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 attach_aggregation_schemes(variables):
# Aggregation specification has to be factored out as a separate
# function as it has to be applied at the very last stage
# separately to training and validation observables.
result = []
for var in variables:
if var.name == 'weights_penalty':
result.append(named_copy(aggregation.mean(var, batch_size),
'weights_penalty_per_recording'))
elif var.name == 'weights_entropy':
result.append(named_copy(aggregation.mean(
var, recognizer.labels_mask.sum()), 'weights_entropy_per_label'))
else:
result.append(var)
return result
def apply(self, input_, application_call):
V = self.parameters[0]
mean_row_mean = mean(input_.mean(axis=1).sum(), input_.shape[0])
application_call.add_auxiliary_variable((V ** 2).sum(), name="V_squared")
application_call.add_auxiliary_variable(mean_row_mean, name="mean_row_mean")
application_call.add_auxiliary_variable(input_.mean(), name="mean_batch_element")
return input_ + V
def train(self):
print "Loading data"
datafile = self.get_datafile()
nbexamples = datafile.num_examples
nbexamples -= nbexamples%(self.sequence_dim*self.time_dim)
train_stream = ReshapeTransformer(
DataStream(
dataset=datafile,
iteration_scheme=ShuffledBatchChunkScheme(
nbexamples, self.sequence_dim*self.time_dim)),
self.sequence_dim,
self.time_dim)
if self.image_size is not None :
train_stream = Mapping(train_stream, spec_mapping, add_sources=['spectrogram'])
print "Building Theano Graph"
algorithm, self.fprop = self.build_theano_functions()
main_loop = MainLoop(
algorithm=algorithm,
data_stream=train_stream,
model=self.model,
extensions=[
FinishAfter(after_n_epochs=EPOCHS),
TrainingDataMonitoring(
[aggregation.mean(self.model.outputs[0])],
prefix="train",
after_epoch=True),
Printing(),
SaveParams(EXP_PATH+NAME, after_epoch=True)
])
main_loop.run()
def build_model(alphabet_size, config):
layers = config['lstm_layers']
dimensions = [config['lstm_dim_' + str(i)] for i in range(layers)]
uniform_width = config['lstm_init_width']
stack = []
for dim in dimensions:
stack.append(LSTM(dim=dim, use_bias=True,
weights_init = Uniform(width=uniform_width),
forget_init=Constant(1.)))
recurrent_stack = RecurrentStack(stack, name='transition')
readout = Readout(readout_dim=alphabet_size,
source_names=['states#' + str(layers - 1)],
emitter=SoftmaxEmitter(name='emitter'),
feedback_brick=LookupFeedback(alphabet_size,
feedback_dim=alphabet_size,
name='feedback'),
name='readout')
generator = SequenceGenerator(readout=readout,
transition=recurrent_stack,
weights_init=Uniform(width=uniform_width),
biases_init=Constant(0),
name='generator')
generator.push_initialization_config()
generator.initialize()
x = tensor.lmatrix('features')
mask = tensor.fmatrix('features_mask')
cost_matrix = generator.cost_matrix(x, mask=mask)
log2e = math.log(math.e, 2)
if 'batch_length' in config:
length = config['batch_length'] - config['batch_overlap']
cost = log2e * aggregation.mean(cost_matrix[:,-length:].sum(),
mask[:,-length:].sum())
else:
cost = log2e * aggregation.mean(cost_matrix[:,:].sum(),
mask[:,:].sum())
cost.name = 'bits_per_character'
return generator, cost
def cost(self, image_vects, chars):
# shape (batch, features)
image_embedding = self.image_embedding.apply(image_vects)
cost = aggregation.mean(
self.generator.cost_matrix(
chars, cnn_context=image_embedding).sum()
, chars.shape[1]
)
return cost
def main(save_to, num_epochs, bokeh=False):
mlp = MLP([Tanh(), Softmax()], [784, 100, 10],
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
mlp.initialize()
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
probs = mlp.apply(x)
cost = CategoricalCrossEntropy().apply(y.flatten(), probs)
error_rate = MisclassificationRate().apply(y.flatten(), probs)
cg = ComputationGraph([cost])
W1, W2 = VariableFilter(roles=[WEIGHT])(cg.variables)
cost = cost + .00005 * (W1 ** 2).sum() + .00005 * (W2 ** 2).sum()
cost.name = 'final_cost'
mnist_train = MNIST("train")
mnist_test = MNIST("test")
algorithm = GradientDescent(
cost=cost, params=cg.parameters,
step_rule=Scale(learning_rate=0.1))
extensions = [Timing(),
FinishAfter(after_n_epochs=num_epochs),
DataStreamMonitoring(
[cost, error_rate],
DataStream(mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, 500)),
prefix="test"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
Printing()]
if bokeh:
extensions.append(Plot(
'MNIST example',
channels=[
['test_final_cost',
'test_misclassificationrate_apply_error_rate'],
['train_total_gradient_norm']]))
main_loop = MainLoop(
algorithm,
DataStream(mnist_train,
iteration_scheme=SequentialScheme(
mnist_train.num_examples, 50)),
model=Model(cost),
