def test_model_handles_brickless_parameteres():
x = tensor.matrix('x')
v = shared_floatx(numpy.zeros((10, 10)), name='V')
add_role(v, PARAMETER)
y = x.dot(v)
model = Model(y)
assert list(model.get_parameter_dict().items()) == [('V', v)]
def evaluate(model, load_path):
with open(load_path + '/trained_params_best.npz') as f:
loaded = np.load(f)
blocks_model = Model(model.cost)
params_dicts = blocks_model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
# '/f_6_.W' --> 'f_6_.W'
slash_index = param_name.find('/')
param_name = param_name[slash_index + 1:]
assert param.get_value().shape == loaded[param_name].shape
param.set_value(loaded[param_name])
train_data_stream, valid_data_stream = get_cmv_v2_streams(100)
# T x B x F
data = train_data_stream.get_epoch_iterator().next()
cg = ComputationGraph(model.cost)
f = theano.function(cg.inputs, [model.location, model.scale],
on_unused_input='ignore',
allow_input_downcast=True)
res = f(data[1], data[0])
for i in range(10):
visualize_attention(data[0][:, i, :],
res[0][:, i, :], res[1][:, i, :], prefix=str(i))
def test_sampling():
# Create Theano variables
sampling_input = theano.tensor.lmatrix("input")
# Construct model
encoder = BidirectionalEncoder(vocab_size=10, embedding_dim=5, state_dim=8)
decoder = Decoder(vocab_size=12, embedding_dim=6, state_dim=8, representation_dim=16, theano_seed=1234)
sampling_representation = encoder.apply(sampling_input, theano.tensor.ones(sampling_input.shape))
generateds = decoder.generate(sampling_input, sampling_representation)
model = Model(generateds[1])
# Initialize model
encoder.weights_init = decoder.weights_init = IsotropicGaussian(0.01)
encoder.biases_init = decoder.biases_init = Constant(0)
encoder.push_initialization_config()
decoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
# Compile a function for the generated
sampling_fn = model.get_theano_function()
# Create literal variables
numpy.random.seed(1234)
x = numpy.random.randint(0, 10, size=(1, 2))
# Call function and check result
generated_step = sampling_fn(x)
assert len(generated_step[0].flatten()) == 4
def create_act_table(self, save_to, act_table):
batch_size = 500
image_size = (28, 28)
output_size = 10
convnet = create_lenet_5()
layers = convnet.layers
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
probs = convnet.apply(x)
cg = ComputationGraph([probs])
def full_brick_name(brick):
return '/'.join([''] + [b.name for b in brick.get_unique_path()])
# Find layer outputs to probe
outmap = OrderedDict((full_brick_name(get_brick(out)), out)
for out in VariableFilter(
roles=[OUTPUT], bricks=[Convolutional, Linear])(
cg.variables))
# Generate pics for biases
biases = VariableFilter(roles=[BIAS])(cg.parameters)
# Generate parallel array, in the same order, for outputs
outs = [outmap[full_brick_name(get_brick(b))] for b in biases]
# Figure work count
error_rate = (MisclassificationRate().apply(y.flatten(), probs)
.copy(name='error_rate'))
max_activation_table = (MaxActivationTable().apply(
outs).copy(name='max_activation_table'))
max_activation_table.tag.aggregation_scheme = (
Concatenate(max_activation_table))
model = Model([
error_rate,
max_activation_table])
# Load it with trained parameters
params = load_parameters(open(save_to, 'rb'))
model.set_parameter_values(params)
mnist_test_stream = DataStream.default_stream(
self.mnist_test,
iteration_scheme=SequentialScheme(
self.mnist_test.num_examples, batch_size))
evaluator = DatasetEvaluator([
error_rate,
max_activation_table
])
results = evaluator.evaluate(mnist_test_stream)
table = results['max_activation_table']
pickle.dump(table, open(act_table, 'wb'))
return table
def evaluate(model, load_path):
with open(load_path + '/trained_params_best.npz') as f:
loaded = np.load(f)
blocks_model = Model(model)
params_dicts = blocks_model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
assert param.get_value().shape == loaded[param_name].shape
param.set_value(loaded[param_name])
def main():
import configurations
from stream import DStream
logger = logging.getLogger(__name__)
cfig = getattr(configurations, 'get_config_penn')()
rnnlm = Rnnlm(cfig['vocabsize'], cfig['nemb'], cfig['nhids'])
rnnlm.weights_init = IsotropicGaussian(0.1)
rnnlm.biases_init = Constant(0.)
rnnlm.push_initialization_config()
rnnlm.generator.transition.weights_init = Orthogonal()
sentence = tensor.lmatrix('sentence')
sentence_mask = tensor.matrix('sentence_mask')
batch_cost = rnnlm.cost(sentence, sentence_mask).sum()
batch_size = sentence.shape[1].copy(name='batch_size')
cost = aggregation.mean(batch_cost, batch_size)
cost.name = "sequence_log_likelihood"
logger.info("Cost graph is built")
model = Model(cost)
parameters = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in parameters.items()],
width=120))
for brick in model.get_top_bricks():
brick.initialize()
cg = ComputationGraph(cost)
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(10.0), Scale(0.01)]))
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
monitored_vars = [cost, gradient_norm, step_norm]
train_monitor = TrainingDataMonitoring(variables=monitored_vars, after_batch=True,
before_first_epoch=True, prefix='tra')
extensions = [train_monitor, Timing(), Printing(after_batch=True),
FinishAfter(after_n_epochs=1000),
Printing(every_n_batches=1)]
train_stream = DStream(datatype='train', config=cfig)
main_loop = MainLoop(model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
def fine_tuning(cost, args):
param_values = load_parameter_values(args.fine_tuning)
param_values[
"/output_layer.W"] = np.concatenate((
param_values["/output_layer.W"],
0.1 * np.random.randn(args.state_dim, 40).astype(np.float32)))
model = Model(cost)
model.set_parameter_values(param_values)
return cost
def testing(self, fea2obj):
config = self._config
dsdir = config['dsdir']
devfile = dsdir + '/dev.txt'
testfile = dsdir + '/test.txt'
networkfile = config['net']
batch_size = 10000#int(config['batchsize'])
devMentions = load_ent_ds(devfile)
tstMentions = load_ent_ds(testfile)
logger.info('#dev: %d #test: %d', len(devMentions), len(tstMentions))
main_loop = load(networkfile + '.best.pkl')
logger.info('Model loaded. Building prediction function...')
old_model = main_loop.model
logger.info(old_model.inputs)
sources = [inp.name for inp in old_model.inputs]
# fea2obj = build_input_objs(sources, config)
t2idx = fea2obj['targets'].t2idx
deterministic = str_to_bool(config['use_mean_pred']) if 'use_mean_pred' in config else True
kl_weight = shared_floatx(0.001, 'kl_weight')
entropy_weight= shared_floatx(0.001, 'entropy_weight')
cost, _, y_hat, _, _,_,_ = build_model_new(fea2obj, len(t2idx), self._config, kl_weight, entropy_weight, deterministic=deterministic, test=True)
model = Model(cost)
model.set_parameter_values(old_model.get_parameter_values())
theinputs = []
for fe in fea2obj.keys():
if 'targets' in fe:
continue
for inp in model.inputs:
if inp.name == fe:
theinputs.append(inp)
# theinputs = [inp for inp in model.inputs if inp.name != 'targets']
print "theinputs: ", theinputs
predict = theano.function(theinputs, y_hat)
test_stream, num_samples_test = get_comb_stream(fea2obj, 'test', batch_size, shuffle=False)
dev_stream, num_samples_dev = get_comb_stream(fea2obj, 'dev', batch_size, shuffle=False)
logger.info('sources: %s -- number of test/dev samples: %d/%d', test_stream.sources, num_samples_test, num_samples_dev)
idx2type = {idx:t for t,idx in t2idx.iteritems()}
logger.info('Starting to apply on dev inputs...')
self.applypredict(theinputs, predict, dev_stream, devMentions, num_samples_dev, batch_size, os.path.join(config['exp_dir'], config['matrixdev']), idx2type)
logger.info('...apply on dev data finished')
logger.info('Starting to apply on test inputs...')
self.applypredict(theinputs, predict, test_stream, tstMentions, num_samples_test, batch_size, os.path.join(config['exp_dir'], config['matrixtest']), idx2type)
logger.info('...apply on test data finished')
def __init__(self, model_name, model, stream, **kwargs):
super(RunOnTest, self).__init__(**kwargs)
self.model_name = model_name
cg = Model(model.predict(**stream.inputs()))
self.inputs = cg.inputs
self.outputs = model.predict.outputs
req_vars_test = model.predict.inputs + ['trip_id']
self.test_stream = stream.test(req_vars_test)
self.function = cg.get_theano_function()
def evaluate(model, load_path, configs):
with open(load_path + "trained_params_best.npz") as f:
loaded = np.load(f)
blocks_model = Model(model.cost)
params_dicts = blocks_model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
# '/f_6_.W' --> 'f_6_.W'
slash_index = param_name.find("/")
param_name = param_name[slash_index + 1 :]
assert param.get_value().shape == loaded[param_name].shape
param.set_value(loaded[param_name])
inps = ComputationGraph(model.error_rate).inputs
eval_function = theano.function(inps, [model.error_rate, model.probabilities])
_, vds = configs["get_streams"](100)
data = vds.get_epoch_iterator().next()
print "Valid_ER: " + str(eval_function(data[0], data[2], data[1])[0])
return eval_function
def train_model(cost, train_stream, valid_stream, valid_freq, valid_rare,
load_location=None, save_location=None):
cost.name = 'nll'
perplexity = 2 ** (cost / tensor.log(2))
perplexity.name = 'ppl'
# Define the model
model = Model(cost)
# Load the parameters from a dumped model
if load_location is not None:
logger.info('Loading parameters...')
model.set_param_values(load_parameter_values(load_location))
cg = ComputationGraph(cost)
algorithm = GradientDescent(cost=cost, step_rule=Scale(learning_rate=0.01),
params=cg.parameters)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=[
DataStreamMonitoring([cost, perplexity], valid_stream,
prefix='valid_all', every_n_batches=5000),
# Overfitting of rare words occurs between 3000 and 4000 iterations
DataStreamMonitoring([cost, perplexity], valid_rare,
prefix='valid_rare', every_n_batches=500),
DataStreamMonitoring([cost, perplexity], valid_freq,
prefix='valid_frequent',
every_n_batches=5000),
Printing(every_n_batches=500)
]
)
main_loop.run()
# Save the main loop
if save_location is not None:
logger.info('Saving the main loop...')
dump_manager = MainLoopDumpManager(save_location)
dump_manager.dump(main_loop)
logger.info('Saved')
def train_model(cost, error_rate, train_stream,
load_location=None, save_location=None):
cost.name = "Cross_entropy"
error_rate.name = 'Error_rate'
# Define the model
model = Model(cost)
# Load the parameters from a dumped model
if load_location is not None:
logger.info('Loading parameters...')
model.set_param_values(load_parameter_values(load_location))
cg = ComputationGraph(cost)
step_rule = Momentum(learning_rate=0.1, momentum=0.9)
algorithm = GradientDescent(cost=cost, step_rule=step_rule,
params=cg.parameters)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=[
# DataStreamMonitoring([cost], test_stream, prefix='test',
# after_epoch=False, every_n_epochs=10),
DataStreamMonitoring([cost], train_stream, prefix='train',
after_epoch=True),
Printing(after_epoch=True)
]
)
main_loop.run()
# Save the main loop
if save_location is not None:
logger.info('Saving the main loop...')
dump_manager = MainLoopDumpManager(save_location)
dump_manager.dump(main_loop)
logger.info('Saved')
def test_model():
x = tensor.matrix('x')
mlp1 = MLP([Tanh(), Tanh()], [10, 20, 30], name="mlp1")
mlp2 = MLP([Tanh()], [30, 40], name="mlp2")
h1 = mlp1.apply(x)
h2 = mlp2.apply(h1)
model = Model(h2)
assert model.get_top_bricks() == [mlp1, mlp2]
# The order of parameters returned is deterministic but
# not sensible.
assert list(model.get_parameter_dict().items()) == [
('/mlp2/linear_0.b', mlp2.linear_transformations[0].b),
('/mlp1/linear_1.b', mlp1.linear_transformations[1].b),
('/mlp1/linear_0.b', mlp1.linear_transformations[0].b),
('/mlp1/linear_0.W', mlp1.linear_transformations[0].W),
('/mlp1/linear_1.W', mlp1.linear_transformations[1].W),
('/mlp2/linear_0.W', mlp2.linear_transformations[0].W)]
# Test getting and setting parameter values
mlp3 = MLP([Tanh()], [10, 10])
mlp3.allocate()
model3 = Model(mlp3.apply(x))
parameter_values = {
'/mlp/linear_0.W': 2 * numpy.ones((10, 10),
dtype=theano.config.floatX),
'/mlp/linear_0.b': 3 * numpy.ones(10, dtype=theano.config.floatX)}
model3.set_parameter_values(parameter_values)
assert numpy.all(
mlp3.linear_transformations[0].parameters[0].get_value() == 2)
assert numpy.all(
mlp3.linear_transformations[0].parameters[1].get_value() == 3)
got_parameter_values = model3.get_parameter_values()
assert len(got_parameter_values) == len(parameter_values)
for name, value in parameter_values.items():
assert_allclose(value, got_parameter_values[name])
# Test exception is raised if parameter shapes don't match
def helper():
parameter_values = {
'/mlp/linear_0.W': 2 * numpy.ones((11, 11),
dtype=theano.config.floatX),
'/mlp/linear_0.b': 3 * numpy.ones(11, dtype=theano.config.floatX)}
model3.set_parameter_values(parameter_values)
assert_raises(ValueError, helper)
# Test name conflict handling
mlp4 = MLP([Tanh()], [10, 10])
def helper():
Model(mlp4.apply(mlp3.apply(x)))
assert_raises(ValueError, helper)
def evaluate(ladder, load_path):
with open(load_path + '/trained_params_best.npz') as f:
loaded = np.load(f)
model = Model(ladder.costs.total)
params_dicts = model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
# '/f_6_.W' --> 'f_6_.W'
slash_index = param_name.find('/')
param_name = param_name[slash_index + 1:]
assert param.get_value().shape == loaded[param_name].shape
param.set_value(loaded[param_name])
test_data_stream, test_data_stream = get_mixed_streams(10000)
test_data = test_data_stream.get_epoch_iterator().next()
test_data_input = test_data[10]
test_data_target = test_data[0]
print 'Compiling ...'
cg = ComputationGraph([ladder.costs.total])
eval_ = theano.function(cg.inputs, ladder.error)
print 'Test_set_Error: ' + str(eval_(test_data_input, test_data_target))
import ipdb
ipdb.set_trace()
train_stream = get_stream('train', batch_size, h_dim, False)
data = train_stream.get_epoch_iterator(as_dict=True).next()
cg = ComputationGraph(cost)
f = theano.function(cg.inputs, cost)
print f(data['y'], data['x'], data['is_for_test'], data['drops'])
if not os.path.exists(save_path):
os.makedirs(save_path)
log_path = save_path + '/log.txt'
fh = logging.FileHandler(filename=log_path)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
print 'Bulding training process...'
model = Model(cost)
params = ComputationGraph(cost).parameters
print_num_params(params)
clipping = StepClipping(threshold=np.cast[floatX](1.0))
# Momentum(learning_rate=args.learning_rate, momentum=0.9)
rm_non_finite = RemoveNotFinite()
rms_prop = RMSProp(learning_rate=1e-3, decay_rate=0.5)
step_rule = CompositeRule([clipping, rms_prop, rm_non_finite])
algorithm = GradientDescent(
cost=cost,
parameters=params,
step_rule=step_rule)
# train_stream, valid_stream = get_seq_mnist_streams(
# h_dim, batch_size, update_prob)
train_stream = get_stream('train', batch_size, h_dim, False)
def main(config, tr_stream, dev_stream, use_bokeh=False, the_task=None, the_track=None, use_embeddings=False, lang='german'):
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']))
# Load pretrained embeddings if necessary; after the other parameters; ORDER MATTERS
if use_embeddings:
extensions.append(LoadEmbeddings(config['embeddings'][0] + lang + config['embeddings'][1]))
# 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()
def main(argv):
name = argv[1]
files = map(lambda p: join(folder, p), listdir(folder))
file = next(filter(lambda n: name in n, files))
print(file)
p = load_parameter_values(file)
net = net_dvc((128,128))
x = tensor.tensor4('image_features')
y_hat = net.apply(x)
g = Model(y_hat)
for k,v in p.items():
p[k] = v.astype('float32')
g.set_parameter_values(p)
a,t,v = get_dvc((128,128),trainning=False, shortcut=False)
run = function([x], y_hat)
def run_test(data):
res = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
return res
def max_index(l):
if l[0] > l[1]:
return 0
else:
return 1
def write_kaggle(f, l):
f.write("id,label\n")
for i,e in enumerate(l,start=1):
f.write(str(i)+","+str(e)+"\n")
def kaggle(file, data):
write_kaggle(file,map(max_index, run_test(data)))
def accuracy(data):
res = []
true = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
true.extend(i[1])
res = map(max_index, res)
total = 0
equal = 0
for r,t in zip(res,true):
total += 1
equal += 1 if r == t else 0
return equal/total
print("Training accuracy: ", accuracy(a))
print("Test accuracy: ", accuracy(v))
kaggle_file = join(result_folder, name+".kaggle")
print(kaggle_file)
with open(kaggle_file,'w') as f:
kaggle(f, t)
parameters=cg.parameters,
step_rule=Scale(learning_rate=0.1))
train_set = H5PYDataset('mushrooms.hdf5', which_sets=('train', ))
train_stream = DataStream.default_stream(train_set,
iteration_scheme=SequentialScheme(
train_set.num_examples,
batch_size=128))
test_set = H5PYDataset('mushrooms.hdf5', which_sets=('test', ))
test_stream = DataStream.default_stream(test_set,
iteration_scheme=SequentialScheme(
test_set.num_examples,
batch_size=128))
main = MainLoop(model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
FinishAfter(after_n_epochs=10),
Printing(),
TrainingDataMonitoring([cost, error_rate],
after_batch=True,
prefix='train'),
DataStreamMonitoring([cost, error_rate],
after_batch=True,
data_stream=test_stream,
prefix='test'),
Plot('Train',
channels=[['train_cost', 'test_cost'],
['train_error_rate', 'test_error_rate']])
def main(mode, config, use_bokeh=False):
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(
config['src_vocab_size'], config['enc_embed'], config['enc_nhids'],name='word_encoder')
decoder = Decoder(vocab_size=config['trg_vocab_size'],
embedding_dim=config['dec_embed'],
state_dim=config['dec_nhids'],
representation_dim=config['enc_nhids'] * 2,
match_function=config['match_function'],
use_doubly_stochastic=config['use_doubly_stochastic'],
lambda_ds=config['lambda_ds'],
use_local_attention=config['use_local_attention'],
window_size=config['window_size'],
use_step_decay_cost=config['use_step_decay_cost'],
use_concentration_cost=config['use_concentration_cost'],
lambda_ct=config['lambda_ct'],
use_stablilizer=config['use_stablilizer'],
lambda_st=config['lambda_st'])
# here attended dim (representation_dim) of decoder is 2*enc_nhinds
# because the context given by the encoder is a bidirectional context
if mode == "train":
# Create Theano variables
logger.info('Creating theano variables')
context_sentences=[];
context_sentence_masks=[];
for i in range(config['ctx_num']):
context_sentences.append(tensor.lmatrix('context_'+str(i)));
context_sentence_masks.append(tensor.matrix('context_'+str(i)+'_mask'));
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')
dev_source = tensor.lmatrix('dev_source')
dev_target=tensor.lmatrix('dev_target')
# Get training and development set streams
tr_stream = get_tr_stream_withContext(**config)
dev_stream = get_dev_stream_with_grdTruth(**config)
# Get cost of the model
sentence_representations_list=encoder.apply(source_sentence, source_sentence_mask);
sentence_representations_list=sentence_representations_list.dimshuffle(['x',0,1,2]);
sentence_masks_list=source_sentence_mask.T.dimshuffle(['x',0,1]);
for i in range(config['ctx_num']):
tmp_rep=encoder.apply(context_sentences[i],context_sentence_masks[i]);
tmp_rep=tmp_rep.dimshuffle(['x',0,1,2]);
sentence_representations_list=tensor.concatenate([sentence_representations_list,tmp_rep],axis=0);
sentence_masks_list=tensor.concatenate([sentence_masks_list,context_sentence_masks[i].T.dimshuffle(['x',0,1])],axis=0);
cost = decoder.cost(sentence_representations_list,
sentence_masks_list,
target_sentence,
target_sentence_mask)
logger.info('Creating computational graph')
perplexity = tensor.exp(cost)
perplexity.name = 'perplexity'
costs_computer = function(context_sentences+context_sentence_masks+[target_sentence,
target_sentence_mask,
source_sentence,
source_sentence_mask], (perplexity))
cg = ComputationGraph(cost)
# Initialize model
logger.info('Initializing model')
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()
encoder.bidir.prototype.weights_init = Orthogonal()
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
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'])
# 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")
extensions = [
FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([perplexity], after_batch=True),
CheckpointNMT(config['saveto'],
config['model_name'],
every_n_batches=config['save_freq'])
]
# Set up beam search and sampling computation graphs if necessary
if config['hook_samples'] >= 1 or config['bleu_script'] is not None:
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]))
# Add sampling
if config['hook_samples'] >= 1:
logger.info("Building sampler")
extensions.append(
Sampler(model=search_model, data_stream=tr_stream,
model_name=config['model_name'],
hook_samples=config['hook_samples'],
every_n_batches=config['sampling_freq'],
src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
if False:
logger.info("Building bleu validator")
extensions.append(
BleuValidator(sampling_input, samples=samples, config=config,
model=search_model, data_stream=dev_stream,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq'],
n_best=3,
track_n_models=6))
logger.info("Building perplexity validator")
extensions.append(
pplValidation(dev_source,dev_target, config=config,
model=costs_computer, data_stream=dev_stream,
model_name=config['model_name'],
every_n_batches=config['sampling_freq']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot('Cs-En', channels=[['decoder_cost_cost']],
after_batch=True))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
initial_learning_rate = config['initial_learning_rate']
log_path = os.path.join(config['saveto'], 'log')
if config['reload'] and os.path.exists(log_path):
with open(log_path, 'rb') as source:
log = cPickle.load(source)
last = max(log.keys()) - 1
if 'learning_rate' in log[last]:
initial_learning_rate = log[last]['learning_rate']
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([Scale(initial_learning_rate),
StepClipping(config['step_clipping']),
eval(config['step_rule'])()]))
_learning_rate = algorithm.step_rule.components[0].learning_rate
if config['learning_rate_decay']:
extensions.append(
LearningRateHalver(record_name='validation_cost',
comparator=lambda x, y: x > y,
learning_rate=_learning_rate,
patience_default=3))
else:
extensions.append(OldModelRemover(saveto=config['saveto']))
if config['learning_rate_grow']:
extensions.append(
LearningRateDoubler(record_name='validation_cost',
comparator=lambda x, y: x < y,
learning_rate=_learning_rate,
patience_default=3))
extensions.append(
SimplePrinting(config['model_name'], after_batch=True))
# 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()
elif mode == 'ppl':
# Create Theano variables
# Create Theano variables
logger.info('Creating theano variables')
context_sentences=[];
context_sentence_masks=[];
for i in range(config['ctx_num']):
context_sentences.append(tensor.lmatrix('context_'+str(i)));
context_sentence_masks.append(tensor.matrix('context_'+str(i)+'_mask'));
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
target_sentence = tensor.lmatrix('target')
target_sentence_mask = tensor.matrix('target_mask')
# Get training and development set streams
#tr_stream = get_tr_stream_withContext(**config)
dev_stream = get_dev_stream_withContext_grdTruth(**config)
# Get cost of the model
sentence_representations_list=encoder.apply(source_sentence, source_sentence_mask);
sentence_representations_list=sentence_representations_list.dimshuffle(['x',0,1,2]);
sentence_masks_list=source_sentence_mask.T.dimshuffle(['x',0,1]);
for i in range(config['ctx_num']):
tmp_rep=encoder.apply(context_sentences[i],context_sentence_masks[i]);
tmp_rep=tmp_rep.dimshuffle(['x',0,1,2]);
sentence_representations_list=tensor.concatenate([sentence_representations_list,tmp_rep],axis=0);
sentence_masks_list=tensor.concatenate([sentence_masks_list,context_sentence_masks[i].T.dimshuffle(['x',0,1])],axis=0);
cost = decoder.cost(sentence_representations_list,
sentence_masks_list,
target_sentence,
target_sentence_mask)
logger.info('Creating computational graph')
costs_computer = function(context_sentences+context_sentence_masks+[target_sentence,
target_sentence_mask,
source_sentence,
source_sentence_mask], (cost))
logger.info("Loading the model..")
