theano.config.floatX)})
#print (m).shape.eval({
#m : np.ones((45, 111), dtype=theano.config.floatX)})
#raw_input()
# =================
cg = ComputationGraph([cost])
cg = apply_dropout(cg, variables=dropout_variables, drop_prob=0.5)
params = cg.parameters
algorithm = GradientDescent(
cost=cg.outputs[0],
params=params,
step_rule=CompositeRule([
StepClipping(threshold=4),
Adam(learning_rate=0.002, beta1=0.1, beta2=0.001),
#NAG(lr=0.1, momentum=0.9),
#AdaDelta(),
]))
# ========
print "setting up data"
ports = {
'gpu0_train': 5557,
'gpu0_test': 5558,
'cuda0_train': 5557,
'cuda0_test': 5558,
'opencl0:0_train': 5557,
'opencl0:0_test': 5558,
'gpu1_train': 5559,
'gpu1_test': 5560,
}
#batch_size = 16
#batch_size = 32
batch_size = 40
def start_server(port, which_set):
fuel.server.logger.setLevel('WARN')
dataset = IMDBText(which_set, sorted=True)
n_train = dataset.num_examples
#scheme = ShuffledScheme(examples=n_train, batch_size=batch_size)
scheme = BatchwiseShuffledScheme(examples=n_train,
batch_size=batch_size)
stream = DataStream(dataset=dataset, iteration_scheme=scheme)
print "loading glove"
glove = GloveTransformer(glove_version, data_stream=stream)
padded = Padding(
data_stream=glove,
#mask_sources=('features',)
mask_sources=('features', ))
padded = DropSources(padded, ['features_mask'])
fuel.server.start_server(padded, port=port, hwm=20)
train_port = ports[theano.config.device + '_train']
train_p = Process(target=start_server, args=(train_port, 'train'))
train_p.start()
test_port = ports[theano.config.device + '_test']
test_p = Process(target=start_server, args=(test_port, 'test'))
test_p.start()
#train_stream = ServerDataStream(('features', 'features_mask', 'targets'), port=train_port)
#test_stream = ServerDataStream(('features', 'features_mask', 'targets'), port=test_port)
train_stream = ServerDataStream(('features', 'targets'), port=train_port)
test_stream = ServerDataStream(('features', 'targets'), port=test_port)
print "setting up model"
#ipdb.set_trace()
n_examples = 25000
print "Batches per epoch", n_examples // (batch_size + 1)
batches_extensions = 100
monitor_rate = 50
#======
model = Model(cg.outputs[0])
extensions = []
extensions.append(
EpochProgress(batch_per_epoch=n_examples // batch_size + 1))
extensions.append(
TrainingDataMonitoring(
[cost, misclassification],
prefix='train',
every_n_batches=monitor_rate,
))
extensions.append(
DataStreamMonitoring([cost, misclassification],
data_stream=test_stream,
prefix='test',
after_epoch=True,
before_first_epoch=False))
extensions.append(Timing())
extensions.append(Printing())
#extensions.append(Plot("norms", channels=[['train_lstm_norm', 'train_pre_norm']], after_epoch=True))
#extensions.append(Plot(theano.config.device+"_result", channels=[['test_misclassification', 'train_misclassification']], after_epoch=True))
#extensions.append(PlotHistogram(
#channels=['train_state_to_state'],
#bins=50,
#every_n_batches=30))
extensions.append(
Plot(theano.config.device + "_result",
channels=[['train_cost'], ['train_misclassification']],
every_n_batches=monitor_rate))
main_loop = MainLoop(model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