extensions=extensions)
main_loop.run()
def test_training_data_monitoring():
weights = numpy.array([-1, 1], dtype=theano.config.floatX)
features = [numpy.array(f, dtype=theano.config.floatX)
for f in [[1, 2], [3, 4], [5, 6]]]
targets = [(weights * f).sum() for f in features]
n_batches = 3
dataset = IterableDataset(dict(features=features, targets=targets))
x = tensor.vector('features')
y = tensor.scalar('targets')
W = shared_floatx([0, 0], name='W')
V = shared_floatx(7, name='V')
W_sum = named_copy(W.sum(), 'W_sum')
cost = ((x * W).sum() - y) ** 2
cost.name = 'cost'
class TrueCostExtension(TrainingExtension):
def before_batch(self, data):
self.main_loop.log.current_row['true_cost'] = (
((W.get_value() * data["features"]).sum() -
data["targets"]) ** 2)
main_loop = MainLoop(
model=None, data_stream=dataset.get_example_stream(),
algorithm=GradientDescent(cost=cost, params=[W],
step_rule=Scale(0.001)),
extensions=[
FinishAfter(after_n_epochs=1),
TrainingDataMonitoring([W_sum, cost, V], prefix="train1",
after_batch=True),
TrainingDataMonitoring([aggregation.mean(W_sum), cost],
prefix="train2", after_epoch=True),
TrueCostExtension()])
main_loop.run()
# Check monitoring of a shared varible
assert_allclose(main_loop.log.current_row['train1_V'], 7.0)
for i in range(n_batches):
# The ground truth is written to the log before the batch is
# processed, where as the extension writes after the batch is
# processed. This is why the iteration numbers differs here.
assert_allclose(main_loop.log[i]['true_cost'],
main_loop.log[i + 1]['train1_cost'])
assert_allclose(
main_loop.log[n_batches]['train2_cost'],
sum([main_loop.log[i]['true_cost']
for i in range(n_batches)]) / n_batches)
assert_allclose(
main_loop.log[n_batches]['train2_W_sum'],
sum([main_loop.log[i]['train1_W_sum']
for i in range(1, n_batches + 1)]) / n_batches)
def main(save_to, num_epochs, batch_size):
mlp = MLP([Tanh(), Tanh(), Tanh(), Softmax()], [3072, 4096, 1024, 512, 10],
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
mlp.initialize()
x = tt.tensor4('features', dtype='float32')
y = tt.vector('label', dtype='int32')
probs = mlp.apply(x.reshape((-1,3072)))
cost = CategoricalCrossEntropy().apply(y, probs)
error_rate = MisclassificationRate().apply(y, probs)
cg = ComputationGraph([cost])
ws = VariableFilter(roles=[WEIGHT])(cg.variables)
cost = cost + .00005 * sum(([(w**2).sum() for w in ws]))
cost.name = 'final_cost'
train_dataset = Cifar10Dataset(data_dir='/home/belohlavek/data/cifar10', is_train=True)
valid_dataset = Cifar10Dataset(data_dir='/home/belohlavek/data/cifar10', is_train=False)
train_stream = train_dataset.get_stream(batch_size)
valid_stream = valid_dataset.get_stream(batch_size)
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=Adam(learning_rate=0.001))
extensions = [Timing(),
LogExtension('/home/belohlavek/ALI/mlp.log'),
FinishAfter(after_n_epochs=num_epochs),
DataStreamMonitoring([cost, error_rate], valid_stream, prefix="test"),
TrainingDataMonitoring(
[cost, error_rate, aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
Printing()]
main_loop = MainLoop(algorithm,
train_stream,
model=Model(cost),
extensions=extensions)
main_loop.run()
def build_and_run(label, config):
############## CREATE THE NETWORK ###############
#Define the parameters
num_epochs, num_batches, num_channels, image_shape, filter_size, num_filter, pooling_sizes, mlp_hiddens, output_size, batch_size, activation, mlp_activation = config[
'num_epochs'], config['num_batches'], config['num_channels'], config[
'image_shape'], config['filter_size'], config[
'num_filter'], config['pooling_sizes'], config[
'mlp_hiddens'], config['output_size'], config[
'batch_size'], config['activation'], config[
'mlp_activation']
# print(num_epochs, num_channels, image_shape, filter_size, num_filter, pooling_sizes, mlp_hiddens, output_size, batch_size, activation, mlp_activation)
lambda_l1 = 0.000025
lambda_l2 = 0.000025
print("Building model")
#Create the symbolics variable
x = T.tensor4('image_features')
y = T.lmatrix('targets')
#Get the parameters
conv_parameters = zip(filter_size, num_filter)
#Create the convolutions layers
conv_layers = list(
interleave([(Convolutional(filter_size=filter_size,
num_filters=num_filter,
name='conv_{}'.format(i))
for i, (filter_size,
num_filter) in enumerate(conv_parameters)),
(activation),
(MaxPooling(size, name='pool_{}'.format(i))
for i, size in enumerate(pooling_sizes))]))
# (AveragePooling(size, name='pool_{}'.format(i)) for i, size in enumerate(pooling_sizes))]))
#Create the sequence
conv_sequence = ConvolutionalSequence(conv_layers,
num_channels,
image_size=image_shape,
weights_init=Uniform(width=0.2),
biases_init=Constant(0.))