model = Model(cost)
#loader = LoadNMT(config['saveto'])
loader = LoadNMT(config['validation_load']);
loader.set_model_parameters(model, loader.load_parameters_default())
logger.info("Started Validation: ")
ts = dev_stream.get_epoch_iterator()
total_cost = 0.0
total_tokens=0.0
#pbar = ProgressBar(max_value=len(ts)).start()#modified
pbar = ProgressBar(max_value=10000).start();
for i, (ctx_0,ctx_0_mask,ctx_1,ctx_1_mask,ctx_2,ctx_2_mask,src, src_mask, trg, trg_mask) in enumerate(ts):
costs = costs_computer(*[ctx_0,ctx_1,ctx_2,ctx_0_mask,ctx_1_mask,ctx_2_mask,trg, trg_mask,src, src_mask])
cost = costs.sum()
total_cost+=cost
total_tokens+=trg_mask.sum()
pbar.update(i + 1)
total_cost/=total_tokens;
pbar.finish()
#dev_stream.reset()
# run afterprocess
# self.ap.main()
total_cost=2**total_cost;
print("Average validation cost: " + str(total_cost));
elif mode == 'translate':
logger.info('Creating theano variables')
context_sentences=[];
context_sentence_masks=[];
for i in range(config['ctx_num']):
context_sentences.append(tensor.lmatrix('context_'+str(i)));
context_sentence_masks.append(tensor.matrix('context_'+str(i)+'_mask'));
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
sutils = SamplingBase()
unk_idx = config['unk_id']
src_eos_idx = config['src_vocab_size'] - 1
trg_eos_idx = config['trg_vocab_size'] - 1
trg_vocab = _ensure_special_tokens(
cPickle.load(open(config['trg_vocab'], 'rb')), bos_idx=0,
eos_idx=trg_eos_idx, unk_idx=unk_idx)
trg_ivocab = {v: k for k, v in trg_vocab.items()}
config['batch_size'] = 1
sentence_representations_list=encoder.apply(source_sentence, source_sentence_mask);
sentence_representations_list=sentence_representations_list.dimshuffle(['x',0,1,2]);
sentence_masks_list=source_sentence_mask.T.dimshuffle(['x',0,1]);
for i in range(config['ctx_num']):
tmp_rep=encoder.apply(context_sentences[i],context_sentence_masks[i]);
tmp_rep=tmp_rep.dimshuffle(['x',0,1,2]);
sentence_representations_list=tensor.concatenate([sentence_representations_list,tmp_rep],axis=0);
sentence_masks_list=tensor.concatenate([sentence_masks_list,context_sentence_masks[i].T.dimshuffle(['x',0,1])],axis=0);
generated = decoder.generate(sentence_representations_list,sentence_masks_list)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
beam_search = BeamSearch(samples=samples)
logger.info("Loading the model..")
model = Model(generated)
#loader = LoadNMT(config['saveto'])
loader = LoadNMT(config['validation_load']);
loader.set_model_parameters(model, loader.load_parameters_default())
logger.info("Started translation: ")
test_stream = get_dev_stream_withContext(**config)
ts = test_stream.get_epoch_iterator()
rts = open(config['val_set_source']).readlines()
ftrans_original = open(config['val_output_orig'], 'w')
saved_weights = []
total_cost = 0.0
pbar = ProgressBar(max_value=len(rts)).start()
for i, (line, line_raw) in enumerate(zip(ts, rts)):
trans_in = line_raw[3].split()
seqs=[];
input_=[];
input_mask=[];
for j in range(config['ctx_num']+1):
seqs.append(sutils._oov_to_unk(
line[2*j][0], config['src_vocab_size'], unk_idx))
input_mask.append(numpy.tile(line[2*j+1][0],(config['beam_size'], 1)))
input_.append(numpy.tile(seqs[j], (config['beam_size'], 1)))
#v=costs_computer(input_[0]);
# draw sample, checking to ensure we don't get an empty string back
trans, costs, attendeds, weights = \
beam_search.search(
input_values={source_sentence: input_[3],source_sentence_mask:input_mask[3],
context_sentences[0]: input_[0],context_sentence_masks[0]:input_mask[0],
context_sentences[1]: input_[1],context_sentence_masks[1]:input_mask[1],
context_sentences[2]: input_[2],context_sentence_masks[2]:input_mask[2]},
max_length=3*len(seqs[2]), eol_symbol=trg_eos_idx,
ignore_first_eol=True)
# normalize costs according to the sequence lengths
if config['normalized_bleu']:
lengths = numpy.array([len(s) for s in trans])
costs = costs / lengths
b = numpy.argsort(costs)[0]
#best=numpy.argsort(costs)[0:config['beam_size']];
#for b in best:
try:
total_cost += costs[b]
trans_out = trans[b]
totalLen=4*len(line[0][0]);
#weight = weights[b][:, :totalLen]
weight=weights
trans_out = sutils._idx_to_word(trans_out, trg_ivocab)
except ValueError:
logger.info(
"Can NOT find a translation for line: {}".format(i+1))
trans_out = '<UNK>'
saved_weights.append(weight)
print(' '.join(trans_out), file=ftrans_original)
pbar.update(i + 1)
pbar.finish()
logger.info("Total cost of the test: {}".format(total_cost))
cPickle.dump(saved_weights, open(config['attention_weights'], 'wb'))
ftrans_original.close()
ap = afterprocesser(config)
ap.main()
def get_confidence_function(exp_config):
# 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_suffix')
target_sentence_mask = tensor.matrix('target_suffix_mask')
target_prefix = tensor.lmatrix('target_prefix')
target_prefix_mask = tensor.matrix('target_prefix_mask')
logger.info('Creating computational graph')
# build the model
encoder = BidirectionalEncoder(exp_config['src_vocab_size'],
exp_config['enc_embed'],
exp_config['enc_nhids'])
# Note: the 'min_risk' kwarg tells the decoder which sequence_generator and cost_function to use
decoder = NMTPrefixDecoder(exp_config['trg_vocab_size'],
exp_config['dec_embed'],
exp_config['dec_nhids'],
exp_config['enc_nhids'] * 2,
loss_function='cross_entropy')
# rename to match baseline NMT systems
decoder.name = 'decoder'
predictions, merged_states = decoder.prediction_tags(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask,
target_prefix, target_prefix_mask)
# WORKING: also get the softmax prediction feature
# WORKING: add features for source len, prefix len, position in suffix (position in suffix only makes sense if we're training on predictions)
p_shape = predictions.shape
predictions = predictions.reshape([p_shape[0] * p_shape[1], p_shape[2]])
prediction_softmax = tensor.nnet.nnet.softmax(
predictions.reshape([p_shape[0] * p_shape[1],
p_shape[2]])).reshape(p_shape)
prediction_feature = prediction_softmax.max(axis=-1)[:, :, None]
all_features = tensor.concatenate([merged_states, prediction_feature],
axis=-1)
confidence_output = decoder.sequence_generator.confidence_predictions(
all_features)
logger.info('Creating computational graph')
confidence_model = Model(confidence_output)
# Note that the parameters of this model must be pretrained, otherwise this doesn't make sense
param_values = LoadNMT.load_parameter_values(
exp_config['saved_parameters'], brick_delimiter=None)
LoadNMT.set_model_parameters(confidence_model, param_values)
confidence_param_values = LoadNMT.load_parameter_values(
exp_config['confidence_saved_parameters'], brick_delimiter=None)
LoadNMT.set_model_parameters(confidence_model, confidence_param_values)
confidence_function = confidence_model.get_theano_function()
return confidence_function
def main(config,
tr_stream,
dev_stream,
use_bokeh=False,
src_vocab=None,
trg_vocab=None):
# 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(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)
cost = decoder.cost(encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence,
target_sentence_mask)
# Initialize model
logger.info('Initializing model')
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()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# GRAPH TRANSFORMATIONS FOR BETTER TRAINING
# TODO: allow user to remove some params from the graph, for example if embeddings should be kept static
if config.get('l2_regularization', False) is True:
l2_reg_alpha = config['l2_regularization_alpha']
logger.info(
'Applying l2 regularization with alpha={}'.format(l2_reg_alpha))
model_weights = VariableFilter(roles=[WEIGHT])(cg.variables)
for W in model_weights:
cost = cost + (l2_reg_alpha * (W**2).sum())
# why do we need to name the cost variable? Where did the original name come from?
cost.name = 'decoder_cost_cost'
cg = ComputationGraph(cost)
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
# this is the probability of dropping out, so you probably want to make it <=0.5
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'])
# 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)
# allow user to externally initialize some params
model_params = training_model.get_parameter_dict()
if config.get('external_embeddings', None) is not None:
for key in config['external_embeddings']:
path_to_params = config['external_embeddings'][key]
logger.info(
'Replacing {} parameters with external params at: {}'.format(
key, path_to_params))
external_params = numpy.load(path_to_params)
len_external_idx = external_params.shape[0]
print(external_params.shape)
# Working: look in the dictionary and overwrite the correct rows
existing_params = model_params[key].get_value()
if key == '/bidirectionalencoder/embeddings.W':
vocab = src_vocab
elif key == '/decoder/sequencegenerator/readout/lookupfeedbackwmt15/lookuptable.W':
vocab = trg_vocab
else:
raise KeyError(
'Unknown embedding parameter key: {}'.format(key))
for k, i in vocab.items():
if i < len_external_idx:
existing_params[i] = external_params[i]
# model_params_shape = model_params[key].get_value().shape
# assert model_params[key].get_value().shape == external_params.shape, ("Parameter dims must not change,"
# "shapes {} and {} do not match".
# format(model_params_shape,
# external_params.shape))
model_params[key].set_value(existing_params)
# create the training directory, and copy this config there if directory doesn't exist
if not os.path.isdir(config['saveto']):
os.makedirs(config['saveto'])
shutil.copy(config['config_file'], config['saveto'])
# Set extensions
logger.info("Initializing extensions")
extensions = []
# Set up beam search and sampling computation graphs if necessary
if config['hook_samples'] >= 1 or config['bleu_script'] is not None:
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
# note that generated containes several different outputs
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
# Note: this is broken for unicode chars
#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=config['sampling_freq'],
# src_vocab_size=config['src_vocab_size']))
# WORKING: remove these validators in favor of Async
# TODO: implement burn-in in the validation extension (don't fire until we're past the burn-in iteration)
# Add early stopping based on bleu
# if config.get('bleu_script', None) is not None:
# logger.info("Building bleu validator")
# extensions.append(
# BleuValidator(sampling_input, samples=samples, config=config,
# model=search_model, data_stream=dev_stream,
# normalize=config['normalized_bleu'],
# every_n_batches=config['bleu_val_freq']))
# Add early stopping based on Meteor
# if config.get('meteor_directory', None) is not None:
# logger.info("Building meteor validator")
# extensions.append(
# MeteorValidator(sampling_input, samples=samples, config=config,
# model=search_model, data_stream=dev_stream,
# normalize=config['normalized_bleu'],
# every_n_batches=config['bleu_val_freq']))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Set up training algorithm
logger.info("Initializing training algorithm")
# if there is dropout or random noise, we need to use the output of the modified graph
if config['dropout'] < 1.0 or config['weight_noise_ff'] > 0.0:
algorithm = GradientDescent(cost=cg.outputs[0],
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]))
else:
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]))
# enrich the logged information
extensions.extend([
Timing(every_n_batches=100),
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'])
])
# External non-blocking validation
extensions.append(
RunExternalValidation(config=config,
every_n_batches=config['bleu_val_freq']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot(config['model_save_directory'],
channels=[['decoder_cost_cost'],
['validation_set_bleu_score'],
['validation_set_meteor_score']],
every_n_batches=1))
# 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()
cost_monitor.name = "cost_monitor"
elif conf.task == 'framewise':
cost_train = categorical_crossentropy_batch().apply(y_hat_softmax, y, x_m)
cost_train.name = 'cost'
cost_monitor = cost_train
else:
raise ValueError, conf.task
recognizer.initialize()
cg = ComputationGraph([cost_train, y_hat, x_m, y, y_m])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
cg = apply_noise(cg, weights, conf.weight_noise)
#************* training algorithm *************
model = Model(cost_train)
if conf.step_rule == 'AdaDelta':
step_rule = AdaDelta()
elif conf.step_rule == 'Momentum':
step_rule = Momentum(learning_rate=conf.learning_rate,
momentum=conf.momentum)
else:
raise ('step_rule not known: {}'.format(conf.step_rule))
step_rule = CompositeRule([step_rule, StepClipping(conf.step_clipping)])
algorithm = GradientDescent(cost=cost_train,
parameters=cg.parameters,
step_rule=step_rule)
#***************** main loop ****************
train_monitor = TrainingDataMonitoring([cost_monitor], prefix="train")
def main(n_hiddens=1500,
n_out=200,
noise_std=0.5,
learning_rate=2e-1,
momentum=0.9,
gamma=2.0):
######################
# Model
######################
import theano
from theano.sandbox.rng_mrg import MRG_RandomStreams
import numpy
import theano.tensor as tensor
from lvq.lvq import AaronLVQ, SupervisedNG, initialize_prototypes
from blocks.bricks import Linear, LinearMaxout
from blocks.bricks import Rectifier, Softmax, Logistic
from blocks.bricks.cost import MisclassificationRate
from blocks.config import config
from blocks.initialization import Uniform, Constant
from blocks.model import Model
from lvq.batch_norm import MLP
#from blocks.bricks import MLP
seed = config.default_seed
rng = MRG_RandomStreams(seed)
x = tensor.tensor4('features')
flat_x = tensor.flatten(x, outdim=2)
flat_x_noise = flat_x + rng.normal(size=flat_x.shape, std=noise_std)
y = tensor.imatrix('targets')
flat_y = tensor.flatten(y, outdim=1)
act = Rectifier()
mlp = MLP(dims=[784, n_hiddens, n_hiddens, n_out],
activations=[act, act, None])
mlp.weights_init = Uniform(0.0, 0.001)
mlp.biases_init = Constant(0.0)
mlp.initialize()
train_out = mlp.apply(flat_x_noise)
test_out = mlp.inference(flat_x)
lamb = theano.shared(numpy.float32(10.0))
lvq = SupervisedNG(10,
n_out,
nonlin=True,
gamma=gamma,
lamb=lamb,
name='lvq')
test_loss, test_misclass = lvq.apply(test_out, flat_y, 'test')
loss, misclass = lvq.apply(train_out, flat_y, 'train')
model = Model(loss)
######################
# Data
######################
#from mnist import MNIST
from fuel.datasets import MNIST
from fuel.streams import DataStream
from fuel.schemes import ShuffledScheme, SequentialScheme
from fuel.transformers import ScaleAndShift, ForceFloatX
mnist_train = MNIST([
'train'
]) #MNIST('train', drop_input=False, sources=('features', 'targets'))
mnist_test = MNIST([
'test'
]) # MNIST('test', drop_input=False, sources=('features', 'targets'))
batch_size = 100 #Batch size for training
batch_size_mon = 2000 # Batch size for monitoring and batch normalization
n_batches = int(
numpy.ceil(float(mnist_train.num_examples) / batch_size_mon))
num_protos = 10
ind = range(mnist_train.num_examples)
train_ind = ind[:50000]
val_ind = ind[50000:]
def preprocessing(data_stream):
return ForceFloatX(ScaleAndShift(data_stream,
1 / 255.0,
0.0,
which_sources=('features', )),
which_sources=('features', ))
train_stream_mon = preprocessing(
DataStream(mnist_train,
iteration_scheme=ShuffledScheme(train_ind, batch_size_mon)))
train_stream_bn = preprocessing(
DataStream(mnist_train,
iteration_scheme=ShuffledScheme(train_ind, batch_size_mon)))
train_stream = preprocessing(
DataStream(mnist_train,
iteration_scheme=ShuffledScheme(train_ind, batch_size)))
valid_stream = preprocessing(
DataStream(mnist_train,
iteration_scheme=ShuffledScheme(val_ind, batch_size)))
test_stream = preprocessing(
DataStream(mnist_test,
iteration_scheme=ShuffledScheme(mnist_test.num_examples,
batch_size_mon)))
initialize_prototypes(lvq, x, train_out, train_stream_mon)
######################
# Training
######################
from blocks.main_loop import MainLoop
from blocks.extensions import FinishAfter, Timing, Printing
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
from blocks.algorithms import GradientDescent, Momentum, RMSProp, Adam
from blocks.extensions.saveload import Checkpoint
from lvq.extensions import EarlyStopping, LRDecay, MomentumSwitchOff, NCScheduler
from lvq.batch_norm import BatchNormExtension
lr = theano.shared(numpy.float32(1e-3))
step_rule = Momentum(
learning_rate, 0.9
) #Adam(learning_rate=lr) #RMSProp(learning_rate=1e-5, max_scaling=1e4)
num_epochs = 100
earlystop = EarlyStopping('valid_lvq_apply_misclass', 100,
'./exp/alvq.pkl')
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=GradientDescent(cost=model.outputs[0],
parameters=model.parameters,
step_rule=step_rule),
extensions=[
FinishAfter(after_n_epochs=num_epochs),
BatchNormExtension(model, train_stream_bn, n_batches),
LRDecay(step_rule.learning_rate, [20, 40, 60, 80]),
MomentumSwitchOff(step_rule.momentum, num_epochs - 20),
NCScheduler(lamb, 30., 0, num_epochs),
DataStreamMonitoring(variables=[test_loss, test_misclass],
data_stream=train_stream_mon,
prefix='train'),
DataStreamMonitoring(variables=[test_loss, test_misclass],
data_stream=valid_stream,
prefix='valid'),
DataStreamMonitoring(variables=[test_loss, test_misclass],
data_stream=test_stream,
prefix='test'),
Timing(), earlystop,
Printing(after_epoch=True)
])
main_loop.run()
return main_loop.status.get('best_test_lvq_apply_misclass', None)
def train_snli_model(new_training_job,
config,
save_path,
params,
fast_start,
fuel_server,
seed,
model='simple'):
if config['exclude_top_k'] > config['num_input_words'] and config[
'num_input_words'] > 0:
raise Exception("Some words have neither word nor def embedding")
c = config
logger = configure_logger(name="snli_baseline_training",
log_file=os.path.join(save_path, "log.txt"))
if not os.path.exists(save_path):
logger.info("Start a new job")
os.mkdir(save_path)
else:
logger.info("Continue an existing job")
with open(os.path.join(save_path, "cmd.txt"), "w") as f:
f.write(" ".join(sys.argv))
# Make data paths nice
for path in [
'dict_path', 'embedding_def_path', 'embedding_path', 'vocab',
'vocab_def', 'vocab_text'
]:
if c.get(path, ''):
if not os.path.isabs(c[path]):
c[path] = os.path.join(fuel.config.data_path[0], c[path])
main_loop_path = os.path.join(save_path, 'main_loop.tar')
main_loop_best_val_path = os.path.join(save_path, 'main_loop_best_val.tar')
stream_path = os.path.join(save_path, 'stream.pkl')
# Save config to save_path
json.dump(config, open(os.path.join(save_path, "config.json"), "w"))
if model == 'simple':
nli_model, data, used_dict, used_retrieval, _ = _initialize_simple_model_and_data(
c)
elif model == 'esim':
nli_model, data, used_dict, used_retrieval, _ = _initialize_esim_model_and_data(
c)
else:
raise NotImplementedError()
# Compute cost
s1, s2 = T.lmatrix('sentence1'), T.lmatrix('sentence2')
if c['dict_path']:
assert os.path.exists(c['dict_path'])
s1_def_map, s2_def_map = T.lmatrix('sentence1_def_map'), T.lmatrix(
'sentence2_def_map')
def_mask = T.fmatrix("def_mask")
defs = T.lmatrix("defs")
else:
s1_def_map, s2_def_map = None, None
def_mask = None
defs = None
s1_mask, s2_mask = T.fmatrix('sentence1_mask'), T.fmatrix('sentence2_mask')
y = T.ivector('label')
cg = {}
for train_phase in [True, False]:
# NOTE: Please don't change outputs of cg
if train_phase:
with batch_normalization(nli_model):
pred = nli_model.apply(s1,
s1_mask,
s2,
s2_mask,
def_mask=def_mask,
defs=defs,
s1_def_map=s1_def_map,
s2_def_map=s2_def_map,
train_phase=train_phase)
else:
pred = nli_model.apply(s1,
s1_mask,
s2,
s2_mask,
def_mask=def_mask,
defs=defs,
s1_def_map=s1_def_map,
s2_def_map=s2_def_map,
train_phase=train_phase)
cost = CategoricalCrossEntropy().apply(y.flatten(), pred)
error_rate = MisclassificationRate().apply(y.flatten(), pred)
cg[train_phase] = ComputationGraph([cost, error_rate])
# Weight decay (TODO: Make it less bug prone)
if model == 'simple':
weights_to_decay = VariableFilter(
bricks=[dense for dense, relu, bn in nli_model._mlp],
roles=[WEIGHT])(cg[True].variables)
weight_decay = np.float32(c['l2']) * sum(
(w**2).sum() for w in weights_to_decay)
elif model == 'esim':
weight_decay = 0.0
else:
raise NotImplementedError()
final_cost = cg[True].outputs[0] + weight_decay
final_cost.name = 'final_cost'
# Add updates for population parameters
if c.get("bn", True):
pop_updates = get_batch_normalization_updates(cg[True])
extra_updates = [(p, m * 0.1 + p * (1 - 0.1)) for p, m in pop_updates]
else:
pop_updates = []
extra_updates = []
if params:
logger.debug("Load parameters from {}".format(params))
with open(params) as src:
loaded_params = load_parameters(src)
cg[True].set_parameter_values(loaded_params)
for param, m in pop_updates:
param.set_value(loaded_params[get_brick(
param).get_hierarchical_name(param)])
if os.path.exists(os.path.join(save_path, "main_loop.tar")):
logger.warning("Manually loading BN stats :(")
with open(os.path.join(save_path, "main_loop.tar")) as src:
loaded_params = load_parameters(src)
for param, m in pop_updates:
param.set_value(
loaded_params[get_brick(param).get_hierarchical_name(param)])
if theano.config.compute_test_value != 'off':
test_value_data = next(
data.get_stream('train', batch_size=4).get_epoch_iterator())
s1.tag.test_value = test_value_data[0]
s1_mask.tag.test_value = test_value_data[1]
s2.tag.test_value = test_value_data[2]
s2_mask.tag.test_value = test_value_data[3]
y.tag.test_value = test_value_data[4]
# Freeze embeddings
if not c['train_emb']:
frozen_params = [
p for E in nli_model.get_embeddings_lookups() for p in E.parameters
]
train_params = [p for p in cg[True].parameters]
assert len(set(frozen_params) & set(train_params)) > 0
else:
frozen_params = []
if not c.get('train_def_emb', 1):
frozen_params_def = [
p for E in nli_model.get_def_embeddings_lookups()
for p in E.parameters
]
train_params = [p for p in cg[True].parameters]
assert len(set(frozen_params_def) & set(train_params)) > 0
frozen_params += frozen_params_def
train_params = [p for p in cg[True].parameters if p not in frozen_params]
train_params_keys = [
get_brick(p).get_hierarchical_name(p) for p in train_params
]
# Optimizer
algorithm = GradientDescent(cost=final_cost,
on_unused_sources='ignore',
parameters=train_params,
step_rule=Adam(learning_rate=c['lr']))
algorithm.add_updates(extra_updates)
m = Model(final_cost)
parameters = m.get_parameter_dict() # Blocks version mismatch
logger.info("Trainable parameters" + "\n" +
pprint.pformat([(key, parameters[key].get_value().shape)
for key in sorted(train_params_keys)],
width=120))
logger.info("# of parameters {}".format(
sum([
np.prod(parameters[key].get_value().shape)
for key in sorted(train_params_keys)
])))
### Monitored args ###
train_monitored_vars = [final_cost] + cg[True].outputs
monitored_vars = cg[False].outputs
val_acc = monitored_vars[1]
to_monitor_names = [
'def_unk_ratio', 's1_merged_input_rootmean2', 's1_def_mean_rootmean2',
's1_gate_rootmean2', 's1_compose_gate_rootmean2'
]
for k in to_monitor_names:
train_v, valid_v = VariableFilter(name=k)(
cg[True]), VariableFilter(name=k)(cg[False])
if len(train_v):
logger.info("Adding {} tracking".format(k))
train_monitored_vars.append(train_v[0])
monitored_vars.append(valid_v[0])
else:
logger.warning("Didnt find {} in cg".format(k))
if c['monitor_parameters']:
for name in train_params_keys:
param = parameters[name]
num_elements = numpy.product(param.get_value().shape)
norm = param.norm(2) / num_elements
grad_norm = algorithm.gradients[param].norm(2) / num_elements
step_norm = algorithm.steps[param].norm(2) / num_elements
stats = tensor.stack(norm, grad_norm, step_norm,
step_norm / grad_norm)
stats.name = name + '_stats'
train_monitored_vars.append(stats)
regular_training_stream = data.get_stream('train',
batch_size=c['batch_size'],
seed=seed)
if fuel_server:
# the port will be configured by the StartFuelServer extension
training_stream = ServerDataStream(
sources=regular_training_stream.sources,
hwm=100,
produces_examples=regular_training_stream.produces_examples)
else:
training_stream = regular_training_stream
### Build extensions ###
extensions = [
# Load(main_loop_path, load_iteration_state=True, load_log=True)
# .set_conditions(before_training=not new_training_job),
StartFuelServer(regular_training_stream,
stream_path,
hwm=100,
script_path=os.path.join(
os.path.dirname(__file__),
"../bin/start_fuel_server.py"),
before_training=fuel_server),
Timing(every_n_batches=c['mon_freq']),
ProgressBar(),
RetrievalPrintStats(retrieval=used_retrieval,
every_n_batches=c['mon_freq_valid'],
before_training=not fast_start),
Timestamp(),