#Initialize the convnet
conv_sequence.initialize()
#Add the MLP
top_mlp_dims = [np.prod(conv_sequence.get_dim('output'))
] + mlp_hiddens + [output_size]
out = Flattener().apply(conv_sequence.apply(x))
mlp = MLP(mlp_activation,
top_mlp_dims,
weights_init=Uniform(0, 0.2),
biases_init=Constant(0.))
#Initialisze the MLP
mlp.initialize()
#Get the output
predict = mlp.apply(out)
cost = CategoricalCrossEntropy().apply(y.flatten(),
predict).copy(name='cost')
error = MisclassificationRate().apply(y.flatten(), predict)
#Little trick to plot the error rate in two different plots (We can't use two time the same data in the plot for a unknow reason)
error_rate = error.copy(name='error_rate')
error_rate2 = error.copy(name='error_rate2')
########### REGULARIZATION ##################
cg = ComputationGraph([cost])
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
biases = VariableFilter(roles=[BIAS])(cg.variables)
# # l2_penalty_weights = T.sum([i*lambda_l2/len(weights) * (W ** 2).sum() for i,W in enumerate(weights)]) # Gradually increase penalty for layer
l2_penalty = T.sum([
lambda_l2 * (W**2).sum() for i, W in enumerate(weights + biases)
]) # Gradually increase penalty for layer
# # #l2_penalty_bias = T.sum([lambda_l2*(B **2).sum() for B in biases])
# # #l2_penalty = l2_penalty_weights + l2_penalty_bias
l2_penalty.name = 'l2_penalty'
l1_penalty = T.sum([lambda_l1 * T.abs_(z).sum() for z in weights + biases])
# l1_penalty_weights = T.sum([i*lambda_l1/len(weights) * T.abs_(W).sum() for i,W in enumerate(weights)]) # Gradually increase penalty for layer
# l1_penalty_biases = T.sum([lambda_l1 * T.abs_(B).sum() for B in biases])
# l1_penalty = l1_penalty_biases + l1_penalty_weights
l1_penalty.name = 'l1_penalty'
costreg = cost + l2_penalty + l1_penalty
costreg.name = 'costreg'
########### DEFINE THE ALGORITHM #############
# algorithm = GradientDescent(cost=cost, parameters=cg.parameters, step_rule=Momentum())
algorithm = GradientDescent(cost=costreg,
parameters=cg.parameters,
step_rule=Adam())
########### GET THE DATA #####################
istest = 'test' in config.keys()
train_stream, valid_stream, test_stream = get_stream(batch_size,
image_shape,
test=istest)
########### INITIALIZING EXTENSIONS ##########
checkpoint = Checkpoint('models/best_' + label + '.tar')
checkpoint.add_condition(
['after_epoch'], predicate=OnLogRecord('valid_error_rate_best_so_far'))
#Adding a live plot with the bokeh server
plot = Plot(
label,
channels=[
['train_error_rate', 'valid_error_rate'],
['valid_cost', 'valid_error_rate2'],
# ['train_costreg','train_grad_norm']], #
[
'train_costreg', 'train_total_gradient_norm',
'train_l2_penalty', 'train_l1_penalty'
]
],
server_url="http://hades.calculquebec.ca:5042")
grad_norm = aggregation.mean(algorithm.total_gradient_norm)
grad_norm.name = 'grad_norm'
extensions = [
Timing(),
FinishAfter(after_n_epochs=num_epochs, after_n_batches=num_batches),
DataStreamMonitoring([cost, error_rate, error_rate2],
valid_stream,
prefix="valid"),
TrainingDataMonitoring([
costreg, error_rate, error_rate2, grad_norm, l2_penalty, l1_penalty
],
prefix="train",
after_epoch=True),
plot,
ProgressBar(),
Printing(),
TrackTheBest('valid_error_rate', min), #Keep best
checkpoint, #Save best
FinishIfNoImprovementAfter('valid_error_rate_best_so_far', epochs=4)
] # Early-stopping
model = Model(cost)
main_loop = MainLoop(algorithm,
data_stream=train_stream,
model=model,
extensions=extensions)
main_loop.run()
def main(port_data):
mlp_hiddens = [500]
filter_sizes = [(3, 3), (3, 3)]
feature_maps = [20, 20]
pooling_sizes = [(3, 3), (2, 2)]
save_to = "DvC.pkl"
image_size = (128, 128)
output_size = 2
learningRate = 0.1
num_epochs = 300
num_batches = None
if socket.gethostname() == 'tim-X550JX':
host_plot = 'http://*****:*****@ %s' %
('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']],
after_epoch=True,
server_url=host_plot))
model = Model(cost)
main_loop = MainLoop(algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
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 = (32, 23)
batch_size = 50
output_size = 2
learningRate = 0.1
num_epochs = 10
num_batches = None
# 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,
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('image_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])
########### Loading images #####################
from fuel.datasets.dogs_vs_cats import DogsVsCats
from fuel.streams import DataStream, ServerDataStream
from fuel.schemes import ShuffledScheme
from fuel.transformers.image import RandomFixedSizeCrop, MinimumImageDimensions, Random2DRotation
from fuel.transformers import Flatten, Cast, ScaleAndShift
def create_data(data):
stream = DataStream.default_stream(data,
iteration_scheme=ShuffledScheme(
data.num_examples, batch_size))
stream_downscale = MinimumImageDimensions(
stream, image_size, which_sources=('image_features', ))
#stream_rotate = Random2DRotation(stream_downscale, which_sources=('image_features',))
stream_max = ScikitResize(stream_downscale,
image_size,
which_sources=('image_features', ))
stream_scale = ScaleAndShift(stream_max,
1. / 255,
0,
which_sources=('image_features', ))
stream_cast = Cast(stream_scale,
dtype='float32',
which_sources=('image_features', ))
#stream_flat = Flatten(stream_scale, which_sources=('image_features',))
return stream_cast
stream_data_train = create_data(
DogsVsCats(('train', ), subset=slice(0, 20000)))
stream_data_test = create_data(
DogsVsCats(('train', ), subset=slice(20000, 25000)))
# Train with simple SGD
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=Scale(learning_rate=learningRate))
# `Timing` extension reports time for reading data, aggregating a batch
# and monitoring;
# `ProgressBar` displays a nice progress bar during training.
extensions = []
extensions.append(Timing())
extensions.append(
FinishAfter(after_n_epochs=num_epochs, after_n_batches=num_batches))
extensions.append(
DataStreamMonitoring([cost, error_rate],
stream_data_test,
prefix="valid"))
extensions.append(
TrainingDataMonitoring([
cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)
],
prefix="train",
after_epoch=True))
extensions.append(Checkpoint(save_to))
extensions.append(ProgressBar())
extensions.append(Printing())
model = Model(cost)
########### Loading images #####################
main_loop = MainLoop(algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
def train(model, batch_size=100, num_epochs=1000):
cost = model.cost
monitorings = model.monitorings
# Setting Loggesetr
timestr = time.strftime("%Y_%m_%d_at_%H_%M")
save_path = 'results/CMV_V2_' + timestr
log_path = os.path.join(save_path, 'log.txt')
os.makedirs(save_path)
fh = logging.FileHandler(filename=log_path)
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
# Training
blocks_model = Model(cost)
all_params = blocks_model.parameters
print "Number of found parameters:" + str(len(all_params))
print all_params
clipping = StepClipping(threshold=np.cast[floatX](10))
adam = Adam(learning_rate=model.lr_var)
step_rule = CompositeRule([adam, clipping])
training_algorithm = GradientDescent(cost=cost,
parameters=all_params,
step_rule=step_rule)
monitored_variables = [
model.lr_var, cost,
aggregation.mean(training_algorithm.total_gradient_norm)
] + monitorings
blocks_model = Model(cost)
params_dicts = blocks_model.get_parameter_dict()
for name, param in params_dicts.iteritems():
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_cmv_v2_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_misclassificationrate_apply_error_rate',
blocks_model, save_path),
SaveLog(save_path, after_epoch=True),
ProgressBar(),
LRDecay(model.lr_var, [0.001, 0.0001, 0.00001, 0.000001],
[8, 15, 30, 1000],
after_epoch=True),
Printing()
])
main_loop.run()
def main(args):
"""Run experiment. """
lr_tag = float_tag(args.learning_rate)
x_dim, train_stream, valid_stream, test_stream = datasets.get_streams(args.data, args.batch_size)
#------------------------------------------------------------
# Setup model
deterministic_act = Tanh
deterministic_size = 1.