TrainingDataMonitoring(train_monitored_vars,
prefix="train",
every_n_batches=c['mon_freq']),
]
if c['layout'] == 'snli':
validation = DataStreamMonitoring(monitored_vars,
data.get_stream('valid',
batch_size=14,
seed=seed),
before_training=not fast_start,
on_resumption=True,
after_training=True,
every_n_batches=c['mon_freq_valid'],
prefix='valid')
extensions.append(validation)
elif c['layout'] == 'mnli':
validation = DataStreamMonitoring(monitored_vars,
data.get_stream('valid_matched',
batch_size=14,
seed=seed),
every_n_batches=c['mon_freq_valid'],
on_resumption=True,
after_training=True,
prefix='valid_matched')
validation_mismatched = DataStreamMonitoring(
monitored_vars,
data.get_stream('valid_mismatched', batch_size=14, seed=seed),
every_n_batches=c['mon_freq_valid'],
before_training=not fast_start,
on_resumption=True,
after_training=True,
prefix='valid_mismatched')
extensions.extend([validation, validation_mismatched])
else:
raise NotImplementedError()
# Similarity trackers for embeddings
if len(c.get('vocab_def', '')):
retrieval_vocab = Vocabulary(c['vocab_def'])
else:
retrieval_vocab = data.vocab
retrieval_all = Retrieval(vocab_text=retrieval_vocab,
dictionary=used_dict,
max_def_length=c['max_def_length'],
exclude_top_k=0,
max_def_per_word=c['max_def_per_word'])
for name in [
's1_word_embeddings', 's1_dict_word_embeddings',
's1_translated_word_embeddings'
]:
variables = VariableFilter(name=name)(cg[False])
if len(variables):
s1_emb = variables[0]
logger.info("Adding similarity tracking for " + name)
# A bit sloppy about downcast
if "dict" in name:
embedder = construct_dict_embedder(theano.function(
[s1, defs, def_mask, s1_def_map],
s1_emb,
allow_input_downcast=True),
vocab=data.vocab,
retrieval=retrieval_all)
extensions.append(
SimilarityWordEmbeddingEval(
embedder=embedder,
prefix=name,
every_n_batches=c['mon_freq_valid'],
before_training=not fast_start))
else:
embedder = construct_embedder(theano.function(
[s1], s1_emb, allow_input_downcast=True),
vocab=data.vocab)
extensions.append(
SimilarityWordEmbeddingEval(
embedder=embedder,
prefix=name,
every_n_batches=c['mon_freq_valid'],
before_training=not fast_start))
track_the_best = TrackTheBest(validation.record_name(val_acc),
before_training=not fast_start,
every_n_epochs=c['save_freq_epochs'],
after_training=not fast_start,
every_n_batches=c['mon_freq_valid'],
choose_best=min)
extensions.append(track_the_best)
# Special care for serializing embeddings
if len(c.get('embedding_path', '')) or len(c.get('embedding_def_path',
'')):
extensions.insert(
0,
LoadNoUnpickling(main_loop_path,
load_iteration_state=True,
load_log=True).set_conditions(
before_training=not new_training_job))
extensions.append(
Checkpoint(main_loop_path,
parameters=train_params + [p for p, m in pop_updates],
save_main_loop=False,
save_separately=['log', 'iteration_state'],
before_training=not fast_start,
every_n_epochs=c['save_freq_epochs'],
after_training=not fast_start).add_condition(
['after_batch', 'after_epoch'],
OnLogRecord(track_the_best.notification_name),
(main_loop_best_val_path, )))
else:
extensions.insert(
0,
Load(main_loop_path, load_iteration_state=True,
load_log=True).set_conditions(
before_training=not new_training_job))
extensions.append(
Checkpoint(main_loop_path,
parameters=cg[True].parameters +
[p for p, m in pop_updates],
before_training=not fast_start,
every_n_epochs=c['save_freq_epochs'],
after_training=not fast_start).add_condition(
['after_batch', 'after_epoch'],
OnLogRecord(track_the_best.notification_name),
(main_loop_best_val_path, )))
extensions.extend([
DumpCSVSummaries(save_path,
every_n_batches=c['mon_freq_valid'],
after_training=True),
DumpTensorflowSummaries(save_path,
after_epoch=True,
every_n_batches=c['mon_freq_valid'],
after_training=True),
Printing(every_n_batches=c['mon_freq_valid']),
PrintMessage(msg="save_path={}".format(save_path),
every_n_batches=c['mon_freq']),
FinishAfter(after_n_batches=c['n_batches']).add_condition(
['after_batch'],
OnLogStatusExceed('iterations_done', c['n_batches']))
])
logger.info(extensions)
### Run training ###
if "VISDOM_SERVER" in os.environ:
print("Running visdom server")
ret = subprocess.Popen([
os.path.join(os.path.dirname(__file__), "../visdom_plotter.py"),
"--visdom-server={}".format(os.environ['VISDOM_SERVER']),
"--folder={}".format(save_path)
])
time.sleep(0.1)
if ret.returncode is not None:
raise Exception()
atexit.register(lambda: os.kill(ret.pid, signal.SIGINT))
model = Model(cost)
for p, m in pop_updates:
model._parameter_dict[get_brick(p).get_hierarchical_name(p)] = p
main_loop = MainLoop(algorithm,
training_stream,
model=model,
extensions=extensions)
assert os.path.exists(save_path)
main_loop.run()
def evaluate(c, tar_path, *args, **kwargs):
"""
Performs rudimentary evaluation of SNLI/MNLI run
* Runs on valid and test given network
* Saves all predictions
* Saves embedding matrix
* Saves results.json and predictions.csv
"""
# Load and configure
model = kwargs['model']
assert c.endswith("json")
c = json.load(open(c))
# Very ugly absolute path fix
ABS_PATHS = [
"data/", "/mnt/users/jastrzebski/local/dict_based_learning/data/",
"/data/cf9ffb48-61bd-40dc-a011-b2e7e5acfd72/"
]
from six import string_types
for abs_path in ABS_PATHS:
for k in c:
if isinstance(c[k], string_types):
if c[k].startswith(abs_path):
c[k] = c[k][len(abs_path):]
# Make data paths nice
for path in [
'dict_path', 'embedding_def_path', 'embedding_path', 'vocab',
'vocab_def', 'vocab_text'
]:
if c.get(path, ''):
if not os.path.isabs(c[path]):
c[path] = os.path.join(fuel.config.data_path[0], c[path])
logging.info("Updating config with " + str(kwargs))
c.update(**kwargs)
# NOTE: This assures we don't miss crucial definition for some def heavy words
# usually it is a good idea
c['max_def_per_word'] = c['max_def_per_word'] * 2
assert tar_path.endswith("tar")
dest_path = os.path.dirname(tar_path)
prefix = os.path.splitext(os.path.basename(tar_path))[0]
s1_decoded, s2_decoded = T.lmatrix('sentence1'), T.lmatrix('sentence2')
if c['dict_path']:
s1_def_map, s2_def_map = T.lmatrix('sentence1_def_map'), T.lmatrix(
'sentence2_def_map')
def_mask = T.fmatrix("def_mask")
defs = T.lmatrix("defs")
else:
s1_def_map, s2_def_map = None, None
def_mask = None
defs = None
s1_mask, s2_mask = T.fmatrix('sentence1_mask'), T.fmatrix('sentence2_mask')
if model == 'simple':
model, data, used_dict, used_retrieval, used_vocab = _initialize_simple_model_and_data(
c)
elif model == 'esim':
model, data, used_dict, used_retrieval, used_vocab = _initialize_esim_model_and_data(
c)
else:
raise NotImplementedError()
pred = model.apply(s1_decoded,
s1_mask,
s2_decoded,
s2_mask,
def_mask=def_mask,
defs=defs,
s1_def_map=s1_def_map,
s2_def_map=s2_def_map,
train_phase=False)
cg = ComputationGraph([pred])
if c.get("bn", True):
bn_params = [
p for p in VariableFilter(bricks=[BatchNormalization])(cg)
if hasattr(p, "set_value")
]
else:
bn_params = []
# Load model
model = Model(cg.outputs)
parameters = model.get_parameter_dict() # Blocks version mismatch
logging.info(
"Trainable parameters" + "\n" +
pprint.pformat([(key, parameters[key].get_value().shape)
for key in sorted([
get_brick(param).get_hierarchical_name(param)
for param in cg.parameters
])],
width=120))
logging.info("# of parameters {}".format(
sum([
np.prod(parameters[key].get_value().shape) for key in sorted([
get_brick(param).get_hierarchical_name(param)
for param in cg.parameters
])
])))
with open(tar_path) as src:
params = load_parameters(src)
loaded_params_set = set(params.keys())
model_params_set = set([
get_brick(param).get_hierarchical_name(param)
for param in cg.parameters
])
logging.info("Loaded extra parameters")
logging.info(loaded_params_set - model_params_set)
logging.info("Missing parameters")
logging.info(model_params_set - loaded_params_set)
model.set_parameter_values(params)
if c.get("bn", True):
logging.info("Loading " + str([
get_brick(param).get_hierarchical_name(param)
for param in bn_params
]))
for param in bn_params:
param.set_value(
params[get_brick(param).get_hierarchical_name(param)])
for p in bn_params:
model._parameter_dict[get_brick(p).get_hierarchical_name(p)] = p
# Read logs
logs = pd.read_csv(os.path.join(dest_path, "logs.csv"))
best_val_acc = logs['valid_misclassificationrate_apply_error_rate'].min()
logging.info("Best measured valid acc: " + str(best_val_acc))
# NOTE(kudkudak): We need this to have comparable mean rank and embedding scores
reference_vocab = Vocabulary(
os.path.join(fuel.config.data_path[0], c['data_path'], 'vocab.txt'))
vocab_all = Vocabulary(
os.path.join(
fuel.config.data_path[0], c['data_path'],
'vocab_all.txt')) # Can include OOV words, which is interesting
retrieval_all = Retrieval(vocab_text=used_vocab,
dictionary=used_dict,
max_def_length=c['max_def_length'],
exclude_top_k=0,
max_def_per_word=c['max_def_per_word'])
# logging.info("Calculating dict and word embeddings for vocab.txt and vocab_all.txt")
# for name in ['s1_word_embeddings', 's1_dict_word_embeddings']:
# variables = VariableFilter(name=name)(cg)
# if len(variables):
# s1_emb = variables[0]
# # A bit sloppy about downcast
#
# if "dict" in name:
# embedder = construct_dict_embedder(
# theano.function([s1_decoded, defs, def_mask, s1_def_map], s1_emb, allow_input_downcast=True),
# vocab=data.vocab, retrieval=retrieval_all)
# else:
# embedder = construct_embedder(theano.function([s1_decoded], s1_emb, allow_input_downcast=True),
# vocab=data.vocab)
#
# for v_name, v in [("vocab_all", vocab_all), ("vocab", reference_vocab)]:
# logging.info("Calculating {} embeddings for {}".format(name, v_name))
# Predict
predict_fnc = theano.function(cg.inputs, pred)
results = {}
batch_size = 14
for subset in ['valid', 'test']:
logging.info("Predicting on " + subset)
stream = data.get_stream(subset, batch_size=batch_size, seed=778)
it = stream.get_epoch_iterator()
rows = []
for ex in tqdm.tqdm(it, total=10000 / batch_size):
ex = dict(zip(stream.sources, ex))
inp = [ex[v.name] for v in cg.inputs]
prob = predict_fnc(*inp)
label_pred = np.argmax(prob, axis=1)
for id in range(len(prob)):
s1_decoded = used_vocab.decode(ex['sentence1'][id]).split()
s2_decoded = used_vocab.decode(ex['sentence2'][id]).split()
assert used_vocab == data.vocab
s1_decoded = [
'*' + w + '*'
if used_vocab.word_to_id(w) > c['num_input_words'] else w
for w in s1_decoded
]
s2_decoded = [
'*' + w + '*'
if used_vocab.word_to_id(w) > c['num_input_words'] else w
for w in s2_decoded
]
# Different difficulty metrics
# text_unk_percentage
s1_no_pad = [w for w in ex['sentence1'][id] if w != 0]
s2_no_pad = [w for w in ex['sentence2'][id] if w != 0]
s1_unk_percentage = sum([
1. for w in s1_no_pad if w == used_vocab.unk
]) / len(s1_no_pad)
s2_unk_percentage = sum([
1. for w in s1_no_pad if w == used_vocab.unk
]) / len(s2_no_pad)
# mean freq word
s1_mean_freq = np.mean([
0 if w == data.vocab.unk else used_vocab._id_to_freq[w]
for w in s1_no_pad
])
s2_mean_freq = np.mean([
0 if w == data.vocab.unk else used_vocab._id_to_freq[w]
for w in s2_no_pad
])
# mean rank word (UNK is max rank)
# NOTE(kudkudak): Will break if we reindex unk between vocabs :P
s1_mean_rank = np.mean([
reference_vocab.size() if reference_vocab.word_to_id(
used_vocab.id_to_word(w)) == reference_vocab.unk else
reference_vocab.word_to_id(used_vocab.id_to_word(w))
for w in s1_no_pad
])
s2_mean_rank = np.mean([
reference_vocab.size() if reference_vocab.word_to_id(
used_vocab.id_to_word(w)) == reference_vocab.unk else
reference_vocab.word_to_id(used_vocab.id_to_word(w))
for w in s2_no_pad
])
rows.append({
"pred": label_pred[id],
"true_label": ex['label'][id],
"s1": ' '.join(s1_decoded),
"s2": ' '.join(s2_decoded),
"s1_unk_percentage": s1_unk_percentage,
"s2_unk_percentage": s2_unk_percentage,
"s1_mean_freq": s1_mean_freq,
"s2_mean_freq": s2_mean_freq,
"s1_mean_rank": s1_mean_rank,
"s2_mean_rank": s2_mean_rank,
"p_0": prob[id, 0],
"p_1": prob[id, 1],
"p_2": prob[id, 2]
})
preds = pd.DataFrame(rows, columns=rows[0].keys())
preds.to_csv(
os.path.join(dest_path,
prefix + '_predictions_{}.csv'.format(subset)))
results[subset] = {}
results[subset]['misclassification'] = 1 - np.mean(
preds.pred == preds.true_label)
if subset == "valid" and np.abs(
(1 - np.mean(preds.pred == preds.true_label)) -
best_val_acc) > 0.001:
logging.error("!!!")
logging.error(
"Found different best_val_acc. Probably due to changed specification of the model class."
)
logging.error("Discrepancy {}".format(
(1 - np.mean(preds.pred == preds.true_label)) - best_val_acc))
logging.error("!!!")
logging.info(results)
json.dump(results,
open(os.path.join(dest_path, prefix + '_results.json'), "w"))
def main(save_to, cost_name, learning_rate, momentum, num_epochs):
mlp = MLP([None], [784, 10],
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
mlp.initialize()
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
scores = mlp.apply(x)
batch_size = y.shape[0]
indices = tensor.arange(y.shape[0])
target_scores = tensor.set_subtensor(
tensor.zeros((batch_size, 10))[indices, y.flatten()], 1)
score_diff = scores - target_scores
# Logistic Regression
if cost_name == 'lr':
cost = Softmax().categorical_cross_entropy(y.flatten(), scores).mean()
# MSE
elif cost_name == 'mse':
cost = (score_diff**2).mean()
# Perceptron
elif cost_name == 'perceptron':
cost = (scores.max(axis=1) - scores[indices, y.flatten()]).mean()
# TLE
elif cost_name == 'minmin':
cost = abs(score_diff[indices, y.flatten()]).mean()
cost += abs(score_diff[indices, scores.argmax(axis=1)]).mean()
# TLEcut
elif cost_name == 'minmin_cut':
# Score of the groundtruth should be greater or equal than its target score
cost = tensor.maximum(0, -score_diff[indices, y.flatten()]).mean()
# Score of the prediction should be less or equal than its actual score
cost += tensor.maximum(0, score_diff[indices,
scores.argmax(axis=1)]).mean()
# TLE2
elif cost_name == 'minmin2':
cost = ((score_diff[tensor.arange(y.shape[0]), y.flatten()])**2).mean()
cost += ((score_diff[tensor.arange(y.shape[0]),
scores.argmax(axis=1)])**2).mean()
# Direct loss minimization
elif cost_name == 'direct':
epsilon = 0.1
cost = (-scores[indices,
(scores + epsilon * target_scores).argmax(axis=1)] +
scores[indices, scores.argmax(axis=1)]).mean()
cost /= epsilon
elif cost_name == 'svm':
cost = (scores[indices, (scores - 1 * target_scores).argmax(axis=1)] -
scores[indices, y.flatten()]).mean()
else:
raise ValueError("Unknown cost " + cost)
error_rate = MisclassificationRate().apply(y.flatten(), scores)
error_rate.name = 'error_rate'
cg = ComputationGraph([cost])
cost.name = 'cost'
mnist_train = MNIST(("train", ))
mnist_test = MNIST(("test", ))
if learning_rate == None:
learning_rate = 0.0001
if momentum == None:
momentum = 0.0
rule = Momentum(learning_rate=learning_rate, momentum=momentum)
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=rule)
extensions = [
Timing(),
FinishAfter(after_n_epochs=num_epochs),
DataStreamMonitoring([cost, error_rate],
Flatten(DataStream.default_stream(
mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, 500)),
which_sources=('features', )),
prefix="test"),
# CallbackExtension(
# lambda: rule.learning_rate.set_value(rule.learning_rate.get_value() * 0.9),
# after_epoch=True),
TrainingDataMonitoring([
cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm), rule.learning_rate
],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
Printing()
]
if BLOCKS_EXTRAS_AVAILABLE:
extensions.append(
Plot('MNIST example',
channels=[['test_cost', 'test_error_rate'],
['train_total_gradient_norm']]))
main_loop = MainLoop(algorithm,
Flatten(DataStream.default_stream(
mnist_train,
iteration_scheme=SequentialScheme(
mnist_train.num_examples, 50)),
which_sources=('features', )),
model=Model(cost),
extensions=extensions)
main_loop.run()
df = pandas.DataFrame.from_dict(main_loop.log, orient='index')
res = {
'cost': cost_name,
'learning_rate': learning_rate,
'momentum': momentum,
'train_cost': df.train_cost.iloc[-1],
'test_cost': df.test_cost.iloc[-1],
'best_test_cost': df.test_cost.min(),
'train_error': df.train_error_rate.iloc[-1],
'test_error': df.test_error_rate.iloc[-1],
'best_test_error': df.test_error_rate.min()
}
res = {
k: float(v) if isinstance(v, numpy.ndarray) else v
for k, v in res.items()
}
json.dump(res, sys.stdout)
sys.stdout.flush()
def main(config, tr_stream, dev_stream):
# Create Theano variables
logger.info('Creating theano variables')
source_char_seq = tensor.lmatrix('source_char_seq')
source_sample_matrix = tensor.btensor3('source_sample_matrix')
source_char_aux = tensor.bmatrix('source_char_aux')
source_word_mask = tensor.bmatrix('source_word_mask')
target_char_seq = tensor.lmatrix('target_char_seq')
target_char_aux = tensor.bmatrix('target_char_aux')
target_char_mask = tensor.bmatrix('target_char_mask')
target_sample_matrix = tensor.btensor3('target_sample_matrix')
target_word_mask = tensor.bmatrix('target_word_mask')
target_resample_matrix = tensor.btensor3('target_resample_matrix')
target_prev_char_seq = tensor.lmatrix('target_prev_char_seq')
target_prev_char_aux = tensor.bmatrix('target_prev_char_aux')
target_bos_idx = tr_stream.trg_bos
target_space_idx = tr_stream.space_idx['target']
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(config['src_vocab_size'], config['enc_embed'], config['src_dgru_nhids'],
config['enc_nhids'], config['src_dgru_depth'], config['bidir_encoder_depth'])
decoder = Decoder(config['trg_vocab_size'], config['dec_embed'], config['trg_dgru_nhids'], config['trg_igru_nhids'],
config['dec_nhids'], config['enc_nhids'] * 2, config['transition_depth'],
config['trg_igru_depth'],
config['trg_dgru_depth'], target_space_idx, target_bos_idx)
representation = encoder.apply(source_char_seq, source_sample_matrix, source_char_aux, source_word_mask)
cost = decoder.cost(representation, source_word_mask, target_char_seq, target_sample_matrix,
target_resample_matrix, target_char_aux, target_char_mask,
target_word_mask, target_prev_char_seq, target_prev_char_aux)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# Initialize model
logger.info('Initializing model')
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()
encoder.decimator.bidir_w.prototype.recurrent.weights_init = Orthogonal()
for layer_n in range(config['src_dgru_depth']):
encoder.decimator.dgru.transitions[layer_n].weights_init = Orthogonal()
for layer_n in range(config['bidir_encoder_depth']):
encoder.children[1 + layer_n].prototype.recurrent.weights_init = Orthogonal()
if config['trg_igru_depth'] == 1:
decoder.interpolator.igru.weights_init = Orthogonal()
else:
for layer_n in range(config['trg_igru_depth']):
decoder.interpolator.igru.transitions[layer_n].weights_init = Orthogonal()
for layer_n in range(config['trg_dgru_depth']):
decoder.interpolator.feedback_brick.dgru.transitions[layer_n].weights_init = Orthogonal()
for layer_n in range(config['transition_depth']):
decoder.transition.transitions[layer_n].weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
# 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(str(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(str(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 up training algorithm
logger.info("Initializing training algorithm")
# You could use 1e-4 in Adam, however manually decay will be faster.
# We decay it to 5e-4 when trained for about 30K
# then decay it to 2e-4 when trained for about 90K
# finally set it to 1e-4 when trained for about 180K
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(config['step_clipping']),
Adam(learning_rate=1e-3)]))
# Set extensions
logger.info("Initializing extensions")
# Extensions
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
train_monitor = CostCurve([cost, gradient_norm, step_norm], config=config, after_batch=True,
before_first_epoch=True, prefix='tra')
extensions = [
train_monitor, Timing(),
Printing(every_n_batches=config['print_freq']),
FinishAfter(after_n_batches=config['finish_after']),
CheckpointNMT(saveto=config['saveto'], dump_freq=config['dump_freq'], 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")
generated = decoder.generate(representation, source_word_mask)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[config['transition_depth']])) # generated[transition_depth] 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'], transition_depth=config['transition_depth'],
every_n_batches=config['sampling_freq'], src_vocab_size=config['src_vocab_size']))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# 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()
def main(config,
tr_stream,
dev_stream,
source_vocab,
target_vocab,
use_bokeh=False):
# add the tags from this function to the IMT datastream
# prediction function signature
# [target_suffix, source_mask, source, target_prefix_mask, target_prefix, target_suffix_mask]
prediction_function = get_prediction_function(exp_config=config)
tr_stream = Mapping(
tr_stream,
CallPredictionFunctionOnStream(prediction_function,
[1, 0, 5, 4, 7, 6]),
#tr_stream = Mapping(tr_stream, CallFunctionOnStream(prediction_function, [6, 1, 0, 5, 4, 7]),
add_sources=('predictions', 'orig_readouts', 'prediction_tags'))
# now datastream has 11 things
import ipdb
ipdb.set_trace()
# WORKING: call prediction function twice to get new readouts on predictions instead of reference suffs
# the only difference is the index of the suffix
tr_stream = Mapping(tr_stream,
CallPredictionFunctionOnStream(prediction_function,
[1, 0, 5, 4, 7, 8]),
add_sources=('dummy_predictions', 'readouts',
'dummy_prediction_tags'))
import ipdb
ipdb.set_trace()
# Create the prediction confidence model
# the first draft of this model uses the readout output (before the post-merge step) as the per-timestep state vector
# Create Theano variables
logger.info('Creating theano variables')
source_sentence = tensor.lmatrix('source')
source_sentence_mask = tensor.matrix('source_mask')
# Note that the _names_ are changed from normal NMT
# for IMT training, we use only the suffix as the reference
target_sentence = tensor.lmatrix('target_suffix')
target_sentence_mask = tensor.matrix('target_suffix_mask')
target_prefix = tensor.lmatrix('target_prefix')
target_prefix_mask = tensor.matrix('target_prefix_mask')
# symbolic variable which tags each timestep as GOOD/BAD
# Note: later this might be tags for a hypothesis i.e. from TER(p), right now the timesteps are actually determined by the reference
# By zipping the confidence model output with the reference, we get the model's confidence that this reference word
# will be predicted correctly
prediction_tags = tensor.matrix('prediction_tags')
readouts = tensor.tensor3('readouts')
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(config['src_vocab_size'],
config['enc_embed'], config['enc_nhids'])
decoder = NMTPrefixDecoder(config['trg_vocab_size'],
config['dec_embed'],
config['dec_nhids'],
config['enc_nhids'] * 2,
loss_function='cross_entropy')
# rename to match baseline NMT systems
decoder.name = 'decoder'
cost = decoder.confidence_cost(
encoder.apply(source_sentence, source_sentence_mask),
source_sentence_mask, target_sentence, target_sentence_mask,
target_prefix, target_prefix_mask, readouts, prediction_tags)
# WORKING: add l2 regularization
logger.info('Creating computational graph')
# working: implement cost for confidence model
cg = ComputationGraph(cost)
# INITIALIZATION
logger.info('Initializing model')
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()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
#cost_cg = ComputationGraph(cost)
if config['l2_reg']:
l2_reg_alpha = config['l2_reg_alpha']
model_weights = VariableFilter(roles=[WEIGHT])(cg.variables)
for W in model_weights:
cost = cost + (l2_reg_alpha * (W**2).sum())
# do we need to name the cost variable again?
cost.name = 'cost'
cg = ComputationGraph(cost)
# apply dropout for regularization
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
# this is the probability of dropping out, so you probably want to make it <=0.5
logger.info('Applying dropout')
dropout_inputs = [
x for x in cg.intermediary_variables if x.name in set([
'confidence_model1_apply_output',
'confidence_model2_apply_output',
'confidence_model3_apply_output'
])
]
# if x.name == 'maxout_apply_output']
# if x.name == 'maxout_apply_output']
cg = apply_dropout(cg, dropout_inputs, config['dropout'])
# WORKING: implement confidence -- remove all params except output model
cost_model = Model(cost)
model_params = cost_model.get_parameter_dict()
trainable_params = cg.parameters
import ipdb
ipdb.set_trace()
print('trainable params')
#params_to_remove = [model_params[k] for k in model_params.keys() if 'confidence' not in k]
#for p in params_to_remove:
# trainable_params.remove(p)
# target_embeddings = model.get_parameter_dict()['/target_recurrent_lm_with_alignments/target_embeddings.W']
# trainable_params.remove(source_embeddings)
# trainable_params.remove(target_embeddings)
# END WORKING: implement confidence -- remove all params except output model
# TODO: fixed dropout mask for recurrent params?
# 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)
# create the training directory, and copy this config there if directory doesn't exist
if not os.path.isdir(config['saveto']):
os.makedirs(config['saveto'])
shutil.copy(config['config_file'], config['saveto'])
# Set extensions
logger.info("Initializing extensions")
extensions = [
FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([cost], after_batch=True),
# TrainingDataMonitoring(trainable_params, after_batch=True),
# Printing(after_batch=True),
CheckpointNMT(config['saveto'], every_n_batches=config['save_freq'])
]
# WORKING: confidence prediction
#monitor everything that could possibly be relevant
# Set up the sampling graph for validation during training
# Theano variables for the sampling graph
# Note this also loads the model parameters
sampling_vars = load_params_and_get_beam_search(config,
encoder=encoder,
decoder=decoder)
beam_search, search_model, samples, sampling_input, sampling_prefix = sampling_vars
#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=config['sampling_freq'],
# src_vocab=source_vocab,
# trg_vocab=target_vocab,
# src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
#if config['bleu_script'] is not None:
# logger.info("Building bleu validator")
# extensions.append(
# BleuValidator(sampling_input, sampling_prefix, samples=samples, config=config,
# model=search_model, data_stream=dev_stream,
# src_vocab=source_vocab,
# trg_vocab=target_vocab,
# normalize=config['normalized_bleu'],
# every_n_batches=config['bleu_val_freq']))
# TODO: add first-word accuracy validation
# TODO: add IMT meteor early stopping
#if config.get('imt_f1_validation', None) is not None:
# logger.info("Building imt F1 validator")
# extensions.append(
# IMT_F1_Validator(sampling_input, sampling_prefix,
# samples=samples,
# config=config,
# model=search_model, data_stream=dev_stream,
# src_vocab=source_vocab,
# trg_vocab=target_vocab,
# normalize=config['normalized_bleu'],
# every_n_batches=config['bleu_val_freq']))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# TODO: hacking here: get the predictions of the confidence model using the `readouts` source of the data_stream
# Note that the parameters of this model must be pretrained, otherwise this doesn't make sense
# confidence_predictions = decoder.get_confidence(readouts)
# confidence_prediction_model = Model(confidence_predictions)
#
# confidence_param_values = LoadNMT.load_parameter_values(config['confidence_saved_parameters'], brick_delimiter=None)
# LoadNMT.set_model_parameters(confidence_prediction_model, confidence_param_values)
#
# confidence_prediction_func = confidence_prediction_model.get_theano_function()
# import ipdb; ipdb.set_trace()
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
# Plot(config['model_save_directory'], channels=[['decoder_confidence_cost_cost']],
Plot(config['model_save_directory'],
channels=[['cost']],
every_n_batches=10))
# Set up training algorithm
logger.info("Initializing training algorithm")
# WORKING: implement confidence model
# if there is dropout or random noise, we need to use the output of the modified graph
algorithm = GradientDescent(
cost=cg.outputs[0],
parameters=trainable_params,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]),
# eval(config['step_rule'])(), RemoveNotFinite()]),
# step_rule=CompositeRule([StepClipping(10.0), Scale(0.01)]),
on_unused_sources='warn')
#if config['dropout'] < 1.0:
# algorithm = GradientDescent(
# cost=cg.outputs[0], parameters=trainable_params,
# step_rule=CompositeRule([StepClipping(config['step_clipping']),
# eval(config['step_rule'])(), RemoveNotFinite()]),
# # step_rule=CompositeRule([StepClipping(10.0), Scale(0.01)]),
# on_unused_sources='warn'
# )
#else:
# algorithm = GradientDescent(
# cost=cost, parameters=cg.parameters,
# step_rule=CompositeRule([StepClipping(config['step_clipping']),
# eval(config['step_rule'])()]),
# on_unused_sources='warn'
# )
# END WORKING: implement confidence model
import ipdb
ipdb.set_trace()
# enrich the logged information
extensions.append(Timing(every_n_batches=100))
# WORKING: debugging confidence
# get theano function from model
# WORKING: implement word-level confidence cost
# @application(inputs=['representation', 'source_sentence_mask',
# 'target_sentence_mask', 'target_sentence', 'target_prefix_mask', 'target_prefix'],
# outputs=['cost'])
# def confidence_cost(self, representation, source_sentence_mask,
# target_sentence, target_sentence_mask, target_prefix, target_prefix_mask):
logger.info('Creating theano variables')
# WORKING: 26.9.16 -- get confidence outputs directly from (source, prefix, suffix) inputs
# This is equivalent to forced alignment --> confidence scores
# Note: but this section should probably be in "evaluate" mode, not here in "train"
# source_sentence = tensor.lmatrix('source')
# source_sentence_mask = tensor.matrix('source_mask')
# Note that the _names_ are changed from normal NMT
# for IMT training, we use only the suffix as the reference
#target_sentence = tensor.lmatrix('target_suffix')
#target_sentence_mask = tensor.matrix('target_suffix_mask')
# TODO: change names back to *_suffix, there is currently a theano function name error
# TODO: in the GradientDescent Algorithm
#target_prefix = tensor.lmatrix('target_prefix')
#target_prefix_mask = tensor.matrix('target_prefix_mask')
# confidence_output = decoder.confidence_cost(
# encoder.apply(source_sentence, source_sentence_mask),
# source_sentence_mask, target_sentence, target_sentence_mask,
# target_prefix, target_prefix_mask)
# confidence_model = Model(confidence_output)
# t_cost_func = confidence_model.get_theano_function()
# inputs
# [source_mask, source, target_prefix_mask, target_prefix, target_suffix_mask, target_suffix]
#import ipdb;ipdb.set_trace()
# get the right args from the datastream
# TODO: just print source, prefix, suffix, prediction, correct to new files -- this makes sure everything is aligned
# OUTPUT_DIR = '/media/1tb_drive/imt_models/word_prediction_accuracy_experiments/en-de/exp_1'
# for the_file in os.listdir(OUTPUT_DIR):
# file_path = os.path.join(OUTPUT_DIR, the_file)
# try:
# if os.path.isfile(file_path):
# os.unlink(file_path)
# except Exception as e:
# print(e)
#
# def write_file_truncate_mask(filename, data, mask, mode='a'):
# ''' data is list of list '''
#
# assert len(data) == len(mask)
# with codecs.open(filename, mode, encoding='utf8') as out:
# for l, m in zip(data, mask):
# output = u' '.join(l[:int(m.sum())]) + u'\n'
# out.write(output)
# logger.info('Wrote file: {}'.format(filename))
#
#
# target_ivocab = {k:v.decode('utf8') for v,k in target_vocab.items()}
# source_ivocab = {k:v.decode('utf8') for v,k in source_vocab.items()}
# import ipdb; ipdb.set_trace()
# tag_ivocab = {1: 'True', 0: 'False'}
#
# test_iter = tr_stream.get_epoch_iterator()
# it = 0
# for t_source, t_source_mask, t_target, t_target_mask, t_target_prefix, t_target_prefix_mask, t_target_suffix, t_target_suffix_mask in test_iter:
# if it <= 1000:
# it += 1
# t_cost = t_cost_func(t_source_mask, t_source, t_target_prefix_mask, t_target_prefix, t_target_suffix_mask, t_target_suffix)
# readouts = t_cost[0]
# preds = readouts.argmax(axis=2)
# correct = preds.T == t_target_suffix
#
#
# source_output = os.path.join(OUTPUT_DIR,'sources.en')
# prefix_output = os.path.join(OUTPUT_DIR,'prefixes.de')
# suffix_output = os.path.join(OUTPUT_DIR,'suffixes.de')
# prediction_output = os.path.join(OUTPUT_DIR,'predictions.de')
# correct_output = os.path.join(OUTPUT_DIR,'prefix_word_prediction_acc.out')
#
# source_text = [[source_ivocab[w] for w in s] for s in t_source]
# prefix_text = [[target_ivocab[w] for w in s] for s in t_target_prefix]
# suffix_text = [[target_ivocab[w] for w in s] for s in t_target_suffix]
# pred_text = [[target_ivocab[w] for w in s] for s in preds.T]
# correct_text = [[tag_ivocab[w] for w in s] for s in correct]
#
#
# for triple in zip([source_output, prefix_output, suffix_output, prediction_output, correct_output],
# [source_text, prefix_text, suffix_text, pred_text, correct_text],
# [t_source_mask, t_target_prefix_mask, t_target_suffix_mask, t_target_suffix_mask, t_target_suffix_mask]):
# write_file_truncate_mask(*triple)
# else:
# break
#
# import ipdb; ipdb.set_trace()
#t_cost = t_cost_func(t_source, t_target_prefix)
#t_cost = t_cost_func(t_target_suffix, t_source_mask, t_source, t_target_prefix_mask, t_target_prefix, t_target_suffix_mask)
#t_cost = t_cost_func(t_source_mask, t_source, t_target_prefix_mask, t_target_prefix, t_target_suffix_mask, t_target_suffix)
# return confidence_cost, flat_y, confidence_logits, readouts
#predictions = t_cost[0].argmax(axis=2)
# TODO: next step -- print gradients and weights during training find out where nan is coming from
# TODO: look at the gradient of this function with respect to parameters? -- see here: http://deeplearning.net/software/theano/tutorial/gradients.html
# TODO: function which adds right/wrong tags for model predictions to the datastream. In this case we can learn a simple linear model as a baseline
# TODO: print predictions for each batch for each timestep to file -- _dont shuffle_ so that we get the right order
# import ipdb;ipdb.set_trace()
# from blocks reverse_words example
# observables = [
# cost, min_energy, max_energy, mean_activation,
# batch_size, max_length, cost_per_character,
# algorithm.total_step_norm, algorithm.total_gradient_norm]
# for name, parameter in trainable_params.items():
# observables.append(parameter.norm(2).copy(name + "_norm"))
# observables.append(algorithm.gradients[parameter].norm(2).copy(
# name + "_grad_norm"))
for i, (k, v) in enumerate(algorithm.updates):
v.name = k.name + '_{}'.format(i)
aux_vars = [v for v in cg.auxiliary_variables[-3:]]
# import ipdb; ipdb.set_trace()
extensions.extend([
TrainingDataMonitoring([cost], after_batch=True),
# TrainingDataMonitoring([v for k,v in algorithm.updates[:2]], after_batch=True),
# TrainingDataMonitoring(aux_vars, after_batch=True),
# TrainingDataMonitoring(trainable_params, after_batch=True),
Printing(after_batch=True)
])
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(model=training_model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions)
import ipdb
ipdb.set_trace()
# Train!
main_loop.run()
def main():
nclasses = 27
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--length", type=int, default=180)
parser.add_argument("--num-epochs", type=int, default=100)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--learning-rate", type=float, default=1e-3)
parser.add_argument("--epsilon", type=float, default=1e-5)
parser.add_argument("--num-hidden", type=int, default=1000)
parser.add_argument("--baseline", action="store_true")
parser.add_argument("--initialization", choices="identity glorot orthogonal uniform".split(), default="identity")
parser.add_argument("--initial-gamma", type=float, default=1e-1)
parser.add_argument("--initial-beta", type=float, default=0)
parser.add_argument("--cluster", action="store_true")
parser.add_argument("--activation", choices=list(activations.keys()), default="tanh")
parser.add_argument("--optimizer", choices="sgdmomentum adam rmsprop", default="rmsprop")
parser.add_argument("--continue-from")
parser.add_argument("--evaluate")
parser.add_argument("--dump-hiddens")
args = parser.parse_args()
np.random.seed(args.seed)
blocks.config.config.default_seed = args.seed
if args.continue_from:
from blocks.serialization import load
main_loop = load(args.continue_from)
main_loop.run()
sys.exit(0)
graphs, extensions, updates = construct_graphs(args, nclasses)
### optimization algorithm definition
if args.optimizer == "adam":
optimizer = Adam(learning_rate=args.learning_rate)
elif args.optimizer == "rmsprop":
optimizer = RMSProp(learning_rate=args.learning_rate, decay_rate=0.9)
elif args.optimizer == "sgdmomentum":
optimizer = Momentum(learning_rate=args.learning_rate, momentum=0.99)
step_rule = CompositeRule([StepClipping(1.0), optimizer])
algorithm = GradientDescent(
cost=graphs["training"].outputs[0], parameters=graphs["training"].parameters, step_rule=step_rule
)
algorithm.add_updates(updates["training"])
model = Model(graphs["training"].outputs[0])
extensions = extensions["training"] + extensions["inference"]
# step monitor
step_channels = []
step_channels.extend(
[
algorithm.steps[param].norm(2).copy(name="step_norm:%s" % name)
for name, param in model.get_parameter_dict().items()
]
)
step_channels.append(algorithm.total_step_norm.copy(name="total_step_norm"))
step_channels.append(algorithm.total_gradient_norm.copy(name="total_gradient_norm"))
step_channels.extend(graphs["training"].outputs)
logger.warning("constructing training data monitor")
extensions.append(TrainingDataMonitoring(step_channels, prefix="iteration", after_batch=True))
# parameter monitor
extensions.append(
DataStreamMonitoring(
[param.norm(2).copy(name="parameter.norm:%s" % name) for name, param in model.get_parameter_dict().items()],
data_stream=None,
after_epoch=True,
)
)
validation_interval = 500
# performance monitor
for situation in "training inference".split():
if situation == "inference" and not args.evaluate:
# save time when we don't need the inference graph
continue
for which_set in "train valid test".split():
logger.warning("constructing %s %s monitor" % (which_set, situation))
channels = list(graphs[situation].outputs)
extensions.append(
DataStreamMonitoring(
channels,
prefix="%s_%s" % (which_set, situation),
every_n_batches=validation_interval,
data_stream=get_stream(
which_set=which_set, batch_size=args.batch_size, num_examples=10000, length=args.length
),
)
)
extensions.extend(
[
TrackTheBest("valid_training_error_rate", "best_valid_training_error_rate"),
DumpBest("best_valid_training_error_rate", "best.zip"),
FinishAfter(after_n_epochs=args.num_epochs),
# FinishIfNoImprovementAfter("best_valid_error_rate", epochs=50),
Checkpoint("checkpoint.zip", on_interrupt=False, every_n_epochs=1, use_cpickle=True),
DumpLog("log.pkl", after_epoch=True),
]
)
if not args.cluster:
extensions.append(ProgressBar())
extensions.extend([Timing(), Printing(every_n_batches=validation_interval), PrintingTo("log")])
main_loop = MainLoop(
data_stream=get_stream(which_set="train", batch_size=args.batch_size, length=args.length, augment=True),
algorithm=algorithm,
extensions=extensions,
model=model,
)
if args.dump_hiddens:
dump_hiddens(args, main_loop)
return
if args.evaluate:
evaluate(args, main_loop)
return
main_loop.run()
def main(mode, config, use_bokeh=False):
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(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)
if mode == "train":
# 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')
# Get training and development set streams
tr_stream = get_tr_stream(**config)
dev_stream = get_dev_stream(**config)
# Get cost of the model
cost = decoder.cost(
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')
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()
encoder.bidir.prototype.weights_init = Orthogonal()
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
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'])
# 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")
extensions = [
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 or config['bleu_script'] is not None:
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]))
# 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=config['sampling_freq'],
src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
if config['bleu_script'] is not None:
logger.info("Building bleu validator")
extensions.append(
BleuValidator(sampling_input,
samples=samples,
config=config,
model=search_model,
data_stream=dev_stream,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq']))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot('Cs-En',
channels=[['decoder_cost_cost']],
after_batch=True))
# 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()
elif mode == 'translate':
# Create Theano variables
logger.info('Creating theano variables')
sampling_input = tensor.lmatrix('source')
# Get test set stream
test_stream = get_dev_stream(config['test_set'], config['src_vocab'],
config['src_vocab_size'],
config['unk_id'])
ftrans = open(config['test_set'] + '.trans.out', 'w')
# Helper utilities
sutils = SamplingBase()
unk_idx = config['unk_id']
src_eos_idx = config['src_vocab_size'] - 1
trg_eos_idx = config['trg_vocab_size'] - 1
# Get beam search
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
generated = decoder.generate(sampling_input, sampling_representation)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
beam_search = BeamSearch(samples=samples)
logger.info("Loading the model..")
model = Model(generated)
loader = LoadNMT(config['saveto'])
loader.set_model_parameters(model, loader.load_parameters())
# Get target vocabulary
trg_vocab = _ensure_special_tokens(pickle.load(
open(config['trg_vocab'])),
bos_idx=0,
eos_idx=trg_eos_idx,
unk_idx=unk_idx)
trg_ivocab = {v: k for k, v in trg_vocab.items()}
logger.info("Started translation: ")
total_cost = 0.0
for i, line in enumerate(test_stream.get_epoch_iterator()):
seq = sutils._oov_to_unk(line[0], config['src_vocab_size'],
unk_idx)
input_ = numpy.tile(seq, (config['beam_size'], 1))
# draw sample, checking to ensure we don't get an empty string back
trans, costs = \
beam_search.search(
input_values={sampling_input: input_},
max_length=3*len(seq), eol_symbol=src_eos_idx,
ignore_first_eol=True)
# normalize costs according to the sequence lengths
if config['normalized_bleu']:
lengths = numpy.array([len(s) for s in trans])
costs = costs / lengths
best = numpy.argsort(costs)[0]
try:
total_cost += costs[best]
trans_out = trans[best]
# convert idx to words
trans_out = sutils._idx_to_word(trans_out, trg_ivocab)
except ValueError:
logger.info(
"Can NOT find a translation for line: {}".format(i + 1))
trans_out = '<UNK>'
print(trans_out, file=ftrans)
if i != 0 and i % 100 == 0:
logger.info("Translated {} lines of test set...".format(i))
logger.info("Total cost of the test: {}".format(total_cost))
ftrans.close()
def train(step_rule, label_dim, state_dim, epochs, seed, dropout, test_cost,
experiment_path, features, weight_noise, to_watch, patience,
batch_size, batch_norm, **kwargs):
print '.. TIMIT experiment'
print '.. arguments:', ' '.join(sys.argv)
t0 = time.time()
# ------------------------------------------------------------------------
# Streams
rng = np.random.RandomState(seed)
stream_args = dict(rng=rng, batch_size=batch_size)
print '.. initializing iterators'
train_dataset = Timit('train', features=features)
train_stream = construct_stream(train_dataset, **stream_args)
dev_dataset = Timit('dev', features=features)
dev_stream = construct_stream(dev_dataset, **stream_args)
test_dataset = Timit('test', features=features)
test_stream = construct_stream(test_dataset, **stream_args)
update_stream = construct_stream(train_dataset,
n_batches=100,
**stream_args)
phone_dict = train_dataset.get_phoneme_dict()
phoneme_dict = {
k: phone_to_phoneme_dict[v] if v in phone_to_phoneme_dict else v
for k, v in phone_dict.iteritems()
}
ind_to_phoneme = {v: k for k, v in phoneme_dict.iteritems()}
eol_symbol = ind_to_phoneme['<STOP>']
# ------------------------------------------------------------------------
# Graph
print '.. building model'
x = T.tensor3('features')
y = T.matrix('phonemes')
input_mask = T.matrix('features_mask')
output_mask = T.matrix('phonemes_mask')
theano.config.compute_test_value = 'off'
x.tag.test_value = np.random.randn(100, 24, 123).astype(floatX)
y.tag.test_value = np.ones((30, 24), dtype=floatX)
input_mask.tag.test_value = np.ones((100, 24), dtype=floatX)
output_mask.tag.test_value = np.ones((30, 24), dtype=floatX)
seq_len = 100
input_dim = 123
activation = Tanh()
recurrent_init = IdentityInit(0.99)
rec1 = TimLSTM(not batch_norm,
input_dim,
state_dim,
activation,
name='LSTM')
rec1.initialize()
l1 = Linear(state_dim,
label_dim + 1,
name='out_linear',
weights_init=Orthogonal(),
biases_init=Constant(0.0))
l1.initialize()
o1 = rec1.apply(x)
y_hat_o = l1.apply(o1)
shape = y_hat_o.shape
y_hat = Softmax().apply(y_hat_o.reshape((-1, shape[-1]))).reshape(shape)
y_mask = output_mask
y_hat_mask = input_mask
# ------------------------------------------------------------------------
# Costs and Algorithm
ctc_cost = T.sum(
ctc.cpu_ctc_th(y_hat_o, T.sum(y_hat_mask, axis=0), y + T.ones_like(y),
T.sum(y_mask, axis=0)))
batch_cost = ctc_cost.copy(name='batch_cost')
bs = y.shape[1]
cost_train = aggregation.mean(batch_cost, bs).copy("sequence_cost")
cost_per_character = aggregation.mean(
batch_cost, output_mask.sum()).copy("character_cost")
cg_train = ComputationGraph(cost_train)
model = Model(cost_train)
train_cost_per_character = aggregation.mean(
cost_train, output_mask.sum()).copy("train_character_cost")
algorithm = GradientDescent(step_rule=step_rule,
cost=cost_train,
parameters=cg_train.parameters,
on_unused_sources='warn')
# ------------------------------------------------------------------------
# Monitoring and extensions
parameters = model.get_parameter_dict()
observed_vars = [
cost_train, train_cost_per_character,
aggregation.mean(algorithm.total_gradient_norm)
]
for name, param in parameters.iteritems():
observed_vars.append(param.norm(2).copy(name + "_norm"))
observed_vars.append(
algorithm.gradients[param].norm(2).copy(name + "_grad_norm"))
train_monitor = TrainingDataMonitoring(variables=observed_vars,
prefix="train",
after_epoch=True)
dev_monitor = DataStreamMonitoring(
variables=[cost_train, cost_per_character],
data_stream=dev_stream,
prefix="dev")
train_ctc_monitor = CTCMonitoring(x,
input_mask,
y_hat,
eol_symbol,
train_stream,
prefix='train',
every_n_epochs=1,
before_training=True,
phoneme_dict=phoneme_dict,
black_list=black_list,
train=True)
dev_ctc_monitor = CTCMonitoring(x,
input_mask,
y_hat,
eol_symbol,
dev_stream,
prefix='dev',
every_n_epochs=1,
phoneme_dict=phoneme_dict,
black_list=black_list)
extensions = []
if 'load_path' in kwargs:
extensions.append(Load(kwargs['load_path']))
extensions.extend([
FinishAfter(after_n_epochs=epochs), train_monitor, dev_monitor,
train_ctc_monitor, dev_ctc_monitor
])
if test_cost:
test_monitor = DataStreamMonitoring(
variables=[cost_train, cost_per_character],
data_stream=test_stream,
prefix="test")
test_ctc_monitor = CTCMonitoring(x,
input_mask,
y_hat,
eol_symbol,
test_stream,
prefix='test',
every_n_epochs=1,
phoneme_dict=phoneme_dict,
black_list=black_list)
extensions.append(test_monitor)
extensions.append(test_ctc_monitor)
#if not os.path.exists(experiment_path):
# os.makedirs(experiment_path)
#best_path = os.path.join(experiment_path, 'best/')
#if not os.path.exists(best_path):
# os.mkdir(best_path)
#best_path = os.path.join(best_path, 'model.bin')
extensions.append(EarlyStopping(to_watch, patience, '/dev/null'))
extensions.extend([ProgressBar(), Printing()])
# ------------------------------------------------------------------------
# Main Loop
main_loop = MainLoop(model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
print "Building time: %f" % (time.time() - t0)
# if write_predictions:
# with open('predicted.txt', 'w') as f_pred:
# with open('targets.txt', 'w') as f_targets:
# evaluator = CTCEvaluator(
# eol_symbol, x, input_mask, y_hat, phoneme_dict, black_list)
# evaluator.evaluate(dev_stream, file_pred=f_pred,
# file_targets=f_targets)
# return
main_loop.run()
parameters=cg.parameters,
step_rule=CompositeRule(step_rules))
from blocks.extensions import Timing, FinishAfter, Printing, ProgressBar
from blocks.extensions.monitoring import TrainingDataMonitoring
from fuel.streams import DataStream
from fuel.schemes import SequentialScheme
from blocks.main_loop import MainLoop
from blocks.extensions.saveload import Checkpoint
from blocks.model import Model
main_loop = MainLoop(algorithm=algorithm,
data_stream=DataStream.default_stream(
dataset=train_dataset,
iteration_scheme=SequentialScheme(
train_dataset.num_examples, batch_size=10)),
model=Model(y_est),
extensions=[
Timing(),
FinishAfter(after_n_epochs=200),
TrainingDataMonitoring(variables=[cost],
prefix="train",
after_epoch=True),
Printing(),
ProgressBar(),
Checkpoint(path="./checkpoint.zip")
])
main_loop.run()
def main(mode, config, use_bokeh=False):
# Construct model
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(config['src_vocab_size'],
config['enc_embed'], config['enc_nhids'])
topical_transformer = topicalq_transformer(
config['source_topic_vocab_size'], config['topical_embedding_dim'],
config['enc_nhids'], config['topical_word_num'], config['batch_size'])
decoder = Decoder(vocab_size=config['trg_vocab_size'],
topicWord_size=config['trg_topic_vocab_size'],
embedding_dim=config['dec_embed'],
topical_dim=config['topical_embedding_dim'],
state_dim=config['dec_nhids'],
representation_dim=config['enc_nhids'] * 2,
match_function=config['match_function'],
use_doubly_stochastic=config['use_doubly_stochastic'],
lambda_ds=config['lambda_ds'],
use_local_attention=config['use_local_attention'],
window_size=config['window_size'],
use_step_decay_cost=config['use_step_decay_cost'],
use_concentration_cost=config['use_concentration_cost'],
lambda_ct=config['lambda_ct'],
use_stablilizer=config['use_stablilizer'],
lambda_st=config['lambda_st'])
# here attended dim (representation_dim) of decoder is 2*enc_nhinds
# because the context given by the encoder is a bidirectional context
if mode == "train":
# 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')
target_topic_sentence = tensor.lmatrix('target_topic')
target_topic_binary_sentence = tensor.lmatrix('target_binary_topic')
#target_topic_sentence_mask=tensor.lmatrix('target_topic_mask');
sampling_input = tensor.lmatrix('input')
source_topical_word = tensor.lmatrix('source_topical')
source_topical_mask = tensor.matrix('source_topical_mask')
topic_embedding = topical_transformer.apply(source_topical_word)
# Get training and development set streams
tr_stream = get_tr_stream_with_topic_target(**config)
#dev_stream = get_dev_tr_stream_with_topic_target(**config)
# Get cost of the model
representations = encoder.apply(source_sentence, source_sentence_mask)
tw_representation = topical_transformer.look_up.apply(
source_topical_word.T)
content_embedding = representations[0, :,
(representations.shape[2] / 2):]
cost = decoder.cost(representations, source_sentence_mask,
tw_representation, source_topical_mask,
target_sentence, target_sentence_mask,
target_topic_sentence,
target_topic_binary_sentence, topic_embedding,
content_embedding)
logger.info('Creating computational graph')
perplexity = tensor.exp(cost)
perplexity.name = 'perplexity'
cg = ComputationGraph(cost)
costs_computer = function([
target_sentence, target_sentence_mask, source_sentence,
source_sentence_mask, source_topical_word, target_topic_sentence,
target_topic_binary_sentence
], (perplexity),
on_unused_input='ignore')
# Initialize model
logger.info('Initializing model')
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()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
topical_transformer.weights_init = IsotropicGaussian(
config['weight_scale'])
topical_transformer.biases_init = Constant(0)
topical_transformer.push_allocation_config()
#don't know whether the initialize is for
topical_transformer.look_up.weights_init = Orthogonal()
topical_transformer.transformer.weights_init = Orthogonal()
topical_transformer.initialize()
word_topical_embedding = cPickle.load(
open(config['topical_embeddings'], 'rb'))
np_word_topical_embedding = numpy.array(word_topical_embedding,
dtype='float32')
topical_transformer.look_up.W.set_value(np_word_topical_embedding)
topical_transformer.look_up.W.tag.role = []
# 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'])
# 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")
extensions = [
FinishAfter(after_n_batches=config['finish_after']),
TrainingDataMonitoring([perplexity], after_batch=True),
CheckpointNMT(config['saveto'],
config['model_name'],
every_n_batches=config['save_freq'])
]
# # Set up beam search and sampling computation graphs if necessary
# if config['hook_samples'] >= 1 or config['bleu_script'] is not None:
# 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]))
#
# # Add sampling
# if config['hook_samples'] >= 1:
# logger.info("Building sampler")
# extensions.append(
# Sampler(model=search_model, data_stream=tr_stream,
# model_name=config['model_name'],
# hook_samples=config['hook_samples'],
# every_n_batches=config['sampling_freq'],
# src_vocab_size=config['src_vocab_size']))
#
# # Add early stopping based on bleu
# if False:
# logger.info("Building bleu validator")
# extensions.append(
# BleuValidator(sampling_input, samples=samples, config=config,
# model=search_model, data_stream=dev_stream,
# normalize=config['normalized_bleu'],
# every_n_batches=config['bleu_val_freq'],
# n_best=3,
# track_n_models=6))
#
# logger.info("Building perplexity validator")
# extensions.append(
# pplValidation( config=config,
# model=costs_computer, data_stream=dev_stream,
# model_name=config['model_name'],
# every_n_batches=config['sampling_freq']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot('Cs-En',
channels=[['decoder_cost_cost']],
after_batch=True))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
initial_learning_rate = config['initial_learning_rate']
log_path = os.path.join(config['saveto'], 'log')
if config['reload'] and os.path.exists(log_path):
with open(log_path, 'rb') as source:
log = cPickle.load(source)
last = max(log.keys()) - 1
if 'learning_rate' in log[last]:
initial_learning_rate = log[last]['learning_rate']
# Set up training algorithm
logger.info("Initializing training algorithm")
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
Scale(initial_learning_rate),
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]),
on_unused_sources='ignore')
_learning_rate = algorithm.step_rule.components[0].learning_rate
if config['learning_rate_decay']:
extensions.append(
LearningRateHalver(record_name='validation_cost',
comparator=lambda x, y: x > y,
learning_rate=_learning_rate,
patience_default=3))
else:
extensions.append(OldModelRemover(saveto=config['saveto']))
if config['learning_rate_grow']:
extensions.append(
LearningRateDoubler(record_name='validation_cost',
comparator=lambda x, y: x < y,
learning_rate=_learning_rate,
patience_default=3))
extensions.append(
SimplePrinting(config['model_name'], after_batch=True))
# 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()
elif mode == 'translate':
logger.info('Creating theano variables')
sampling_input = tensor.lmatrix('source')
source_topical_word = tensor.lmatrix('source_topical')
tw_vocab_overlap = tensor.lmatrix('tw_vocab_overlap')
tw_vocab_overlap_matrix = cPickle.load(
open(config['tw_vocab_overlap'], 'rb'))
tw_vocab_overlap_matrix = numpy.array(tw_vocab_overlap_matrix,
dtype='int32')
#tw_vocab_overlap=shared(tw_vocab_overlap_matrix);
topic_embedding = topical_transformer.apply(source_topical_word)
sutils = SamplingBase()
unk_idx = config['unk_id']
src_eos_idx = config['src_vocab_size'] - 1
trg_eos_idx = config['trg_vocab_size'] - 1
trg_vocab = _ensure_special_tokens(cPickle.load(
open(config['trg_vocab'], 'rb')),
bos_idx=0,
eos_idx=trg_eos_idx,
unk_idx=unk_idx)
trg_ivocab = {v: k for k, v in trg_vocab.items()}
logger.info("Building sampling model")
sampling_representation = encoder.apply(
sampling_input, tensor.ones(sampling_input.shape))
topic_embedding = topical_transformer.apply(source_topical_word)
tw_representation = topical_transformer.look_up.apply(
source_topical_word.T)
content_embedding = sampling_representation[0, :, (
sampling_representation.shape[2] / 2):]
generated = decoder.generate(sampling_input,
sampling_representation,
tw_representation,
topical_embedding=topic_embedding,
content_embedding=content_embedding)
_, samples = VariableFilter(
bricks=[decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
beam_search = BeamSearch(samples=samples)
logger.info("Loading the model..")