if args.method == 'vae':
sizes_tag = args.layer_spec.replace(",", "-")
layer_sizes = [int(i) for i in args.layer_spec.split(",")]
layer_sizes, z_dim = layer_sizes[:-1], layer_sizes[-1]
name = "%s-%s-%s-lr%s-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.n_samples, sizes_tag)
if args.activation == "tanh":
hidden_act = Tanh()
elif args.activation == "logistic":
hidden_act = Logistic()
elif args.activation == "relu":
hidden_act = Rectifier()
else:
raise "Unknown hidden nonlinearity %s" % args.hidden_act
model = VAE(x_dim=x_dim, hidden_layers=layer_sizes, hidden_act=hidden_act, z_dim=z_dim,
batch_norm=args.batch_normalization)
model.initialize()
elif args.method == 'rws':
sizes_tag = args.layer_spec.replace(",", "-")
name = "%s-%s-%s-lr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(
args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act, deterministic_size)
model = ReweightedWakeSleep(
p_layers,
q_layers,
)
model.initialize()
elif args.method == 'bihm':
sizes_tag = args.layer_spec.replace(",", "-")
name = "%s-%s-%s-lr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(
args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act, deterministic_size)
model = BiHM(
p_layers,
q_layers,
l1reg=args.l1reg,
l2reg=args.l2reg,
)
model.initialize()
elif args.method == 'continue':
import cPickle as pickle
from os.path import basename, splitext
with open(args.model_file, 'rb') as f:
m = pickle.load(f)
if isinstance(m, MainLoop):
m = m.model
model = m.get_top_bricks()[0]
while len(model.parents) > 0:
model = model.parents[0]
assert isinstance(model, (BiHM, ReweightedWakeSleep, VAE))
mname, _, _ = basename(args.model_file).rpartition("_model.pkl")
name = "%s-cont-%s-lr%s-spl%s" % (mname, args.name, lr_tag, args.n_samples)
else:
raise ValueError("Unknown training method '%s'" % args.method)
#------------------------------------------------------------
x = tensor.matrix('features')
#------------------------------------------------------------
# Testset monitoring
train_monitors = []
valid_monitors = []
test_monitors = []
for s in [1, 10, 100, 1000,]:
log_p, log_ph = model.log_likelihood(x, s)
log_p = -log_p.mean()
log_ph = -log_ph.mean()
log_p.name = "log_p_%d" % s
log_ph.name = "log_ph_%d" % s
#valid_monitors += [log_p, log_ph]
test_monitors += [log_p, log_ph]
#------------------------------------------------------------
# Z estimation
#for s in [100000]:
# z2 = tensor.exp(model.estimate_log_z2(s)) / s
# z2.name = "z2_%d" % s
#
# valid_monitors += [z2]
# test_monitors += [z2]
#------------------------------------------------------------
# Gradient and training monitoring
if args.method in ['vae', 'dvae']:
log_p_bound = model.log_likelihood_bound(x, args.n_samples)
gradients = None
log_p_bound = -log_p_bound.mean()
log_p_bound.name = "log_p_bound"
cost = log_p_bound
train_monitors += [log_p_bound, named(model.kl_term.mean(), 'kl_term'), named(model.recons_term.mean(), 'recons_term')]
valid_monitors += [log_p_bound, named(model.kl_term.mean(), 'kl_term'), named(model.recons_term.mean(), 'recons_term')]
test_monitors += [log_p_bound, named(model.kl_term.mean(), 'kl_term'), named(model.recons_term.mean(), 'recons_term')]
else:
log_p, log_ph, gradients = model.get_gradients(x, args.n_samples)
log_p_bound = named( -model.log_p_bound.mean(), "log_p_bound")
log_p = named( -log_p.mean(), "log_p")
log_ph = named( -log_ph.mean(), "log_ph")
cost = log_p
train_monitors += [log_p_bound, log_p, log_ph]
valid_monitors += [log_p_bound, log_p, log_ph]
#------------------------------------------------------------
cg = ComputationGraph([cost])
if args.step_rule == "momentum":
step_rule = Momentum(args.learning_rate, 0.95)
elif args.step_rule == "rmsprop":
step_rule = RMSProp(args.learning_rate)
elif args.step_rule == "adam":
step_rule = Adam(args.learning_rate)
else:
raise "Unknown step_rule %s" % args.step_rule
parameters = cg.parameters
algorithm = GradientDescent(
cost=cost,
parameters=parameters,
gradients=gradients,
step_rule=CompositeRule([
step_rule,
])
)
#------------------------------------------------------------
train_monitors += [aggregation.mean(algorithm.total_gradient_norm),
aggregation.mean(algorithm.total_step_norm)]