model = Model(generated)
#loader = LoadNMT(config['saveto'])
loader = LoadNMT(config['validation_load'])
loader.set_model_parameters(model, loader.load_parameters_default())
logger.info("Started translation: ")
test_stream = get_dev_stream_with_topicalq(**config)
ts = test_stream.get_epoch_iterator()
rts = open(config['val_set_source']).readlines()
ftrans_original = open(config['val_output_orig'], 'w')
saved_weights = []
total_cost = 0.0
pbar = ProgressBar(max_value=len(rts)).start()
for i, (line, line_raw) in enumerate(zip(ts, rts)):
trans_in = line_raw.split()
seq = sutils._oov_to_unk(line[0], config['src_vocab_size'],
unk_idx)
seq1 = line[1]
input_topical = numpy.tile(seq1, (config['beam_size'], 1))
input_ = numpy.tile(seq, (config['beam_size'], 1))
# draw sample, checking to ensure we don't get an empty string back
trans, costs, attendeds, weights = \
beam_search.search(
input_values={sampling_input: input_,source_topical_word:input_topical,tw_vocab_overlap:tw_vocab_overlap_matrix},
tw_vocab_overlap=tw_vocab_overlap_matrix,
max_length=3*len(seq), eol_symbol=trg_eos_idx,
ignore_first_eol=True)
# normalize costs according to the sequence lengths
if config['normalized_bleu']:
lengths = numpy.array([len(s) for s in trans])
costs = costs / lengths
best = numpy.argsort(costs)[0]
try:
total_cost += costs[best]
trans_out = trans[best]
weight = weights[best][:, :len(trans_in)]
trans_out = sutils._idx_to_word(trans_out, trg_ivocab)
except ValueError:
logger.info(
"Can NOT find a translation for line: {}".format(i + 1))
trans_out = '<UNK>'
saved_weights.append(weight)
print(' '.join(trans_out), file=ftrans_original)
pbar.update(i + 1)
pbar.finish()
logger.info("Total cost of the test: {}".format(total_cost))
cPickle.dump(saved_weights, open(config['attention_weights'], 'wb'))
ftrans_original.close()
# ap = afterprocesser(config)
# ap.main()
elif mode == 'score':
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')
target_topic_sentence = tensor.lmatrix('target_topic')
target_topic_binary_sentence = tensor.lmatrix('target_binary_topic')
source_topical_word = tensor.lmatrix('source_topical')
topic_embedding = topical_transformer.apply(source_topical_word)
# Get cost of the model
representations = encoder.apply(source_sentence, source_sentence_mask)
costs = decoder.cost(representations, source_sentence_mask,
target_sentence, target_sentence_mask,
target_topic_sentence,
target_topic_binary_sentence, topic_embedding)
config['batch_size'] = 1
config['sort_k_batches'] = 1
# Get test set stream
test_stream = get_tr_stream_with_topic_target(**config)
logger.info("Building sampling model")
logger.info("Loading the model..")
model = Model(costs)
loader = LoadNMT(config['validation_load'])
loader.set_model_parameters(model, loader.load_parameters_default())
costs_computer = function([
target_sentence, target_sentence_mask, source_sentence,
source_sentence_mask, source_topical_word, target_topic_sentence,
target_topic_binary_sentence
], (costs),
on_unused_input='ignore')
iterator = test_stream.get_epoch_iterator()
scores = []
att_weights = []
for i, (src, src_mask, trg, trg_mask, te, te_mask, tt, tt_mask, tb,
tb_mask) in enumerate(iterator):
costs = costs_computer(*[trg, trg_mask, src, src_mask, te, tt, tb])
cost = costs.sum()
print(i, cost)
scores.append(cost)
print(sum(scores) / 10007)
cg = ComputationGraph(cost)
monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables))
valid_monitored = monitored
if hasattr(model, 'valid_cost'):
valid_cost = model.valid_cost(**inputs)
valid_cg = ComputationGraph(valid_cost)
valid_monitored = set([valid_cost] + VariableFilter(
roles=[roles.COST])(valid_cg.variables))
if hasattr(config, 'dropout') and config.dropout < 1.0:
cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
if hasattr(config, 'noise') and config.noise > 0.0:
cg = apply_noise(cg, config.noise_inputs(cg), config.noise)
cost = cg.outputs[0]
cg = Model(cost)
logger.info('# Parameter shapes:')
parameters_size = 0
for value in cg.parameters:
logger.info(' %20s %s' % (value.get_value().shape, value.name))
parameters_size += reduce(operator.mul, value.get_value().shape, 1)
logger.info('Total number of parameters: %d in %d matrices' %
(parameters_size, len(cg.parameters)))
if hasattr(config, 'step_rule'):
step_rule = config.step_rule
else:
step_rule = AdaDelta()
logger.info("Fuel seed: %d" % fuel.config.default_seed)
def main(mode, save_path, num_batches, data_path=None):
reverser = WordReverser(100, len(char2code), name="reverser")
if mode == "train":
# Data processing pipeline
dataset_options = dict(dictionary=char2code,
level="character",
preprocess=_lower)
if data_path:
dataset = TextFile(data_path, **dataset_options)
else:
dataset = OneBillionWord("training", [99], **dataset_options)
data_stream = dataset.get_example_stream()
data_stream = Filter(data_stream, _filter_long)
data_stream = Mapping(data_stream,
reverse_words,
add_sources=("targets", ))
data_stream = Batch(data_stream, iteration_scheme=ConstantScheme(10))
data_stream = Padding(data_stream)
data_stream = Mapping(data_stream, _transpose)
# Initialization settings
reverser.weights_init = IsotropicGaussian(0.1)
reverser.biases_init = Constant(0.0)
reverser.push_initialization_config()
reverser.encoder.weights_init = Orthogonal()
reverser.generator.transition.weights_init = Orthogonal()
# Build the cost computation graph
chars = tensor.lmatrix("features")
chars_mask = tensor.matrix("features_mask")
targets = tensor.lmatrix("targets")
targets_mask = tensor.matrix("targets_mask")
batch_cost = reverser.cost(chars, chars_mask, targets,
targets_mask).sum()
batch_size = named_copy(chars.shape[1], "batch_size")
cost = aggregation.mean(batch_cost, batch_size)
cost.name = "sequence_log_likelihood"
logger.info("Cost graph is built")
# Give an idea of what's going on
model = Model(cost)
parameters = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat([(key, value.get_value().shape)
for key, value in parameters.items()],
width=120))
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
# Define the training algorithm.
cg = ComputationGraph(cost)
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule(
[StepClipping(10.0),
Scale(0.01)]))
# Fetch variables useful for debugging
generator = reverser.generator
(energies, ) = VariableFilter(applications=[generator.readout.readout],
name_regex="output")(cg.variables)
(activations, ) = VariableFilter(
applications=[generator.transition.apply],
name=generator.transition.apply.states[0])(cg.variables)
max_length = named_copy(chars.shape[0], "max_length")
cost_per_character = named_copy(
aggregation.mean(batch_cost, batch_size * max_length),
"character_log_likelihood")
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
mean_activation = named_copy(
abs(activations).mean(), "mean_activation")
observables = [
cost, min_energy, max_energy, mean_activation, batch_size,
max_length, cost_per_character, algorithm.total_step_norm,
algorithm.total_gradient_norm
]
for name, parameter in parameters.items():
observables.append(named_copy(parameter.norm(2), name + "_norm"))
observables.append(
named_copy(algorithm.gradients[parameter].norm(2),
name + "_grad_norm"))
# Construct the main loop and start training!
average_monitoring = TrainingDataMonitoring(observables,
prefix="average",
every_n_batches=10)
main_loop = MainLoop(
model=model,
data_stream=data_stream,
algorithm=algorithm,
extensions=[
Timing(),
TrainingDataMonitoring(observables, after_batch=True),
average_monitoring,
FinishAfter(after_n_batches=num_batches)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition(["after_batch"], _is_nan),
# Saving the model and the log separately is convenient,
# because loading the whole pickle takes quite some time.
Checkpoint(save_path,
every_n_batches=500,
save_separately=["model", "log"]),
Printing(every_n_batches=1)
])
main_loop.run()
elif mode == "sample" or mode == "beam_search":
chars = tensor.lmatrix("input")
generated = reverser.generate(chars)
model = Model(generated)
logger.info("Loading the model..")
model.set_parameter_values(load_parameter_values(save_path))
def generate(input_):
"""Generate output sequences for an input sequence.
Incapsulates most of the difference between sampling and beam
search.
Returns
-------
outputs : list of lists
Trimmed output sequences.
costs : list
The negative log-likelihood of generating the respective
sequences.
"""
if mode == "beam_search":
samples, = VariableFilter(bricks=[reverser.generator],
name="outputs")(ComputationGraph(
generated[1]))
# NOTE: this will recompile beam search functions
# every time user presses Enter. Do not create
# a new `BeamSearch` object every time if
# speed is important for you.
beam_search = BeamSearch(samples)
outputs, costs = beam_search.search({chars: input_},
char2code['</S>'],
3 * input_.shape[0])
else:
_1, outputs, _2, _3, costs = (
model.get_theano_function()(input_))
outputs = list(outputs.T)
costs = list(costs.T)
for i in range(len(outputs)):
outputs[i] = list(outputs[i])
try:
true_length = outputs[i].index(char2code['</S>']) + 1
except ValueError:
true_length = len(outputs[i])
outputs[i] = outputs[i][:true_length]
costs[i] = costs[i][:true_length].sum()
return outputs, costs
while True:
line = input("Enter a sentence\n")
message = ("Enter the number of samples\n"
if mode == "sample" else "Enter the beam size\n")
batch_size = int(input(message))
encoded_input = [
char2code.get(char, char2code["<UNK>"])
for char in line.lower().strip()
]
encoded_input = ([char2code['<S>']] + encoded_input +
[char2code['</S>']])
print("Encoder input:", encoded_input)
target = reverse_words((encoded_input, ))[0]
print("Target: ", target)
samples, costs = generate(
numpy.repeat(numpy.array(encoded_input)[:, None],
batch_size,
axis=1))
messages = []
for sample, cost in equizip(samples, costs):
message = "({})".format(cost)
message += "".join(code2char[code] for code in sample)
if sample == target:
message += " CORRECT!"
messages.append((cost, message))
messages.sort(key=operator.itemgetter(0), reverse=True)
for _, message in messages:
print(message)
on_unused_sources='warn')
# enrich the logged information
extensions.append(Timing(every_n_batches=100))
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(model=model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions)
# Train!
main_loop.run()
training_cost = create_model(train_encoder, train_decoder)
# Set up training model
logger.info("Building model")
train_model = Model(training_cost)
dev_stream = get_dev_stream(**exp_config)
main(train_model,
training_cost,
exp_config,
masked_stream,
dev_stream=dev_stream,
use_bokeh=True)
def train(step_rule, state_dim, epochs, seed, experiment_path, initialization,
to_watch, patience, static_mask, batch_size, rnn_type, num_layers,
augment, seq_len, drop_prob, drop_prob_states, drop_prob_cells,
drop_prob_igates, ogates_zoneout, stoch_depth, share_mask,
gaussian_drop, weight_noise, norm_cost_coeff, penalty, input_drop,
**kwargs):
print '.. cPTB experiment'
print '.. arguments:', ' '.join(sys.argv)
t0 = time.time()
def numpy_rng(random_seed=None):
if random_seed == None:
random_seed = 1223
return numpy.random.RandomState(random_seed)
###########################################
#
# MAKE DATA STREAMS
#
###########################################
rng = np.random.RandomState(seed)
if share_mask:
drop_prob_cells = drop_prob
# we don't want to actually use these masks, so this is to debug
drop_prob_states = None
print '.. initializing iterators'
if static_mask:
train_stream = get_static_mask_ptb_stream('train',
batch_size,
seq_len,
drop_prob_states,
drop_prob_cells,
drop_prob_igates,
state_dim,
False,
augment=augment)
train_stream_evaluation = get_static_mask_ptb_stream('train',
batch_size,
seq_len,
drop_prob_states,
drop_prob_cells,
drop_prob_igates,
state_dim,
True,
augment=augment)
dev_stream = get_static_mask_ptb_stream('valid',
batch_size,
seq_len,
drop_prob_states,
drop_prob_cells,
drop_prob_igates,
state_dim,
True,
augment=augment)
else:
train_stream = get_ptb_stream('train',
batch_size,
seq_len,
drop_prob_states,
drop_prob_cells,
drop_prob_igates,
state_dim,
False,
augment=augment)
train_stream_evaluation = get_ptb_stream('train',
batch_size,
seq_len,
drop_prob_states,
drop_prob_cells,
drop_prob_igates,
state_dim,
True,
augment=augment)
dev_stream = get_ptb_stream('valid',
batch_size,
seq_len,
drop_prob_states,
drop_prob_cells,
drop_prob_igates,
state_dim,
True,
augment=augment)
data = train_stream.get_epoch_iterator(as_dict=True).next()
#import ipdb; ipdb.set_trace()
###########################################
#
# BUILD MODEL
#
###########################################
print '.. building model'
x = T.tensor3('features', dtype=floatX)
x, y = x[:-1], x[1:]
drops_states = T.tensor3('drops_states')
drops_cells = T.tensor3('drops_cells')
drops_igates = T.tensor3('drops_igates')
x.tag.test_value = data['features']
#y.tag.test_value = data['outputs']
drops_states.tag.test_value = data['drops_states']
drops_cells.tag.test_value = data['drops_cells']
drops_igates.tag.test_value = data['drops_igates']
if initialization == 'glorot':
weights_init = NormalizedInitialization()
elif initialization == 'uniform':
weights_init = Uniform(width=.2)
elif initialization == 'ortho':
weights_init = OrthogonalInitialization()
else:
raise ValueError('No such initialization')
if rnn_type.lower() == 'lstm':
in_to_hid = Linear(50,
state_dim * 4,
name='in_to_hid',
weights_init=weights_init,
biases_init=Constant(0.0))
recurrent_layer = ZoneoutLSTM(dim=state_dim,
weights_init=weights_init,
activation=Tanh(),
model_type=6,
name='rnn',
ogates_zoneout=ogates_zoneout)
elif rnn_type.lower() == 'gru':
in_to_hid = Linear(50,
state_dim * 3,
name='in_to_hid',
weights_init=weights_init,
biases_init=Constant(0.0))
recurrent_layer = ZoneoutGRU(dim=state_dim,
weights_init=weights_init,
activation=Tanh(),
name='rnn')
elif rnn_type.lower() == 'srnn':
in_to_hid = Linear(50,
state_dim,
name='in_to_hid',
weights_init=weights_init,
biases_init=Constant(0.0))
recurrent_layer = ZoneoutSimpleRecurrent(dim=state_dim,
weights_init=weights_init,
activation=Rectifier(),
name='rnn')
else:
raise NotImplementedError
hid_to_out = Linear(state_dim,
50,
name='hid_to_out',
weights_init=weights_init,
biases_init=Constant(0.0))
in_to_hid.initialize()
recurrent_layer.initialize()
hid_to_out.initialize()
h = in_to_hid.apply(x)
if rnn_type.lower() == 'lstm':
yh = recurrent_layer.apply(h, drops_states, drops_cells,
drops_igates)[0]
else:
yh = recurrent_layer.apply(h, drops_states, drops_cells, drops_igates)
y_hat_pre_softmax = hid_to_out.apply(yh)
shape_ = y_hat_pre_softmax.shape
# y_hat = Softmax().apply(
# y_hat_pre_softmax.reshape((-1, shape_[-1])))# .reshape(shape_)
###########################################
#
# SET UP COSTS, MONITORS, and REGULARIZATION
#
###########################################
# cost = CategoricalCrossEntropy().apply(y.flatten().astype('int64'), y_hat)
def crossentropy_lastaxes(yhat, y):
# for sequence of distributions/targets
return -(y * T.log(yhat)).sum(axis=yhat.ndim - 1)
def softmax_lastaxis(x):
# for sequence of distributions
return T.nnet.softmax(x.reshape((-1, x.shape[-1]))).reshape(x.shape)
yhat = softmax_lastaxis(y_hat_pre_softmax)
cross_entropies = crossentropy_lastaxes(yhat, y)
cross_entropy = cross_entropies.mean().copy(name="cross_entropy")
cost = cross_entropy.copy(name="cost")
batch_cost = cost.copy(name='batch_cost')
nll_cost = cost.copy(name='nll_cost')
bpc = (nll_cost / np.log(2.0)).copy(name='bpr')
#nll_cost = aggregation.mean(batch_cost, batch_size).copy(name='nll_cost')
cost_monitor = aggregation.mean(
batch_cost, batch_size).copy(name='sequence_cost_monitor')
cost_per_character = aggregation.mean(
batch_cost, (seq_len - 1) * batch_size).copy(name='character_cost')
cost_train = cost.copy(name='train_batch_cost')
cost_train_monitor = cost_monitor.copy('train_batch_cost_monitor')
cg_train = ComputationGraph([cost_train, cost_train_monitor])
##################
# NORM STABILIZER
##################
norm_cost = 0.
def _magnitude(x, axis=-1):
return T.sqrt(
T.maximum(T.sqr(x).sum(axis=axis),
numpy.finfo(x.dtype).tiny))
if penalty == 'cells':
assert VariableFilter(roles=[MEMORY_CELL])(cg_train.variables)
for cell in VariableFilter(roles=[MEMORY_CELL])(cg_train.variables):
norms = _magnitude(cell)
norm_cost += T.mean(
T.sum((norms[1:] - norms[:-1])**2, axis=0) / (seq_len - 1))
## debugging nans stuff
#gr = T.grad(norm_cost, cg_train.parameters, disconnected_inputs='ignore')
#grf = theano.function([x, input_mask], gr)
#grz = grf(x.tag.test_value, input_mask.tag.test_value)
#params = cg_train.parameters
#mynanz = [(pp, np.sum(gg)) for pp,gg in zip(params, grz) if np.isnan(np.sum(gg))]
#for mm in mynanz: print mm
##import ipdb; ipdb.set_trace()
elif penalty == 'hids':
assert 'rnn_apply_states' in [
o.name for o in VariableFilter(roles=[OUTPUT])(cg_train.variables)
]
for output in VariableFilter(roles=[OUTPUT])(cg_train.variables):
if output.name == 'rnn_apply_states':
norms = _magnitude(output)
norm_cost += T.mean(
T.sum((norms[1:] - norms[:-1])**2, axis=0) / (seq_len - 1))
norm_cost.name = 'norm_cost'
cost_train += norm_cost_coeff * norm_cost
cost_train = cost_train.copy(
'cost_train') #should this be cost_train.outputs[0]?
cg_train = ComputationGraph([cost_train,
cost_train_monitor]) #, norm_cost])
##################
# WEIGHT NOISE
##################
if weight_noise > 0:
weights = VariableFilter(roles=[WEIGHT])(cg_train.variables)
cg_train = apply_noise(cg_train, weights, weight_noise)
cost_train = cg_train.outputs[0].copy(name='cost_train')
cost_train_monitor = cg_train.outputs[1].copy(
'train_batch_cost_monitor')
# if 'l2regularization' in kwargs:
# weights = VariableFilter(roles=[WEIGHT])(cg_train.variables)
# cost_train += kwargs['l2regularization'] * sum([
# (weight ** 2).sum() for weight in weights])
# cost_train.name = 'cost_train'
# cg_train = ComputationGraph(cost_train)
model = Model(cost_train)
train_cost_per_character = aggregation.mean(
cost_train_monitor,
(seq_len - 1) * batch_size).copy(name='train_character_cost')
algorithm = GradientDescent(step_rule=step_rule,
cost=cost_train,
parameters=cg_train.parameters)
observed_vars = [
cost_train, cost_train_monitor, train_cost_per_character,
aggregation.mean(algorithm.total_gradient_norm)
]
# parameters = model.get_parameter_dict()
# for name, param in parameters.iteritems():
# observed_vars.append(param.norm(2).copy(name=name + "_norm"))
# observed_vars.append(
# algorithm.gradients[param].norm(2).copy(name=name + "_grad_norm"))
train_monitor = TrainingDataMonitoring(variables=observed_vars,
prefix="train",
after_epoch=True)
dev_monitor = DataStreamMonitoring(variables=[nll_cost, bpc],
data_stream=dev_stream,
prefix="dev")
extensions = []
if 'load_path' in kwargs:
with open(kwargs['load_path']) as f:
loaded = np.load(f)
model = Model(cost_train)
params_dicts = model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
# '/f_6_.W' --> 'f_6_.W'
slash_index = param_name.find('/')
param_name = param_name[slash_index + 1:]
if param.get_value().shape == loaded[param_name].shape:
print 'Found: ' + param_name
param.set_value(loaded[param_name])
else:
print 'Not found: ' + param_name
extensions.extend(
[FinishAfter(after_n_epochs=epochs), train_monitor, dev_monitor])
if not os.path.exists(experiment_path):
os.makedirs(experiment_path)
log_path = os.path.join(experiment_path, 'log.txt')
fh = logging.FileHandler(filename=log_path)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
extensions.append(
SaveParams('dev_nll_cost', model, experiment_path, every_n_epochs=1))
extensions.append(SaveLog(every_n_epochs=1))
extensions.append(ProgressBar())
extensions.append(Printing())
###########################################
#
# MAIN LOOOOOOOOOOOP
#
###########################################
main_loop = MainLoop(model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
t1 = time.time()
print "Building time: %f" % (t1 - t0)
# if write_predictions:
# with open('predicted.txt', 'w') as f_pred:
# with open('targets.txt', 'w') as f_targets:
# evaluator = CTCEvaluator(
# eol_symbol, x, input_mask, y_hat, phoneme_dict, black_list)
# evaluator.evaluate(dev_stream, file_pred=f_pred,
# file_targets=f_targets)
# return
main_loop.run()
print "Execution time: %f" % (time.time() - t1)
def main(model, cost, config, tr_stream, dev_stream, use_bokeh=False):
# Set the parameters from a trained models (.npz file)
logger.info("Loading parameters from model: {}".format(
exp_config['saved_parameters']))
# Note the brick delimeter='-' is here for legacy reasons because blocks changed the serialization API
param_values = LoadNMT.load_parameter_values(
exp_config['saved_parameters'],
brick_delimiter=exp_config.get('brick_delimiter', None))
LoadNMT.set_model_parameters(model, param_values)
logger.info('Creating computational graph')
cg = ComputationGraph(cost)
# GRAPH TRANSFORMATIONS FOR BETTER TRAINING
if config.get('l2_regularization', False) is True:
l2_reg_alpha = config['l2_regularization_alpha']
logger.info(
'Applying l2 regularization with alpha={}'.format(l2_reg_alpha))
model_weights = VariableFilter(roles=[WEIGHT])(cg.variables)
for W in model_weights:
cost = cost + (l2_reg_alpha * (W**2).sum())
# why do we need to rename the cost variable? Where did the original name come from?
cost.name = 'decoder_cost_cost'
cg = ComputationGraph(cost)
# apply dropout for regularization
# Note dropout variables are hard-coded here
if config['dropout'] < 1.0:
# dropout is applied to the output of maxout in ghog
# this is the probability of dropping out, so you probably want to make it <=0.5
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'])
# create the training directory, and copy this config there if directory doesn't exist
if not os.path.isdir(config['saveto']):
os.makedirs(config['saveto'])
# TODO: mv the actual config file once we switch to .yaml for min-risk
# shutil.copy(config['config_file'], config['saveto'])
# shutil.copy(config['config_file'], config['saveto'])
with codecs.open(os.path.join(config['saveto'], 'config.yaml'),
'w',
encoding='utf8') as yaml_out:
yaml_out.write(yaml.dump(config))
# Set extensions
logger.info("Initializing extensions")
extensions = [
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
# TODO: change the if here
if config['hook_samples'] >= 1 or config['bleu_script'] is not None:
logger.info("Building sampling model")
sampling_representation = train_encoder.apply(
theano_sampling_input, tensor.ones(theano_sampling_input.shape))
# TODO: the generated output actually contains several more values, ipdb to see what they are
generated = train_decoder.generate(theano_sampling_input,
sampling_representation)
search_model = Model(generated)
_, samples = VariableFilter(
bricks=[train_decoder.sequence_generator], name="outputs")(
ComputationGraph(generated[1])) # generated[1] is next_outputs
# Add sampling -- TODO: sampling is broken for min-risk
#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=config['sampling_freq'],
# src_vocab_size=config['src_vocab_size']))
# Add early stopping based on bleu
if config.get('bleu_script', None) is not None:
logger.info("Building bleu validator")
extensions.append(
BleuValidator(theano_sampling_input,
samples=samples,
config=config,
model=search_model,
data_stream=dev_stream,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq']))
# Add early stopping based on bleu
if config.get('meteor_directory', None) is not None:
logger.info("Building meteor validator")
extensions.append(
MeteorValidator(theano_sampling_input,
samples=samples,
config=config,
model=search_model,
data_stream=dev_stream,
normalize=config['normalized_bleu'],
every_n_batches=config['bleu_val_freq']))
# Reload model if necessary
if config['reload']:
extensions.append(LoadNMT(config['saveto']))
# Plot cost in bokeh if necessary
if use_bokeh and BOKEH_AVAILABLE:
extensions.append(
Plot(config['model_save_directory'],
channels=[['decoder_cost_cost'],
['validation_set_meteor_score']],
every_n_batches=10))
# Set up training algorithm
logger.info("Initializing training algorithm")
# TODO: reenable dropout and add L2 regularization as in the main config
# if there is l2_regularization, dropout or random noise, we need to use the output of the modified graph
if config['dropout'] < 1.0:
algorithm = GradientDescent(cost=cg.outputs[0],
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]),
on_unused_sources='warn')
else:
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
]),
on_unused_sources='warn')
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(config['step_clipping']),
eval(config['step_rule'])()
], ),
on_unused_sources='warn')
# enrich the logged information
extensions.append(Timing(every_n_batches=100))
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(model=model,
algorithm=algorithm,
data_stream=tr_stream,
extensions=extensions)
# Train!
main_loop.run()
def mainPredict(config, data_to_predict_stream, use_ensemble, lang=None, et_version=False, use_bokeh=False, the_track=None):
# Create Theano variables
assert the_track != None
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(
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, cost_type=config['error_fct'])
cost = decoder.cost(
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')
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()
encoder.bidir.prototype.weights_init = Orthogonal()
decoder.transition.weights_init = Orthogonal()
encoder.initialize()
decoder.initialize()
# 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 extensions
logger.info("Initializing (empty) extensions")
extensions = [
]
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
# Reload the model (as this is prediction, it is 100% necessary):
if config['reload']:
extensions.append(LoadOnlyBestModel(config['saveto'])) # without early stopping use LoadOnlyModel here!
#extensions.append(LoadOnlyModel(config['saveto'])) # without early stopping use LoadOnlyModel here!
else:
raise Exception('No model available for prediction! (Check config[\'reload\'] variable)')
# 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=search_model,
algorithm=algorithm,
#algorithm=None,
data_stream=data_to_predict_stream,
extensions=extensions
)
predictByHand(main_loop, decoder, data_to_predict_stream, use_ensemble, lang, et_version, config, the_track=the_track)
def main(save_to):
batch_size = 365
feature_maps = [6, 16]
mlp_hiddens = [120, 84]
conv_sizes = [5, 5]
pool_sizes = [2, 2]
image_size = (28, 28)
output_size = 10
# The above are from LeCun's paper. The blocks example had:
# feature_maps = [20, 50]
# mlp_hiddens = [500]
# Use ReLUs everywhere and softmax for the final prediction
conv_activations = [Rectifier() for _ in feature_maps]
mlp_activations = [Rectifier() for _ in mlp_hiddens] + [Softmax()]
convnet = LeNet(conv_activations, 1, image_size,
filter_sizes=zip(conv_sizes, conv_sizes),
feature_maps=feature_maps,
pooling_sizes=zip(pool_sizes, pool_sizes),
top_mlp_activations=mlp_activations,
top_mlp_dims=mlp_hiddens + [output_size],
border_mode='valid',
weights_init=Uniform(width=.2),
biases_init=Constant(0))
# We push initialization config to set different initialization schemes
# for convolutional layers.
convnet.push_initialization_config()
convnet.layers[0].weights_init = Uniform(width=.2)
convnet.layers[1].weights_init = Uniform(width=.09)
convnet.top_mlp.linear_transformations[0].weights_init = Uniform(width=.08)
convnet.top_mlp.linear_transformations[1].weights_init = Uniform(width=.11)
convnet.initialize()
logging.info("Input dim: {} {} {}".format(
*convnet.children[0].get_dim('input_')))
for i, layer in enumerate(convnet.layers):
if isinstance(layer, Activation):
logging.info("Layer {} ({})".format(
i, layer.__class__.__name__))
else:
logging.info("Layer {} ({}) dim: {} {} {}".format(
i, layer.__class__.__name__, *layer.get_dim('output')))
x = tensor.tensor4('features')
# Normalize input and apply the convnet
probs = convnet.apply(x)
cg = ComputationGraph([probs])
outs = VariableFilter(
roles=[OUTPUT], bricks=[Convolutional, Linear])(cg.variables)
# Create an interior activation model
model = Model([probs] + outs)
# Load it with trained parameters
params = load_parameters(open(save_to, 'rb'))
model.set_parameter_values(params)
algorithm = MaximumActivationSearch(outputs=outs)
# Use the mnist test set, unshuffled
mnist_test = MNIST(("test",), sources=['features'])
mnist_test_stream = DataStream.default_stream(
mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, batch_size))
extensions = [Timing(),
FinishAfter(after_n_epochs=1),
DataStreamMonitoring(
[],
mnist_test_stream,
prefix="test"),
Checkpoint("maxact.tar"),
ProgressBar(),
Printing()]
main_loop = MainLoop(
algorithm,
mnist_test_stream,
model=model,
extensions=extensions)
main_loop.run()
examples = mnist_test.get_example_stream()
example = examples.get_data(0)[0]
layers = convnet.layers
for output, record in algorithm.maximum_activations.items():
layer = get_brick(output)
activations, indices, snapshots = (
r.get_value() if r else None for r in record[1:])
filmstrip = Filmstrip(
example.shape[-2:], (indices.shape[1], indices.shape[0]),
background='blue')
if layer in layers:
fieldmap = layerarray_fieldmap(layers[0:layers.index(layer) + 1])
for unit in range(indices.shape[1]):
for index in range(100):
mask = make_mask(example.shape[-2:], fieldmap, numpy.clip(
snapshots[index, unit, :, :], 0, numpy.inf))
imagenum = indices[index, unit, 0]
filmstrip.set_image((unit, index),
examples.get_data(imagenum)[0], mask)
else:
for unit in range(indices.shape[1]):
for index in range(100):
imagenum = indices[index, unit]
filmstrip.set_image((unit, index),
examples.get_data(imagenum)[0])
filmstrip.save(layer.name + '_maxact.jpg')
with open('dataset/vocab.pkl') as f:
vocabs = pkl.load(f)
word_vocab, rel_vocab = vocabs['word'], vocabs['rel']
with open('dataset/trainXY.json') as f:
train = json.load(f)
train = wrap_stream(train)
with open('dataset/testXY.json') as f:
test = json.load(f)
test = wrap_stream(test)
model = LSTMModel(len(vocabs['word']), n_mem, len(vocabs['rel']))
cg = ComputationGraph(model.cost)
bricks_model = Model(model.cost)
for brick in bricks_model.get_top_bricks():
brick.initialize()
model.lookup.W.set_value(vocabs['word'].get_embeddings().astype(theano.config.floatX))
if dropout:
pass
# logger.info('Applying dropout of {}'.format(dropout))
# lstm_dropout = [v for v in cg.intermediary_variables if v.name in {'W_cell_to_in', 'W_cell_to_out'}]
# cg = apply_dropout(cg, lstm_dropout, drop_prob=dropout)
# summary of what's going on
parameters = bricks_model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape, value.get_value().mean()) for key, value
sys.exit(0)
graphs, extensions, updates = construct_graphs(args, nclasses, sequence_length)
### optimization algorithm definition
step_rule = CompositeRule([
StepClipping(1.),
#Momentum(learning_rate=args.learning_rate, momentum=0.9),
RMSProp(learning_rate=args.learning_rate, decay_rate=0.5),
])
algorithm = GradientDescent(cost=graphs["training"].outputs[0],
parameters=graphs["training"].parameters,
step_rule=step_rule)
algorithm.add_updates(updates["training"])
model = Model(graphs["training"].outputs[0])
extensions = extensions["training"] + extensions["inference"]
# step monitor (after epoch to limit the log size)
step_channels = []
step_channels.extend([
algorithm.steps[param].norm(2).copy(name="step_norm:%s" % name)
for name, param in model.get_parameter_dict().items()])
step_channels.append(algorithm.total_step_norm.copy(name="total_step_norm"))
step_channels.append(algorithm.total_gradient_norm.copy(name="total_gradient_norm"))
step_channels.extend(graphs["training"].outputs)
logger.warning("constructing training data monitor")
extensions.append(TrainingDataMonitoring(
step_channels, prefix="iteration", after_batch=False))
def main(mode, save_to, num_epochs, load_params=None,
feature_maps=None, mlp_hiddens=None,
conv_sizes=None, pool_sizes=None, stride=None, repeat_times=None,
batch_size=None, num_batches=None, algo=None,
test_set=None, valid_examples=None,
dropout=None, max_norm=None, weight_decay=None,
batch_norm=None):
if feature_maps is None:
feature_maps = [20, 50, 50]
if mlp_hiddens is None:
mlp_hiddens = [500]
if conv_sizes is None:
conv_sizes = [5, 5, 5]
if pool_sizes is None:
pool_sizes = [2, 2, 2]
if repeat_times is None:
repeat_times = [1, 1, 1]
if batch_size is None:
batch_size = 500
if valid_examples is None:
valid_examples = 2500
if stride is None:
stride = 1
if test_set is None:
test_set = 'test'
if algo is None:
algo = 'rmsprop'
if batch_norm is None:
batch_norm = False
image_size = (128, 128)
output_size = 2
if (len(feature_maps) != len(conv_sizes) or
len(feature_maps) != len(pool_sizes) or
len(feature_maps) != len(repeat_times)):
raise ValueError("OMG, inconsistent arguments")
# Use ReLUs everywhere and softmax for the final prediction
conv_activations = [Rectifier() for _ in feature_maps]
mlp_activations = [Rectifier() for _ in mlp_hiddens] + [Softmax()]
convnet = LeNet(conv_activations, 3, image_size,
stride=stride,
filter_sizes=zip(conv_sizes, conv_sizes),
feature_maps=feature_maps,
pooling_sizes=zip(pool_sizes, pool_sizes),
repeat_times=repeat_times,
top_mlp_activations=mlp_activations,
top_mlp_dims=mlp_hiddens + [output_size],
border_mode='full',
batch_norm=batch_norm,
weights_init=Glorot(),
biases_init=Constant(0))
# We push initialization config to set different initialization schemes
# for convolutional layers.
convnet.initialize()
logging.info("Input dim: {} {} {}".format(
*convnet.children[0].get_dim('input_')))
for i, layer in enumerate(convnet.layers):
if isinstance(layer, Activation):
logging.info("Layer {} ({})".format(
i, layer.__class__.__name__))
else:
logging.info("Layer {} ({}) dim: {} {} {}".format(
i, layer.__class__.__name__, *layer.get_dim('output')))
single_x = tensor.tensor3('image_features')
x = tensor.tensor4('image_features')
single_y = tensor.lvector('targets')
y = tensor.lmatrix('targets')
# Training
with batch_normalization(convnet):
probs = convnet.apply(x)
cost = (CategoricalCrossEntropy().apply(y.flatten(), probs)
.copy(name='cost'))
error_rate = (MisclassificationRate().apply(y.flatten(), probs)
.copy(name='error_rate'))
cg = ComputationGraph([cost, error_rate])
extra_updates = []
if batch_norm: # batch norm:
logger.debug("Apply batch norm")
pop_updates = get_batch_normalization_updates(cg)
# p stands for population mean
# m stands for minibatch
alpha = 0.005
extra_updates = [(p, m * alpha + p * (1 - alpha))
for p, m in pop_updates]
population_statistics = [p for p, m in extra_updates]
if dropout:
relu_outputs = VariableFilter(bricks=[Rectifier], roles=[OUTPUT])(cg)
cg = apply_dropout(cg, relu_outputs, dropout)
cost, error_rate = cg.outputs
if weight_decay:
logger.debug("Apply weight decay {}".format(weight_decay))
cost += weight_decay * l2_norm(cg.parameters)
cost.name = 'cost'
# Validation
valid_probs = convnet.apply_5windows(single_x)
valid_cost = (CategoricalCrossEntropy().apply(single_y, valid_probs)
.copy(name='cost'))
valid_error_rate = (MisclassificationRate().apply(
single_y, valid_probs).copy(name='error_rate'))
model = Model([cost, error_rate])
if load_params:
logger.info("Loaded params from {}".format(load_params))
with open(load_params, 'r') as src:
model.set_parameter_values(load_parameters(src))
# Training stream with random cropping
train = DogsVsCats(("train",), subset=slice(None, 25000 - valid_examples, None))
train_str = DataStream(
train, iteration_scheme=ShuffledScheme(train.num_examples, batch_size))
train_str = add_transformers(train_str, random_crop=True)
# Validation stream without cropping
valid = DogsVsCats(("train",), subset=slice(25000 - valid_examples, None, None))
valid_str = DataStream(
valid, iteration_scheme=SequentialExampleScheme(valid.num_examples))
valid_str = add_transformers(valid_str)
if mode == 'train':
directory, _ = os.path.split(sys.argv[0])
env = dict(os.environ)
env['THEANO_FLAGS'] = 'floatX=float32'
port = numpy.random.randint(1025, 10000)
server = subprocess.Popen(
[directory + '/server.py',
str(25000 - valid_examples), str(batch_size), str(port)],
env=env, stderr=subprocess.STDOUT)
train_str = ServerDataStream(
('image_features', 'targets'), produces_examples=False,
port=port)
save_to_base, save_to_extension = os.path.splitext(save_to)
# Train with simple SGD
if algo == 'rmsprop':
step_rule = RMSProp(decay_rate=0.999, learning_rate=0.0003)
elif algo == 'adam':
step_rule = Adam()
else:
assert False
if max_norm:
conv_params = VariableFilter(bricks=[Convolutional], roles=[WEIGHT])(cg)
linear_params = VariableFilter(bricks=[Linear], roles=[WEIGHT])(cg)
step_rule = CompositeRule(
[step_rule,
Restrict(VariableClipping(max_norm, axis=0), linear_params),
Restrict(VariableClipping(max_norm, axis=(1, 2, 3)), conv_params)])
algorithm = GradientDescent(
cost=cost, parameters=model.parameters,
step_rule=step_rule)
algorithm.add_updates(extra_updates)