#------------------------------------------------------------
# Live plotting?
plotting_extensions = []
if args.live_plotting:
plotting_extensions = [
PlotManager(
name,
[Plotter(channels=[
["valid_%s" % cost.name, "valid_log_p"],
["train_total_gradient_norm", "train_total_step_norm"]],
titles=[
"validation cost",
"norm of training gradient and step"
]),
DisplayImage([
WeightDisplay(
model.p_layers[0].mlp.linear_transformations[0].W,
n_weights=100, image_shape=(28, 28))]
#ImageDataStreamDisplay(test_stream, image_shape=(28,28))]
)]
)
]
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[Timing(),
ProgressBar(),
TrainingDataMonitoring(
train_monitors,
prefix="train",
after_epoch=False,
after_batch=True),
DataStreamMonitoring(
valid_monitors,
data_stream=valid_stream,
prefix="valid"),
DataStreamMonitoring(
test_monitors,
data_stream=test_stream,
prefix="test",
after_epoch=False,
after_training=True,
every_n_epochs=10),
TrackTheBest('valid_%s' % cost.name),
Checkpoint(name+".pkl", save_separately=['log', 'model']),
FinishIfNoImprovementAfter('valid_%s_best_so_far' % cost.name, epochs=args.patience),
FinishAfter(after_n_epochs=args.max_epochs),
Printing()] + plotting_extensions)
main_loop.run()
def train(step_rule, input_dim, state_dim, label_dim, layers, epochs, seed,
pretrain_alignment, uniform_alignment, dropout, beam_search,
test_cost, experiment_path, window_features, features, pool_size,
maximum_frames, initialization, weight_noise, to_watch, patience,
plot, write_predictions, static_mask, drop_prob, drop_prob_states,
drop_prob_cells, drop_prob_igates, ogates_zoneout, batch_size,
stoch_depth, share_mask, gaussian_drop, rnn_type, num_layers,
norm_cost_coeff, penalty, seq_len, input_drop, augment, **kwargs):
print '.. PTB experiment'
print '.. arguments:', ' '.join(sys.argv)
t0 = time.time()
###########################################
#
# LOAD DATA
#
###########################################
def numpy_rng(random_seed=None):
if random_seed == None:
random_seed = 1223
return numpy.random.RandomState(random_seed)
#from utilities import onehot, unhot, vec2chars
# from http://www.iro.umontreal.ca/~memisevr/code/logreg.py
#def onehot(x,numclasses=None):
#""" Convert integer encoding for class-labels (starting with 0 !)
#to one-hot encoding.
#The output is an array who's shape is the shape of the input array plus
#an extra dimension, containing the 'one-hot'-encoded labels.
#"""
#if x.shape==():
#x = x[None]
#if numclasses is None:
#numclasses = x.max() + 1
#result = numpy.zeros(list(x.shape) + [numclasses], dtype="int")
#z = numpy.zeros(x.shape, dtype="int")
#for c in range(numclasses):
#z *= 0
#z[numpy.where(x==c)] = 1
#result[...,c] += z
#return result.astype(theano.config.floatX)
#framelen = 1
#50 = 50
##data = np.load(os.path.join(os.environ['FUEL_DATA_PATH'], 'PennTreebankCorpus/char_level_penntree.npz'))#pentree_char_and_word.npz')
#data = np.load('char_level_penntree.npz')
#trainset = data['train']
#validset = data['valid']
#allletters = " etanoisrhludcmfpkgybw<>\nvN.'xj$-qz&0193#285\\764/*"
#dictionary = dict(zip(list(set(allletters)), range(50)))
#invdict = {v: k for k, v in dictionary.items()}
#numtrain = len(trainset) / seq_len * seq_len
#numvalid = len(validset) / seq_len * seq_len
#trainset = trainset[:numtrain]
#validset = validset[:numvalid]
##if testing:
## train_features_numpy = train_features_numpy[:32 * 5]
## valid_features_numpy = valid_features_numpy[:100]
#train_targets = trainset.reshape(-1, seq_len*framelen)[:,1:]
#valid_targets = validset.reshape(-1, seq_len*framelen)[:,1:]
## still only 2d (b, t*n)
#train_features_numpy = onehot(trainset).reshape(-1, 50*seq_len*framelen)[:,:-50]
#valid_features_numpy = onehot(validset).reshape(-1, 50*seq_len*framelen)[:,:-50]
#del trainset, validset
#data_loaded = True
#print '... done'
#test_value = train_features_numpy[:32]
####################
###########################################
#
# MAKE STREAMS
#
###########################################
rng = np.random.RandomState(seed)
stream_args = dict(rng=rng,
pool_size=pool_size,
maximum_frames=maximum_frames,
pretrain_alignment=pretrain_alignment,
uniform_alignment=uniform_alignment,
window_features=window_features)