# `Timing` extension reports time for reading data, aggregating a batch
# and monitoring;
# `ProgressBar` displays a nice progress bar during training.
extensions = [Timing(every_n_batches=100),
FinishAfter(after_n_epochs=num_epochs,
after_n_batches=num_batches),
DataStreamMonitoring(
[valid_cost, valid_error_rate],
valid_str,
prefix="valid"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
TrackTheBest("valid_error_rate"),
Checkpoint(save_to, save_separately=['log'],
parameters=cg.parameters +
(population_statistics if batch_norm else []),
before_training=True, after_epoch=True)
.add_condition(
['after_epoch'],
OnLogRecord("valid_error_rate_best_so_far"),
(save_to_base + '_best' + save_to_extension,)),
Printing(every_n_batches=100)]
model = Model(cost)
main_loop = MainLoop(
algorithm,
train_str,
model=model,
extensions=extensions)
try:
main_loop.run()
finally:
server.terminate()
elif mode == 'test':
classify = theano.function([single_x], valid_probs.argmax())
test = DogsVsCats((test_set,))
test_str = DataStream(
test, iteration_scheme=SequentialExampleScheme(test.num_examples))
test_str = add_transformers(test_str)
correct = 0
with open(save_to, 'w') as dst:
print("id", "label", sep=',', file=dst)
for index, example in enumerate(test_str.get_epoch_iterator()):
image = example[0]
prediction = classify(image)
print(index + 1, classify(image), sep=',', file=dst)
if len(example) > 1 and prediction == example[1]:
correct += 1
print(correct / float(test.num_examples))
else:
assert False
def evaluate(model, load_path, configs):
print "FIX THIS : NOT BEST"
with open(load_path + 'trained_params.npz') as f:
loaded = np.load(f)
blocks_model = Model(model.cost)
params_dicts = blocks_model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
# '/f_6_.W' --> 'f_6_.W'
slash_index = param_name.find('/')
param_name = param_name[slash_index + 1:]
# if param_name in ['initial_location', 'initial_scale', 'initial_alpha']:
# param_name = 'lstmattention.' + param_name
if param.get_value().shape == loaded[param_name].shape:
param.set_value(loaded[param_name])
else:
print param_name
inps = ComputationGraph(model.error_rate).inputs
eval_function = theano.function(
inps, [model.error_rate, model.probabilities])
# tds, vds = configs['get_streams'](100)
# it = tds.get_epoch_iterator()
# data = it.next()
# print eval_function(data[0], data[1])
return eval_function
train_probs = []
valid_probs = []
train_unites = []
valid_unites = []
train_labels = []
valid_labels = []
it = tds.get_epoch_iterator()
for batch in range(6):
print batch
data = it.next()
train_probs.append(eval_function(data[0], data[1])[1])
train_unites.append(data[2])
train_labels.append(data[1])
it = vds.get_epoch_iterator()
for batch in range(2):
print batch
data = it.next()
valid_probs.append(eval_function(data[0], data[1])[1])
valid_unites.append(data[2])
valid_labels.append(data[1])
train_probs = np.vstack(train_probs)
valid_probs = np.vstack(valid_probs)
train_labels = np.hstack(train_labels)
valid_labels = np.hstack(valid_labels)
train_unites = np.hstack(train_unites)
valid_unites = np.hstack(valid_unites)
# For training
map_vid_to_onehot = {}
for j in list(set(train_unites)):
map_vid_to_onehot[j] = []
for i in train_unites:
for j in list(set(train_unites)):
if i == j:
map_vid_to_onehot[j].append(1)
else:
map_vid_to_onehot[j].append(0)
map_vid_to_class = {}
for j in list(set(train_unites)):
onehot = np.array(map_vid_to_onehot[j])[:, np.newaxis]
masked = onehot * train_probs
map_vid_to_class[j] = np.argmax(np.sum(masked, axis=0))
predicted_labels = []
for i in train_unites:
predicted_labels.append(map_vid_to_class[i])
incorrect = 0
for label, predicted_label in zip(train_labels, predicted_labels):
if label != predicted_label:
incorrect = incorrect + 1
print float(incorrect) / train_unites.shape[0]
map_vid_to_onehot = {}
for j in list(set(train_unites)):
map_vid_to_onehot[j] = []
for i in train_unites:
for j in list(set(train_unites)):
if i == j:
map_vid_to_onehot[j].append(1)
else:
map_vid_to_onehot[j].append(0)
# For validation
map_vid_to_onehot = {}
for j in list(set(valid_unites)):
map_vid_to_onehot[j] = []
for i in valid_unites:
for j in list(set(valid_unites)):
if i == j:
map_vid_to_onehot[j].append(1)
else:
map_vid_to_onehot[j].append(0)
map_vid_to_class = {}
for j in list(set(valid_unites)):
onehot = np.array(map_vid_to_onehot[j])[:, np.newaxis]
masked = onehot * valid_probs
map_vid_to_class[j] = np.argmax(np.sum(masked, axis=0))
predicted_labels = []
for i in valid_unites:
predicted_labels.append(map_vid_to_class[i])
incorrect = 0
for label, predicted_label in zip(valid_labels, predicted_labels):
if label != predicted_label:
incorrect = incorrect + 1
print float(incorrect) / valid_unites.shape[0]
return eval_function
def run_visualizations(cost, updates,
train_stream, valid_stream,
args,
hidden_states=None, gate_values=None):
# Load the parameters from a dumped model
assert args.load_path is not None
model = Model(cost)
model.set_parameter_values(load_parameter_values(args.load_path))
# Run a visualization
if args.visualize == "generate":
visualize_generate(cost,
hidden_states, updates,
train_stream, valid_stream,
args)
elif args.visualize == "gates" and (gate_values is not None):
if args.rnn_type == "lstm":
visualize_gates_lstm(gate_values, hidden_states, updates,
train_stream, valid_stream,
args)
elif args.rnn_type == "soft":
visualize_gates_soft(gate_values, hidden_states, updates,
train_stream, valid_stream,
args)
else:
assert False
elif args.visualize == "states":
visualize_states(hidden_states, updates,
train_stream, valid_stream,
args)
elif args.visualize == "gradients":
visualize_gradients(hidden_states, updates,
train_stream, valid_stream,
args)
elif args.visualize == "jacobian":
visualize_jacobian(hidden_states, updates,
train_stream, valid_stream,
args)
elif args.visualize == "presoft":
visualize_presoft(cost,
hidden_states, updates,
train_stream, valid_stream,
args)
elif args.visualize == "matrices":
visualize_matrices(args)
elif args.visualize == "trained_singular_values":
visualize_singular_values(args)
elif args.visualize == "gradients_flow_pie":
visualize_gradients_flow_pie(hidden_states, updates,
args)
else:
assert False
def __init__(self, save_to):
batch_size = 500
image_size = (28, 28)
output_size = 10
convnet = create_lenet_5()
layers = convnet.layers
logging.info("Input dim: {} {} {}".format(
*convnet.children[0].get_dim('input_')))
for i, layer in enumerate(convnet.layers):
if isinstance(layer, Activation):
logging.info("Layer {} ({})".format(
i, layer.__class__.__name__))
else:
logging.info("Layer {} ({}) dim: {} {} {}".format(
i, layer.__class__.__name__, *layer.get_dim('output')))
mnist_test = MNIST(("test",), sources=['features', 'targets'])
basis = create_fair_basis(mnist_test, 10, 10)
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
probs = convnet.apply(x)
cg = ComputationGraph([probs])
def full_brick_name(brick):
return '/'.join([''] + [b.name for b in brick.get_unique_path()])
# Find layer outputs to probe
outs = OrderedDict((full_brick_name(get_brick(out)), out)
for out in VariableFilter(
roles=[OUTPUT], bricks=[Convolutional, Linear])(
cg.variables))
# Normalize input and apply the convnet
error_rate = (MisclassificationRate().apply(y.flatten(), probs)
.copy(name='error_rate'))
confusion = (ConfusionMatrix().apply(y.flatten(), probs)
.copy(name='confusion'))
confusion.tag.aggregation_scheme = Sum(confusion)
confusion_image = (ConfusionImage().apply(y.flatten(), probs, x)
.copy(name='confusion_image'))
confusion_image.tag.aggregation_scheme = Sum(confusion_image)
model = Model(
[error_rate, confusion, confusion_image] + list(outs.values()))
# Load it with trained parameters
params = load_parameters(open(save_to, 'rb'))
model.set_parameter_values(params)
mnist_test = MNIST(("test",))
mnist_test_stream = DataStream.default_stream(
mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, batch_size))
self.model = model
self.mnist_test_stream = mnist_test_stream
self.evaluator = DatasetEvaluator(
[error_rate, confusion, confusion_image])
self.base_results = self.evaluator.evaluate(mnist_test_stream)
# TODO: allow target layer to be parameterized
self.target_layer = '/lenet/mlp/linear_0'
self.next_layer_param = '/lenet/mlp/linear_1.W'
self.base_sample = extract_sample(
outs[self.target_layer], mnist_test_stream)
self.base_param_value = (
model.get_parameter_dict()[
self.next_layer_param].get_value().copy())
import sys
from fuel.datasets.mnist import MNIST
from fuel.streams import DataStream
from fuel.schemes import ShuffledScheme
import theano
import logging
import numpy as np
logging.basicConfig()
m = VAModel()
# load parameters
model = Model(m.variational_cost)
print "loading params"
params = load_parameter_values(sys.argv[1])
model.set_param_values(params)
test_dataset = MNIST("test", sources=["features"])
test_scheme = ShuffledScheme(test_dataset.num_examples, 128)
test_stream = DataStream(test_dataset, iteration_scheme=test_scheme)
_func_sample = theano.function([m.Z], m.sampled)
# _func_noisy = theano.function([m.X], m.noisy)
# _func_produced = theano.function([m.X], m.produced)
# batch = test_stream.get_epoch_iterator().next()[0]
# out_noise = _func_noisy(batch)
# out_produced = _func_produced(batch)
def evaluate(model, load_path, plot):
with open(load_path + 'trained_params_best.npz') as f:
loaded = np.load(f)
blocks_model = Model(model.cost)
params_dicts = blocks_model.get_parameter_dict()
params_names = params_dicts.keys()
for param_name in params_names:
param = params_dicts[param_name]
# '/f_6_.W' --> 'f_6_.W'
slash_index = param_name.find('/')
param_name = param_name[slash_index + 1:]
assert param.get_value().shape == loaded[param_name].shape
param.set_value(loaded[param_name])
if plot:
train_data_stream, valid_data_stream = get_streams(20)
# T x B x F
data = train_data_stream.get_epoch_iterator().next()
cg = ComputationGraph(model.cost)
f = theano.function(cg.inputs, [model.location, model.scale],
on_unused_input='ignore',
allow_input_downcast=True)
res = f(data[1], data[0])
for i in range(10):
visualize_attention(data[0][:, i, :],
res[0][:, i, :], res[1][:, i, :],
image_shape=(512, 512), prefix=str(i))
plot_curves(path=load_path,
to_be_plotted=['train_categoricalcrossentropy_apply_cost',
'valid_categoricalcrossentropy_apply_cost'],
yaxis='Cross Entropy',
titles=['train', 'valid'],
main_title='CE')
plot_curves(path=load_path,
to_be_plotted=['train_learning_rate',
'train_learning_rate'],
yaxis='lr',
titles=['train', 'train'],
main_title='lr')
plot_curves(path=load_path,
to_be_plotted=['train_total_gradient_norm',
'valid_total_gradient_norm'],
yaxis='GradientNorm',
titles=['train', 'valid'],
main_title='GradientNorm')
for grad in ['_total_gradient_norm',
'_total_gradient_norm',
'_/lstmattention.W_patch_grad_norm',
'_/lstmattention.W_state_grad_norm',
'_/lstmattention.initial_cells_grad_norm',
'_/lstmattention.initial_location_grad_norm',
'_/lstmattention/lstmattention_mlp/linear_0.W_grad_norm',
'_/lstmattention/lstmattention_mlp/linear_1.W_grad_norm',
'_/mlp/linear_0.W_grad_norm',
'_/mlp/linear_1.W_grad_norm']:
plot_curves(path=load_path,
to_be_plotted=['train' + grad,
'valid' + grad],
yaxis='GradientNorm',
titles=['train',
'valid'],
main_title=grad.replace(
"_", "").replace("/", "").replace(".", ""))
plot_curves(path=load_path,
to_be_plotted=[
'train_misclassificationrate_apply_error_rate',
'valid_misclassificationrate_apply_error_rate'],
yaxis='Error rate',
titles=['train', 'valid'],
main_title='Error')
print 'plot printed'
def generate_embeddings(config,
tar_path,
part,
dest_path,
format_,
average=False,
encoder_embeddings=None,
**kwargs):
"""
generate embeddings for all the defintions, average them and serialize OR
if encoder_embeddings, serialize the models' encoder embeddings
config: name of the config of the model
tar_path: tar path of the model parameters
part: part of the dataset (should be either 'train', 'valid', 'test' or 'all')
dest_path: directory where the serialized embeddings will be written
format: either 'dict' or 'glove'
encoder_embeddings: None, 'only', 'mixed', 'if_missing'
- None: don't include encoder embeddings
- 'only': don't read any data, just serialize the encoder embeddings
- 'mixed': add the encoder embeddings to the list of definition embeddings
- 'if_missing': add the encoder embeddings when there is no corresponding def
average: if true, multi-prototype embeddings will be averaged
"""
if not os.path.exists(dest_path):
os.makedirs(dest_path)
c = config
data, model = initialize_data_and_model(c, train_phase=False)
words = T.ltensor3('words')
words_mask = T.matrix('words_mask')
keys = T.lmatrix('keys')
n_identical_keys = T.lvector('n_identical_keys')
sym_args = [words, words_mask]
if format_ not in ['dict', 'glove']:
raise ValueError("format should be either: dict, glove")
if not c['encoder'] and encoder_embeddings != 'only':
raise ValueError('Error: this model does not have an encoder.')
if use_keys(c):
sym_args.append(keys)
if use_n_identical_keys(c):
sym_args.append(n_identical_keys)
costs = model.apply(*sym_args, train_phase=False)
cg = Model(costs)
with open(tar_path) as src:
cg.set_parameter_values(load_parameters(src))
if encoder_embeddings:
if encoder_embeddings == 'only' and not c['encoder']:
embeddings_array = model.get_def_embeddings_params('key').eval()
else:
embeddings_array = model.get_def_embeddings_params('main').eval()
entries = model.get_embeddings_entries()
enc_embeddings = {
e: np.asarray(a)
for e, a in zip(entries, embeddings_array)
}
if encoder_embeddings == 'only':
serialize_embeddings(enc_embeddings, format_, dest_path,
"encoder_embeddings")
return 0
embeddings_var, = VariableFilter(name='embeddings')(cg)
compute = dict({"embeddings": embeddings_var})
if c['proximity_coef'] != 0:
prox_var, = VariableFilter(name='proximity_term')(cg)
compute["proximity_term"] = prox_var
print "sym args", sym_args
predict_f = theano.function(sym_args, compute)
batch_size = 256 # size of test_unseen
stream = data.get_stream(part,
batch_size=batch_size,
max_length=c['max_length'],
remove_keys=False,
remove_n_identical_keys=False)
raw_data = [] # list of dicts containing the inputs and computed outputs
i = 0
vocab = model._vocab
print "start computing"
embeddings = defaultdict(list)
for input_data in stream.get_epoch_iterator(as_dict=True):
if i % 10 == 0:
print "iteration:", i
words = input_data['words']
words_mask = input_data['words_mask']
keys = input_data['keys']
n_identical_keys = input_data['n_identical_keys']
args = [words, words_mask]
if use_keys(c):
args.append(keys)
if use_n_identical_keys(c):
args.append(n_identical_keys)
to_save = predict_f(*args)
for k, h in zip(keys, to_save['embeddings']):
key = vec2str(k)
if encoder_embeddings == 'if_missing':
try:
del enc_embeddings[key]
except KeyError:
pass
embeddings[key].append(h)
i += 1
if encoder_embeddings in ['mixed', 'if_missing']:
for k, e in enc_embeddings.iteritems():
embeddings[k].append(e)
if encoder_embeddings == 'mixed':
prefix_fname = 'mix_e_'
elif encoder_embeddings == 'if_missing':
prefix_fname = 'if_mis_e_'
else:
prefix_fname = ''
# combine:
if average:
mean_embeddings = {}
for k in embeddings.keys():
mean_embeddings[k] = np.mean(np.asarray(embeddings[k]), axis=0)
serialize_embeddings(mean_embeddings, format_, dest_path,
prefix_fname + "mean_embeddings")
else:
serialize_embeddings(embeddings, format_, dest_path,
prefix_fname + "embeddings")
def main(config):
# Create Theano variables
logger.info('Creating theano variables')
source_char_seq = tensor.lmatrix('source_char_seq')
source_sample_matrix = tensor.btensor3('source_sample_matrix')
source_char_aux = tensor.bmatrix('source_char_aux')
source_word_mask = tensor.bmatrix('source_word_mask')
target_char_seq = tensor.lmatrix('target_char_seq')
target_char_aux = tensor.bmatrix('target_char_aux')
target_char_mask = tensor.bmatrix('target_char_mask')
target_sample_matrix = tensor.btensor3('target_sample_matrix')
target_word_mask = tensor.bmatrix('target_word_mask')
target_resample_matrix = tensor.btensor3('target_resample_matrix')
target_prev_char_seq = tensor.lmatrix('target_prev_char_seq')
target_prev_char_aux = tensor.bmatrix('target_prev_char_aux')
src_vocab = _ensure_special_tokens(pickle.load(
open(config['src_vocab'], 'rb')),
bos_idx=0,
eos_idx=config['src_vocab_size'] - 1,
unk_idx=config['unk_id'])
trg_vocab = _ensure_special_tokens(pickle.load(
open(config['trg_vocab'], 'rb')),
bos_idx=0,
eos_idx=config['trg_vocab_size'] - 1,
unk_idx=config['unk_id'])
target_bos_idx = trg_vocab[config['bos_token']]
target_space_idx = trg_vocab[' ']
logger.info('Building RNN encoder-decoder')
encoder = BidirectionalEncoder(config['src_vocab_size'],
config['enc_embed'],
config['src_dgru_nhids'],
config['enc_nhids'],
config['src_dgru_depth'],
config['bidir_encoder_depth'])
decoder = Decoder(config['trg_vocab_size'], config['dec_embed'],
config['trg_dgru_nhids'], config['trg_igru_nhids'],
config['dec_nhids'], config['enc_nhids'] * 2,
config['transition_depth'], config['trg_igru_depth'],
config['trg_dgru_depth'], target_space_idx,
target_bos_idx)
representation = encoder.apply(source_char_seq, source_sample_matrix,
source_char_aux, source_word_mask)
cost = decoder.cost(representation, source_word_mask, target_char_seq,
target_sample_matrix, target_resample_matrix,
target_char_aux, target_char_mask, target_word_mask,
target_prev_char_seq, target_prev_char_aux)
# Set up model
logger.info("Building model")
training_model = Model(cost)
# Set extensions
logger.info("Initializing extensions")
# Reload model if necessary
extensions = [LoadNMT(config['saveto'])]
# Initialize main loop
logger.info("Initializing main loop")
main_loop = MainLoop(model=training_model,
algorithm=None,
data_stream=None,
extensions=extensions)
for extension in main_loop.extensions:
extension.main_loop = main_loop
main_loop._run_extensions('before_training')
char_embedding = encoder.decimator.apply(source_char_seq.T,
source_sample_matrix,
source_char_aux.T)
embedding(Model(char_embedding), src_vocab)
def train(step_rule, label_dim, state_dim, epochs,
seed, dropout, test_cost, experiment_path, features, weight_noise,
to_watch, patience, batch_size, batch_norm, **kwargs):
print '.. TIMIT experiment'
print '.. arguments:', ' '.join(sys.argv)
t0 = time.time()
# ------------------------------------------------------------------------
# Streams
rng = np.random.RandomState(seed)
stream_args = dict(rng=rng, batch_size=batch_size)
print '.. initializing iterators'
train_dataset = Timit('train', features=features)
train_stream = construct_stream(train_dataset, **stream_args)
dev_dataset = Timit('dev', features=features)
dev_stream = construct_stream(dev_dataset, **stream_args)
test_dataset = Timit('test', features=features)
test_stream = construct_stream(test_dataset, **stream_args)
update_stream = construct_stream(train_dataset, n_batches=100,
**stream_args)
phone_dict = train_dataset.get_phoneme_dict()
phoneme_dict = {k: phone_to_phoneme_dict[v]
if v in phone_to_phoneme_dict else v
for k, v in phone_dict.iteritems()}
ind_to_phoneme = {v: k for k, v in phoneme_dict.iteritems()}
eol_symbol = ind_to_phoneme['<STOP>']
# ------------------------------------------------------------------------
# Graph
print '.. building model'
x = T.tensor3('features')
y = T.matrix('phonemes')
input_mask = T.matrix('features_mask')
output_mask = T.matrix('phonemes_mask')
theano.config.compute_test_value = 'off'
x.tag.test_value = np.random.randn(100, 24, 123).astype(floatX)
y.tag.test_value = np.ones((30, 24), dtype=floatX)
input_mask.tag.test_value = np.ones((100, 24), dtype=floatX)
output_mask.tag.test_value = np.ones((30, 24), dtype=floatX)
seq_len = 100
input_dim = 123
activation = Tanh()
recurrent_init = IdentityInit(0.99)
if batch_norm:
rec1 = LSTMBatchNorm(name='rec1',
dim=state_dim,
activation=activation,
weights_init=NormalizedInitialization())
#rec1 = SimpleRecurrentBatchNorm(name='rec1',
# dim=state_dim,
# activation=activation,
# seq_len=seq_len,
# weights_init=recurrent_init)
#rec2 = SimpleRecurrentBatchNorm(name='rec2',
# dim=state_dim,
# activation=activation,
# seq_len=seq_len,
# weights_init=recurrent_init)
#rec3 = SimpleRecurrentBatchNorm(name='rec3',
# dim=state_dim,
# activation=activation,
# seq_len=seq_len,
# weights_init=recurrent_init)
else:
rec1 = LSTM(name='rec1', dim=state_dim, activation=activation,
weights_init=NormalizedInitialization())
#rec1 = SimpleRecurrent(name='rec1', dim=state_dim, activation=activation,
# weights_init=recurrent_init)
#rec2 = SimpleRecurrent(name='rec2', dim=state_dim, activation=activation,
# weights_init=recurrent_init)
#rec3 = SimpleRecurrent(name='rec3', dim=state_dim, activation=activation,
# weights_init=recurrent_init)
rec1.initialize()
#rec2.initialize()
#rec3.initialize()
s1 = MyRecurrent(rec1, [input_dim, state_dim, label_dim + 1],
activations=[Identity(), Identity()], name='s1')
#s2 = MyRecurrent(rec2, [state_dim, state_dim, state_dim],
# activations=[Identity(), Identity()], name='s2')
#s3 = MyRecurrent(rec3, [state_dim, state_dim, label_dim + 1],
# activations=[Identity(), Identity()], name='s3')
s1.initialize()
#s2.initialize()
#s3.initialize()
o1 = s1.apply(x, input_mask)
#o2 = s2.apply(o1)
#y_hat_o = s3.apply(o2)
y_hat_o = o1
shape = y_hat_o.shape
y_hat = Softmax().apply(y_hat_o.reshape((-1, shape[-1]))).reshape(shape)
y_mask = output_mask
y_hat_mask = input_mask
# ------------------------------------------------------------------------
# Costs and Algorithm
ctc_cost = T.sum(ctc.cpu_ctc_th(
y_hat_o, T.sum(y_hat_mask, axis=0),
y + T.ones_like(y), T.sum(y_mask, axis=0)))
batch_cost = ctc_cost.copy(name='batch_cost')
bs = y.shape[1]
cost_train = aggregation.mean(batch_cost, bs).copy("sequence_cost")
cost_per_character = aggregation.mean(batch_cost,
output_mask.sum()).copy(
"character_cost")
cg_train = ComputationGraph(cost_train)
model = Model(cost_train)
train_cost_per_character = aggregation.mean(cost_train,
output_mask.sum()).copy(
"train_character_cost")
algorithm = GradientDescent(step_rule=step_rule, cost=cost_train,
parameters=cg_train.parameters,
on_unused_sources='warn')
# ------------------------------------------------------------------------
# Monitoring and extensions
parameters = model.get_parameter_dict()
observed_vars = [cost_train, train_cost_per_character,
aggregation.mean(algorithm.total_gradient_norm)]
for name, param in parameters.iteritems():
observed_vars.append(param.norm(2).copy(name + "_norm"))
observed_vars.append(algorithm.gradients[param].norm(2).copy(name + "_grad_norm"))
train_monitor = TrainingDataMonitoring(
variables=observed_vars,
prefix="train", after_epoch=True)
dev_monitor = DataStreamMonitoring(
variables=[cost_train, cost_per_character],
data_stream=dev_stream, prefix="dev"
)
train_ctc_monitor = CTCMonitoring(x, input_mask, y_hat, eol_symbol, train_stream,
prefix='train', every_n_epochs=1,
before_training=True,
phoneme_dict=phoneme_dict,
black_list=black_list, train=True)
dev_ctc_monitor = CTCMonitoring(x, input_mask, y_hat, eol_symbol, dev_stream,
prefix='dev', every_n_epochs=1,
phoneme_dict=phoneme_dict,
black_list=black_list)
extensions = []
if 'load_path' in kwargs:
extensions.append(Load(kwargs['load_path']))
extensions.extend([FinishAfter(after_n_epochs=epochs),
train_monitor,
dev_monitor,
train_ctc_monitor,
dev_ctc_monitor])
if test_cost:
test_monitor = DataStreamMonitoring(
variables=[cost_train, cost_per_character],
data_stream=test_stream,
prefix="test"
)
test_ctc_monitor = CTCMonitoring(x, input_mask, y_hat, eol_symbol, test_stream,
prefix='test', every_n_epochs=1,
phoneme_dict=phoneme_dict,
black_list=black_list)
extensions.append(test_monitor)
extensions.append(test_ctc_monitor)
#if not os.path.exists(experiment_path):
# os.makedirs(experiment_path)
#best_path = os.path.join(experiment_path, 'best/')
#if not os.path.exists(best_path):
# os.mkdir(best_path)
#best_path = os.path.join(best_path, 'model.bin')
extensions.append(EarlyStopping(to_watch, patience, '/dev/null'))
extensions.extend([ProgressBar(), Printing()])
# ------------------------------------------------------------------------
# Main Loop
main_loop = MainLoop(model=model, data_stream=train_stream,
algorithm=algorithm, extensions=extensions)
print "Building time: %f" % (time.time() - t0)
# if write_predictions:
# with open('predicted.txt', 'w') as f_pred:
# with open('targets.txt', 'w') as f_targets:
# evaluator = CTCEvaluator(
# eol_symbol, x, input_mask, y_hat, phoneme_dict, black_list)
# evaluator.evaluate(dev_stream, file_pred=f_pred,
# file_targets=f_targets)
# return
main_loop.run()
iteration_scheme=SequentialScheme(
data_test.num_examples,
batch_size=bs))
learning_rate = 0.0002
n_epochs = 100
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(10.),
Adam(learning_rate),
]))
print('..loading...')
load = Load('/home/xuehongyang/checkpoints_open/snapshot_18')
predictor = PredictDataStream(data_stream=data_stream_test,
output_tensor=result,
path='/home/xuehongyang/RESULT_MAIN',
before_training=True,
after_epoch=False,
after_training=False)
main_loop = MainLoop(
model=Model(cost),
data_stream=data_stream_train,
algorithm=algorithm,
extensions=[Timing(),
FinishAfter(after_n_epochs=1), load, predictor])
print('start prediction ...')
main_loop.run()
def main(name, epochs, batch_size, learning_rate,
dim, mix_dim, old_model_name, max_length, bokeh, GRU, dropout,
depth, max_grad, step_method, epsilon, sample, skip, uniform, top):
#----------------------------------------------------------------------
datasource = name
def shnum(x):
""" Convert a positive float into a short tag-usable string
E.g.: 0 -> 0, 0.005 -> 53, 100 -> 1-2
"""
return '0' if x <= 0 else '%s%d' % (("%e"%x)[0], -np.floor(np.log10(x)))
jobname = "%s-%dX%dm%dd%dr%sb%de%s" % (datasource, depth, dim, mix_dim,
int(dropout*10),
shnum(learning_rate), batch_size,
shnum(epsilon))
if max_length != 600:
jobname += '-L%d'%max_length
if GRU:
jobname += 'g'
if max_grad != 5.:
jobname += 'G%g'%max_grad
if step_method != 'adam':
jobname += step_method
if skip:
jobname += 'D'
assert depth > 1
if top:
jobname += 'T'
assert depth > 1
if uniform > 0.:
jobname += 'u%d'%int(uniform*100)
if debug:
jobname += ".debug"
if sample:
print("Sampling")
else:
print("\nRunning experiment %s" % jobname)
if old_model_name:
print("starting from model %s"%old_model_name)
#----------------------------------------------------------------------
transitions = [GatedRecurrent(dim=dim) if GRU else LSTM(dim=dim)
for _ in range(depth)]
if depth > 1:
transition = RecurrentStack(transitions, name="transition",
fast=True, skip_connections=skip or top)
if skip:
source_names=['states'] + ['states_%d'%d for d in range(1,depth)]
else:
source_names=['states_%d'%(depth-1)]
else:
transition = transitions[0]
transition.name = "transition"
source_names=['states']
emitter = SketchEmitter(mix_dim=mix_dim,
epsilon=epsilon,
name="emitter")
readout = Readout(
readout_dim=emitter.get_dim('inputs'),
source_names=source_names,
emitter=emitter,
name="readout")
normal_inputs = [name for name in transition.apply.sequences
if 'mask' not in name]
fork = Fork(normal_inputs, prototype=Linear(use_bias=True))
generator = SequenceGenerator(readout=readout, transition=transition,
fork=fork)
# Initialization settings
if uniform > 0.:
generator.weights_init = Uniform(width=uniform*2.)
else:
generator.weights_init = OrthogonalGlorot()
generator.biases_init = Constant(0)
# Build the cost computation graph [steps, batch_size, 3]
x = T.tensor3('features', dtype=floatX)
if debug:
x.tag.test_value = np.ones((max_length,batch_size,3)).astype(floatX)
x = x[:max_length,:,:] # has to be after setting test_value
cost = generator.cost(x)
cost.name = "sequence_log_likelihood"
# Give an idea of what's going on
model = Model(cost)
params = model.get_params()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in params.items()],
width=120))
model_size = 0
for v in params.itervalues():
s = v.get_value().shape
model_size += s[0] * (s[1] if len(s) > 1 else 1)
logger.info("Total number of parameters %d"%model_size)
#------------------------------------------------------------
extensions = []
if old_model_name == 'continue':
extensions.append(LoadFromDump(jobname))
elif old_model_name:
# or you can just load the weights without state using:
old_params = LoadFromDump(old_model_name).manager.load_parameters()
model.set_param_values(old_params)
else:
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
if sample:
assert old_model_name and old_model_name != 'continue'
Sample(generator, steps=max_length, path=old_model_name).do(None)
exit(0)
#------------------------------------------------------------
# Define the training algorithm.
cg = ComputationGraph(cost)
if dropout > 0.:
from blocks.roles import INPUT, OUTPUT
dropout_target = VariableFilter(roles=[OUTPUT],
bricks=transitions,
name_regex='states')(cg.variables)
print('# dropout %d' % len(dropout_target))
cg = apply_dropout(cg, dropout_target, dropout)
opt_cost = cg.outputs[0]
else:
opt_cost = cost
if step_method == 'adam':
step_rule = Adam(learning_rate)
elif step_method == 'rmsprop':
step_rule = RMSProp(learning_rate, decay_rate=0.95)
elif step_method == 'adagrad':
step_rule = AdaGrad(learning_rate)
elif step_method == 'adadelta':
step_rule = AdaDelta()
elif step_method == 'scale':
step_rule = Scale(learning_rate)
else:
raise Exception('Unknown sttep method %s'%step_method)
step_rule = CompositeRule([StepClipping(max_grad), step_rule])
algorithm = GradientDescent(
cost=opt_cost, params=cg.parameters,
step_rule=step_rule)
#------------------------------------------------------------
observables = [cost]
# Fetch variables useful for debugging
(energies,) = VariableFilter(
applications=[generator.readout.readout],
name_regex="output")(cg.variables)
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
observables += [min_energy, max_energy]
# (activations,) = VariableFilter(
# applications=[generator.transition.apply],
# name=generator.transition.apply.states[0])(cg.variables)
# mean_activation = named_copy(abs(activations).mean(),
# "mean_activation")
# observables.append(mean_activation)
observables += [algorithm.total_step_norm, algorithm.total_gradient_norm]
for name, param in params.items():
observables.append(named_copy(
param.norm(2), name + "_norm"))
observables.append(named_copy(
algorithm.gradients[param].norm(2), name + "_grad_norm"))
#------------------------------------------------------------
datasource_fname = os.path.join(fuel.config.data_path, datasource,
datasource+'.hdf5')
train_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_set='train', sources=('features',),
load_in_memory=True)
train_stream = DataStream(train_ds,
iteration_scheme=ShuffledScheme(
train_ds.num_examples, batch_size))
test_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_set='test', sources=('features',),
load_in_memory=True)
test_stream = DataStream(test_ds,
iteration_scheme=SequentialScheme(
test_ds.num_examples, batch_size))
train_stream = Mapping(train_stream, _transpose)
test_stream = Mapping(test_stream, _transpose)
def stream_stats(ds, label):
itr = ds.get_epoch_iterator(as_dict=True)
batch_count = 0
examples_count = 0
for batch in itr:
batch_count += 1
examples_count += batch['features'].shape[1]
print('%s #batch %d #examples %d' %
(label, batch_count, examples_count))
stream_stats(train_stream, 'train')
stream_stats(test_stream, 'test')
extensions += [Timing(every_n_batches=10),
TrainingDataMonitoring(
observables, prefix="train",
every_n_batches=10),
DataStreamMonitoring(
[cost], # without dropout
test_stream,
prefix="test",
on_resumption=True,
after_epoch=False, # by default this is True
every_n_batches=100),
# all monitored data is ready so print it...
# (next steps may take more time and we want to see the
# results as soon as possible so print as soon as you can)
Printing(every_n_batches=10),
# perform multiple dumps at different intervals
# so if one of them breaks (has nan) we can hopefully
# find a model from few batches ago in the other
Dump(jobname, every_n_batches=11),
Dump(jobname+'.test', every_n_batches=100),
Sample(generator, steps=max_length,
path=jobname+'.test',
every_n_batches=100),
ProgressBar(),
FinishAfter(after_n_epochs=epochs)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition("after_batch", _is_nan),
]
if bokeh:
from blocks.extensions.plot import Plot
extensions.append(Plot(
'sketch',
channels=[
['cost'],]))
# Construct the main loop and start training!
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions
)
main_loop.run()
def setup_model(configs):
tensor5 = theano.tensor.TensorType(config.floatX, (False,) * 5)
# shape: T x B x C x X x Y
input_ = tensor5('features')
tensor3 = theano.tensor.TensorType(config.floatX, (False,) * 3)
locs = tensor3('locs')
# shape: B x Classes
target = T.ivector('targets')
model = LSTMAttention(
configs,
weights_init=Glorot(),
biases_init=Constant(0))
model.initialize()
(h, c, location, scale, alpha, patch, downn_sampled_input,
conved_part_1, conved_part_2, pre_lstm) = model.apply(input_, locs)
model.location = location
model.scale = scale
model.alpha = location
model.patch = patch
classifier = MLP(
[Rectifier(), Softmax()],
configs['classifier_dims'],
weights_init=Glorot(),
biases_init=Constant(0))
classifier.initialize()
probabilities = classifier.apply(h[-1])
cost = CategoricalCrossEntropy().apply(target, probabilities)
cost.name = 'CE'
error_rate = MisclassificationRate().apply(target, probabilities)
error_rate.name = 'ER'
model.cost = cost
model.error_rate = error_rate
model.probabilities = probabilities
if configs['load_pretrained']:
blocks_model = Model(model.cost)
all_params = blocks_model.parameters
with open('VGG_CNN_params.npz') as f:
loaded = np.load(f)
all_conv_params = loaded.keys()
for param in all_params:
if param.name in loaded.keys():
assert param.get_value().shape == loaded[param.name].shape
param.set_value(loaded[param.name])
all_conv_params.pop(all_conv_params.index(param.name))
print "the following parameters did not match: " + str(all_conv_params)
if configs['test_model']:
print "TESTING THE MODEL: CHECK THE INPUT SIZE!"
cg = ComputationGraph(model.cost)
f = theano.function(cg.inputs, [model.cost],
on_unused_input='ignore',
allow_input_downcast=True)
data = configs['get_streams'](configs[
'batch_size'])[0].get_epoch_iterator().next()
f(data[1], data[0], data[2])
print "Test passed! ;)"
model.monitorings = [cost, error_rate]
return model
def main(save_to, num_epochs, feature_maps=None, mlp_hiddens=None,
conv_sizes=None, pool_sizes=None, batch_size=500,
num_batches=None):
if feature_maps is None:
feature_maps = [20, 50]
if mlp_hiddens is None:
mlp_hiddens = [500]
if conv_sizes is None:
conv_sizes = [5, 5]
if pool_sizes is None:
pool_sizes = [2, 2]
image_size = (28, 28)
output_size = 10
# Use ReLUs everywhere and softmax for the final prediction
conv_activations = [Rectifier() for _ in feature_maps]
mlp_activations = [Rectifier() for _ in mlp_hiddens] + [Softmax()]
convnet = LeNet(conv_activations, 1, image_size,
filter_sizes=zip(conv_sizes, conv_sizes),
feature_maps=feature_maps,
pooling_sizes=zip(pool_sizes, pool_sizes),
top_mlp_activations=mlp_activations,
top_mlp_dims=mlp_hiddens + [output_size],
border_mode='full',
weights_init=Uniform(width=.2),
biases_init=Constant(0))
# We push initialization config to set different initialization schemes
# for convolutional layers.
convnet.push_initialization_config()
convnet.layers[0].weights_init = Uniform(width=.2)
convnet.layers[1].weights_init = Uniform(width=.09)
convnet.top_mlp.linear_transformations[0].weights_init = Uniform(width=.08)
convnet.top_mlp.linear_transformations[1].weights_init = Uniform(width=.11)
convnet.initialize()
logging.info("Input dim: {} {} {}".format(
*convnet.children[0].get_dim('input_')))
for i, layer in enumerate(convnet.layers):
if isinstance(layer, Activation):
logging.info("Layer {} ({})".format(
i, layer.__class__.__name__))
else:
logging.info("Layer {} ({}) dim: {} {} {}".format(
i, layer.__class__.__name__, *layer.get_dim('output')))
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
probs = convnet.apply(x)
cost = (CategoricalCrossEntropy().apply(y.flatten(), probs)
.copy(name='cost'))
error_rate = (MisclassificationRate().apply(y.flatten(), probs)
.copy(name='error_rate'))
cg = ComputationGraph([cost, error_rate])
mnist_train = MNIST(("train",))
mnist_train_stream = DataStream.default_stream(
mnist_train, iteration_scheme=ShuffledScheme(
mnist_train.num_examples, batch_size))
mnist_test = MNIST(("test",))
mnist_test_stream = DataStream.default_stream(
mnist_test,
iteration_scheme=ShuffledScheme(
mnist_test.num_examples, batch_size))
# Train with simple SGD
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=Scale(learning_rate=0.1))
# `Timing` extension reports time for reading data, aggregating a batch
# and monitoring;
# `ProgressBar` displays a nice progress bar during training.
extensions = [Timing(),
FinishAfter(after_n_epochs=num_epochs,
after_n_batches=num_batches),
DataStreamMonitoring(
[cost, error_rate],
mnist_test_stream,
prefix="test"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
ProgressBar(),
Printing()]
model = Model(cost)
main_loop = MainLoop(
algorithm,
mnist_train_stream,
model=model,
extensions=extensions)
main_loop.run()
def train(model, configs):
get_streams = configs['get_streams']
save_path = configs['save_path']
num_epochs = configs['num_epochs']
batch_size = configs['batch_size']
lrs = configs['lrs']
until_which_epoch = configs['until_which_epoch']
grad_clipping = configs['grad_clipping']
monitorings = model.monitorings
# Training
if configs['weight_noise'] > 0:
cg = ComputationGraph(model.cost)
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
cg = apply_noise(cg, weights, configs['weight_noise'])
model.cost = cg.outputs[0].copy(name='CE')
if configs['l2_reg'] > 0:
cg = ComputationGraph(model.cost)
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
new_cost = model.cost + configs['l2_reg'] * sum([
(weight ** 2).sum() for weight in weights])
model.cost = new_cost.copy(name='CE')
blocks_model = Model(model.cost)
all_params = blocks_model.parameters
print "Number of found parameters:" + str(len(all_params))
print all_params
default_lr = np.float32(configs['lrs'][0])
lr_var = theano.shared(default_lr, name="learning_rate")
clipping = StepClipping(threshold=np.cast[floatX](grad_clipping))
# sgd_momentum = Momentum(
# learning_rate=0.0001,
# momentum=0.95)
# step_rule = CompositeRule([clipping, sgd_momentum])
adam = Adam(learning_rate=lr_var)
step_rule = CompositeRule([clipping, adam])
training_algorithm = GradientDescent(
cost=model.cost, parameters=all_params,
step_rule=step_rule)
monitored_variables = [
lr_var,
aggregation.mean(training_algorithm.total_gradient_norm)] + monitorings
for param in all_params:
name = param.tag.annotations[0].name + "." + param.name
to_monitor = training_algorithm.gradients[param].norm(2)
to_monitor.name = name + "_grad_norm"
monitored_variables.append(to_monitor)
to_monitor = param.norm(2)
to_monitor.name = name + "_norm"
monitored_variables.append(to_monitor)
train_data_stream, valid_data_stream = get_streams(batch_size)
train_monitoring = TrainingDataMonitoring(
variables=monitored_variables,
prefix="train",
after_epoch=True)
valid_monitoring = DataStreamMonitoring(
variables=monitored_variables,
data_stream=valid_data_stream,
prefix="valid",
after_epoch=True)
main_loop = MainLoop(
algorithm=training_algorithm,
data_stream=train_data_stream,
model=blocks_model,
extensions=[
train_monitoring,
valid_monitoring,
FinishAfter(after_n_epochs=num_epochs),
SaveParams('valid_CE',
blocks_model, save_path,
after_epoch=True),
SaveLog(after_epoch=True),
ProgressBar(),
LRDecay(lr_var, lrs, until_which_epoch,
after_epoch=True),
Printing(after_epoch=True)])
main_loop.run()
# states = {}
states = [state for state in generator.transition.apply.outputs if state != "step"]
# ipdb.set_trace()
states = {name: shared_floatx_zeros((batch_size, hidden_size_recurrent)) for name in states}
cost_matrix = generator.cost_matrix(x, attended=context, **states)
cost = cost_matrix.mean() + 0.0 * start_flag
cost.name = "nll"
cg = ComputationGraph(cost)
model = Model(cost)
transition_matrix = VariableFilter(theano_name_regex="state_to_state")(cg.parameters)
for matr in transition_matrix:
matr.set_value(0.98 * numpy.eye(hidden_size_recurrent, dtype=floatX))
from play.utils import regex_final_value
extra_updates = []
for name, var in states.items():
update = tensor.switch(
start_flag, 0.0 * var, VariableFilter(theano_name_regex=regex_final_value(name))(cg.auxiliary_variables)[0]
)
extra_updates.append((var, update))
def main(argv):
name = argv[1]
files = map(lambda p: join(folder, p), listdir(folder))
file = next(filter(lambda n: name in n, files))
print(file)
p = load_parameter_values(file)
net = net_dvc((128, 128))
x = tensor.tensor4('image_features')
y_hat = net.apply(x)
g = Model(y_hat)
for k, v in p.items():
p[k] = v.astype('float32')
g.set_parameter_values(p)
a, t, v = get_dvc((128, 128), trainning=False, shortcut=False)
run = function([x], y_hat)
def run_test(data):
res = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
return res
def max_index(l):
if l[0] > l[1]:
return 0
else:
return 1
def write_kaggle(f, l):
f.write("id,label\n")
for i, e in enumerate(l, start=1):
f.write(str(i) + "," + str(e) + "\n")
def kaggle(file, data):
write_kaggle(file, map(max_index, run_test(data)))
def accuracy(data):
res = []
true = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
true.extend(i[1])
res = map(max_index, res)
total = 0
equal = 0
for r, t in zip(res, true):
total += 1
equal += 1 if r == t else 0
return equal / total
print("Training accuracy: ", accuracy(a))
print("Test accuracy: ", accuracy(v))
kaggle_file = join(result_folder, name + ".kaggle")
print(kaggle_file)
with open(kaggle_file, 'w') as f:
kaggle(f, t)
def main(mode, save_path, num_batches, data_path=None):
reverser = WordReverser(100, len(char2code), name="reverser")
if mode == "train":
# Data processing pipeline
dataset_options = dict(dictionary=char2code, level="character",
preprocess=_lower)
if data_path:
dataset = TextFile(data_path, **dataset_options)
else:
dataset = OneBillionWord("training", [99], **dataset_options)
data_stream = dataset.get_example_stream()
data_stream = Filter(data_stream, _filter_long)
data_stream = Mapping(data_stream, reverse_words,
add_sources=("targets",))
data_stream = Batch(data_stream, iteration_scheme=ConstantScheme(10))
data_stream = Padding(data_stream)
data_stream = Mapping(data_stream, _transpose)
# Initialization settings
reverser.weights_init = IsotropicGaussian(0.1)
reverser.biases_init = Constant(0.0)
reverser.push_initialization_config()
reverser.encoder.weights_init = Orthogonal()
reverser.generator.transition.weights_init = Orthogonal()
# Build the cost computation graph
chars = tensor.lmatrix("features")
chars_mask = tensor.matrix("features_mask")
targets = tensor.lmatrix("targets")
targets_mask = tensor.matrix("targets_mask")
batch_cost = reverser.cost(
chars, chars_mask, targets, targets_mask).sum()
batch_size = chars.shape[1].copy(name="batch_size")
cost = aggregation.mean(batch_cost, batch_size)
cost.name = "sequence_log_likelihood"
logger.info("Cost graph is built")
# Give an idea of what's going on
model = Model(cost)
parameters = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in parameters.items()],
width=120))
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
# Define the training algorithm.
cg = ComputationGraph(cost)
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=CompositeRule([StepClipping(10.0), Scale(0.01)]))
# Fetch variables useful for debugging
generator = reverser.generator
(energies,) = VariableFilter(
applications=[generator.readout.readout],
name_regex="output")(cg.variables)
(activations,) = VariableFilter(
applications=[generator.transition.apply],
name=generator.transition.apply.states[0])(cg.variables)
max_length = chars.shape[0].copy(name="max_length")
cost_per_character = aggregation.mean(
batch_cost, batch_size * max_length).copy(
name="character_log_likelihood")
min_energy = energies.min().copy(name="min_energy")
max_energy = energies.max().copy(name="max_energy")
mean_activation = abs(activations).mean().copy(
name="mean_activation")
observables = [
cost, min_energy, max_energy, mean_activation,
batch_size, max_length, cost_per_character,
algorithm.total_step_norm, algorithm.total_gradient_norm]
for name, parameter in parameters.items():
observables.append(parameter.norm(2).copy(name + "_norm"))
observables.append(algorithm.gradients[parameter].norm(2).copy(
name + "_grad_norm"))
# Construct the main loop and start training!
average_monitoring = TrainingDataMonitoring(
observables, prefix="average", every_n_batches=10)
main_loop = MainLoop(
model=model,
data_stream=data_stream,
algorithm=algorithm,
extensions=[
Timing(),
TrainingDataMonitoring(observables, after_batch=True),
average_monitoring,
FinishAfter(after_n_batches=num_batches)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition(["after_batch"], _is_nan),
# Saving the model and the log separately is convenient,
# because loading the whole pickle takes quite some time.
Checkpoint(save_path, every_n_batches=500,
save_separately=["model", "log"]),
Printing(every_n_batches=1)])
main_loop.run()
elif mode == "sample" or mode == "beam_search":
chars = tensor.lmatrix("input")
generated = reverser.generate(chars)
model = Model(generated)
logger.info("Loading the model..")
model.set_parameter_values(load_parameter_values(save_path))
def generate(input_):
"""Generate output sequences for an input sequence.
Incapsulates most of the difference between sampling and beam
search.
Returns
-------
outputs : list of lists
Trimmed output sequences.
costs : list
The negative log-likelihood of generating the respective
sequences.
"""
if mode == "beam_search":
samples, = VariableFilter(
applications=[reverser.generator.generate], name="outputs")(
ComputationGraph(generated[1]))
# NOTE: this will recompile beam search functions
# every time user presses Enter. Do not create
# a new `BeamSearch` object every time if
# speed is important for you.
beam_search = BeamSearch(samples)
outputs, costs = beam_search.search(
{chars: input_}, char2code['</S>'],
3 * input_.shape[0])
else:
_1, outputs, _2, _3, costs = (
model.get_theano_function()(input_))
outputs = list(outputs.T)
costs = list(costs.T)
for i in range(len(outputs)):
outputs[i] = list(outputs[i])
try:
true_length = outputs[i].index(char2code['</S>']) + 1
except ValueError:
true_length = len(outputs[i])
outputs[i] = outputs[i][:true_length]
costs[i] = costs[i][:true_length].sum()
return outputs, costs
while True:
try:
line = input("Enter a sentence\n")
message = ("Enter the number of samples\n" if mode == "sample"
else "Enter the beam size\n")
batch_size = int(input(message))
except EOFError:
break
except Exception:
traceback.print_exc()
continue
encoded_input = [char2code.get(char, char2code["<UNK>"])
for char in line.lower().strip()]
encoded_input = ([char2code['<S>']] + encoded_input +
[char2code['</S>']])
print("Encoder input:", encoded_input)
target = reverse_words((encoded_input,))[0]
print("Target: ", target)
samples, costs = generate(
numpy.repeat(numpy.array(encoded_input)[:, None],
batch_size, axis=1))
messages = []
for sample, cost in equizip(samples, costs):
message = "({})".format(cost)
message += "".join(code2char[code] for code in sample)
if sample == target:
message += " CORRECT!"
messages.append((cost, message))
messages.sort(key=operator.itemgetter(0), reverse=True)
for _, message in messages:
print(message)
def main(name, epochs, batch_size, learning_rate, dim, mix_dim, old_model_name,
max_length, bokeh, GRU, dropout, depth, max_grad, step_method,
epsilon, sample):
#----------------------------------------------------------------------
datasource = name
def shnum(x):
""" Convert a positive float into a short tag-usable string
E.g.: 0 -> 0, 0.005 -> 53, 100 -> 1-2
"""
return '0' if x <= 0 else '%s%d' % (
("%e" % x)[0], -np.floor(np.log10(x)))
jobname = "%s-%dX%dm%dd%dr%sb%de%s" % (
datasource, depth, dim, mix_dim, int(
dropout * 10), shnum(learning_rate), batch_size, shnum(epsilon))
if max_length != 600:
jobname += '-L%d' % max_length
if GRU:
jobname += 'g'
if max_grad != 5.:
jobname += 'G%g' % max_grad
if step_method != 'adam':
jobname += step_method
if sample:
print("Sampling")
else:
print("\nRunning experiment %s" % jobname)
#----------------------------------------------------------------------
if depth > 1:
transition = LSTMstack(dim=dim,
depth=depth,
name="transition",
lstm_name="transition")
assert not GRU
elif GRU:
transition = GatedRecurrent(dim=dim, name="transition")
else:
transition = LSTM(dim=dim, name="transition")
emitter = SketchEmitter(mix_dim=mix_dim, epsilon=epsilon, name="emitter")
readout = Readout(readout_dim=emitter.get_dim('inputs'),
source_names=['states'],
emitter=emitter,
name="readout")
normal_inputs = [
name for name in transition.apply.sequences if 'mask' not in name
]
fork = Fork(normal_inputs, prototype=Linear(use_bias=True))
generator = SequenceGenerator(readout=readout,
transition=transition,
fork=fork)
# Initialization settings
generator.weights_init = OrthogonalGlorot()
generator.biases_init = Constant(0)
# Build the cost computation graph [steps,batch_size, 3]
x = T.tensor3('features', dtype=floatX)[:max_length, :, :]
x.tag.test_value = np.ones((max_length, batch_size, 3)).astype(np.float32)
cost = generator.cost(x)
cost.name = "sequence_log_likelihood"
# Give an idea of what's going on
model = Model(cost)
params = model.get_params()
logger.info("Parameters:\n" +
pprint.pformat([(key, value.get_value().shape)
for key, value in params.items()],
width=120))
model_size = 0
for v in params.itervalues():
s = v.get_value().shape
model_size += s[0] * (s[1] if len(s) > 1 else 1)
logger.info("Total number of parameters %d" % model_size)
#------------------------------------------------------------
extensions = []
if old_model_name == 'continue':
extensions.append(LoadFromDump(jobname))
elif old_model_name:
# or you can just load the weights without state using:
old_params = LoadFromDump(old_model_name).manager.load_parameters()
model.set_param_values(old_params)
else:
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
if sample:
assert old_model_name and old_model_name != 'continue'
Sample(generator, steps=max_length, path='.').do(None)
exit(0)
#------------------------------------------------------------
# Define the training algorithm.
cg = ComputationGraph(cost)
if dropout > 0.:
from blocks.roles import INPUT, OUTPUT
dropout_target = VariableFilter(roles=[OUTPUT],
bricks=[transition],
name_regex='states')(cg.variables)
cg = apply_dropout(cg, dropout_target, dropout)
cost = cg.outputs[0]
if step_method == 'adam':
step_rule = Adam(learning_rate)
elif step_method == 'rmsprop':
step_rule = RMSProp(learning_rate, decay_rate=0.95)
elif step_method == 'adagrad':
step_rule = AdaGrad(learning_rate)
elif step_method == 'adadelta':
step_rule = AdaDelta()
elif step_method == 'scale':
step_rule = Scale(learning_rate=0.1)
else:
raise Exception('Unknown sttep method %s' % step_method)
step_rule = CompositeRule([StepClipping(max_grad), step_rule])
algorithm = GradientDescent(cost=cost,
params=cg.parameters,
step_rule=step_rule)
#------------------------------------------------------------
observables = [cost]
# Fetch variables useful for debugging
(energies, ) = VariableFilter(applications=[generator.readout.readout],
name_regex="output")(cg.variables)
(activations, ) = VariableFilter(
applications=[generator.transition.apply],
name=generator.transition.apply.states[0])(cg.variables)
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
mean_activation = named_copy(abs(activations).mean(), "mean_activation")
observables += [min_energy, max_energy, mean_activation]
observables += [algorithm.total_step_norm, algorithm.total_gradient_norm]
for name, param in params.items():
observables.append(named_copy(param.norm(2), name + "_norm"))
observables.append(
named_copy(algorithm.gradients[param].norm(2),
name + "_grad_norm"))
#------------------------------------------------------------
datasource_fname = os.path.join(fuel.config.data_path, datasource,
datasource + '.hdf5')
train_ds = H5PYDataset(
datasource_fname, #max_length=max_length,
which_set='train',
sources=('features', ),
load_in_memory=True)
train_stream = DataStream(train_ds,
iteration_scheme=ShuffledScheme(
train_ds.num_examples, batch_size))
test_ds = H5PYDataset(
datasource_fname, #max_length=max_length,
which_set='test',
sources=('features', ),
load_in_memory=True)
test_stream = DataStream(test_ds,
iteration_scheme=SequentialScheme(
test_ds.num_examples, batch_size))
train_stream = Mapping(train_stream, _transpose)
test_stream = Mapping(test_stream, _transpose)
def stream_stats(ds, label):
itr = ds.get_epoch_iterator(as_dict=True)
batch_count = 0
examples_count = 0
for batch in itr:
batch_count += 1
examples_count += batch['features'].shape[1]
print('%s #batch %d #examples %d' %
(label, batch_count, examples_count))
stream_stats(train_stream, 'train')
stream_stats(test_stream, 'test')
extensions += [
Timing(every_n_batches=10),
TrainingDataMonitoring(observables, prefix="train",
every_n_batches=10),
DataStreamMonitoring(
[cost],
test_stream,
prefix="test",
on_resumption=True,
after_epoch=False, # by default this is True
every_n_batches=100),
# all monitored data is ready so print it...
# (next steps may take more time and we want to see the
# results as soon as possible so print as soon as you can)
Printing(every_n_batches=10),
# perform multiple dumps at different intervals
# so if one of them breaks (has nan) we can hopefully
# find a model from few batches ago in the other
Dump(jobname, every_n_batches=11),
Dump(jobname + '.test', every_n_batches=100),
Sample(generator,
steps=max_length,
path=jobname + '.test',
every_n_batches=100),
ProgressBar(),
FinishAfter(after_n_epochs=epochs)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition("after_batch", _is_nan),
]
if bokeh:
extensions.append(Plot('sketch', channels=[
['cost'],
]))
# Construct the main loop and start training!
main_loop = MainLoop(model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