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
# the threes in here are because the number of layers is hardcoded to 3 atm. NIPS!
print '.. initializing iterators'
# train_stream, valid_stream = get_seq_mnist_streams(
# h_dim, batch_size, update_prob)
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)
#train_dataset = Timit('train', features=features)
# assert (train_features_numpy[:,-50:].sum(axis=-2)==1).all()
#train_features_numpy = train_features_numpy.reshape(-1, seq_len-1, 50)#BTN for shuffled dataset?
#train_dataset = IndexableDataset(indexables=OrderedDict(
#[('features', train_features_numpy),
#('outputs', train_targets)]))
#train_stream = construct_stream_np(train_dataset, state_dim, batch_size, len(train_targets),
#drop_prob_states, drop_prob_cells, drop_prob_igates,
#num_layers=num_layers,
#is_for_test=False, stoch_depth=stoch_depth, share_mask=share_mask,
#gaussian_drop=gaussian_drop, input_drop=input_drop, **stream_args)
##dev_dataset = Timit('dev', features=features)
#valid_features_numpy = valid_features_numpy.reshape(-1, seq_len-1, 50)
#dev_dataset = IndexableDataset(indexables=OrderedDict(
#[('features', valid_features_numpy),
#('outputs', valid_targets)]))
#dev_stream = construct_stream_np(dev_dataset, state_dim, batch_size, len(valid_targets),
#drop_prob_states, drop_prob_cells, drop_prob_igates,
#num_layers=num_layers,
#is_for_test=True, stoch_depth=stoch_depth, share_mask=share_mask,
#gaussian_drop=gaussian_drop, input_drop=input_drop, **stream_args)
##test_dataset = Timit('test', features=features)
##test_stream = construct_stream(test_dataset, state_dim, drop_prob_states, drop_prob_cells, drop_prob_igates, 3,
## is_for_test=True, stoch_depth=stoch_depth, share_mask=share_mask,
## gaussian_drop=gaussian_drop, **stream_args)
data = train_stream.get_epoch_iterator(as_dict=True).next()
#import ipdb; ipdb.set_trace()
#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>']
####################
###########################################
#
# BUILD MODEL
#
###########################################
print '.. building model'
x = T.tensor3('features', dtype=floatX)
x, y = x[:-1], x[1:] #T.lmatrix('outputs')# phonemes')
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 = DropLSTM(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 = DropGRU(dim=state_dim,
weights_init=weights_init,
activation=Tanh(),
name='rnn')
elif rnn_type.lower() == 'srnn': #FIXME!!! make ReLU
in_to_hid = Linear(50,
state_dim,
name='in_to_hid',
weights_init=weights_init,
biases_init=Constant(0.0))
recurrent_layer = DropSimpleRecurrent(dim=state_dim,
weights_init=weights_init,
activation=Rectifier(),
name='rnn')
else:
raise NotImplementedError
#lstm2 = DropLSTM(dim=state_dim, activation=Tanh(), model_type=6)
#lstm3 = DropLSTM(dim=state_dim, activation=Tanh(), model_type=6)
#encoder = DropMultiLayerEncoder(weights_init=weights_init,
#biases_init=Constant(.0),
#networks=[lstm1, lstm2, bidir3],
#dims=[input_dim * window_features,
#state_dim,
#state_dim,
#state_dim,
#label_dim + 1])
#encoder.initialize()
#drops_states = [drops_forw_states, drops_back_states]
#drops_cells = [drops_forw_cells, drops_back_cells]
#drops_igates = [drops_forw_igates, drops_back_igates]
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 AND MONITORS
#
###########################################
# 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])
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
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))
## 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()
norm_cost.name = 'norm_cost'
#cost_valid = cost_train
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])
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
##################### DK ADD COST ########################
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")
#train_ctc_monitor = CTCMonitoring(
#x, input_mask,
#drops_forw_states, drops_forw_cells, drops_forw_igates,
#drops_back_states, drops_back_cells, drops_back_igates,
#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,
#drops_forw_states, drops_forw_cells, drops_forw_igates,
#drops_back_states, drops_back_cells, drops_back_igates,
#y_hat, eol_symbol, dev_stream,
#prefix='dev', every_n_epochs=1,
#phoneme_dict=phoneme_dict,
#black_list=black_list)
extensions = []
# /u/pezeshki/speech_project/five_layer_timit/trained_params_best.npz
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
#_evaluator = CTCEvaluator(eol_symbol, x, input_mask, y_hat,
#phoneme_dict=phoneme_dict,
#black_list=black_list)
#logger.info("CTC monitoring on TEST data started")
#value_dict = _evaluator.evaluate(test_stream, False)
#print value_dict.items()
#logger.info("CTC monitoring on TEST data finished")
#logger.info("CTC monitoring on TRAIN data started")
#value_dict = _evaluator.evaluate(train_stream, True)
#print value_dict.items()
#logger.info("CTC monitoring on TRAIN data finished")
#logger.info("CTC monitoring on DEV data started")
#value_dict = _evaluator.evaluate(dev_stream, False)
#print value_dict.items()
#logger.info("CTC monitoring on DEV data finished")
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_monitor, cost_per_character],
data_stream=test_stream,
prefix="test")
extensions.append(test_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_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(mode, save_path, num_batches, data_path=None):
# Experiment configuration
dimension = 100
readout_dimension = len(char2code)
# Build bricks
encoder = Bidirectional(SimpleRecurrent(dim=dimension, activation=Tanh()),
weights_init=Orthogonal())
fork = Fork(
[name for name in encoder.prototype.apply.sequences if name != 'mask'],
weights_init=IsotropicGaussian(0.1),
biases_init=Constant(0))
fork.input_dim = dimension
fork.output_dims = {name: dimension for name in fork.input_names}
lookup = LookupTable(readout_dimension,
dimension,
weights_init=IsotropicGaussian(0.1))
transition = SimpleRecurrent(activation=Tanh(),
dim=dimension,
name="transition")
attention = SequenceContentAttention(state_names=transition.apply.states,
sequence_dim=2 * dimension,
match_dim=dimension,
name="attention")
readout = LinearReadout(readout_dim=readout_dimension,
source_names=["states"],
emitter=SoftmaxEmitter(name="emitter"),
feedbacker=LookupFeedback(readout_dimension,
dimension),
name="readout")
generator = SequenceGenerator(readout=readout,
transition=transition,
attention=attention,
weights_init=IsotropicGaussian(0.1),
biases_init=Constant(0),
name="generator")
generator.push_initialization_config()
transition.weights_init = Orthogonal()
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 = DataStreamMapping(
mapping=_transpose,
data_stream=PaddingDataStream(
BatchDataStream(
iteration_scheme=ConstantScheme(10),
data_stream=DataStreamMapping(
mapping=reverse_words,
add_sources=("targets", ),
data_stream=DataStreamFilter(
predicate=_filter_long,
data_stream=dataset.get_default_stream())))))
# 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 = generator.cost(
targets,
targets_mask,
attended=encoder.apply(**dict_union(fork.apply(
lookup.lookup(chars), return_dict=True),
mask=chars_mask)),
attended_mask=chars_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)
params = model.get_params()
logger.info("Parameters:\n" +
pprint.pformat([(key, value.get_value().shape)
for key, value in params.items()],
width=120))
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
# Fetch variables useful for debugging
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")
cg = ComputationGraph(cost)
(energies, ) = VariableFilter(application=readout.readout,
name="output")(cg.variables)
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
(activations, ) = VariableFilter(
application=generator.transition.apply,
name="states")(cg.variables)
mean_activation = named_copy(
abs(activations).mean(), "mean_activation")
# Define the training algorithm.
algorithm = GradientDescent(cost=cost,
step_rule=CompositeRule(
[StepClipping(10.0),
Scale(0.01)]))
# More variables for debugging
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, 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"))
# 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_every_batch=True),
average_monitoring,
FinishAfter(after_n_batches=num_batches).add_condition(
"after_batch", _is_nan),
Plot(os.path.basename(save_path),
[[average_monitoring.record_name(cost)],
[average_monitoring.record_name(cost_per_character)]],
every_n_batches=10),
SerializeMainLoop(save_path,
every_n_batches=500,
save_separately=["model", "log"]),
Printing(every_n_batches=1)
])
main_loop.run()
elif mode == "test":
logger.info("Model is loaded")
chars = tensor.lmatrix("features")
generated = generator.generate(
n_steps=3 * chars.shape[0],
batch_size=chars.shape[1],
attended=encoder.apply(**dict_union(
fork.apply(lookup.lookup(chars), return_dict=True))),
attended_mask=tensor.ones(chars.shape))
model = Model(generated)
model.set_param_values(load_parameter_values(save_path))
sample_function = model.get_theano_function()
logging.info("Sampling function is compiled")
while True:
# Python 2-3 compatibility
line = input("Enter a sentence\n")
batch_size = int(input("Enter a number of samples\n"))
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)
states, samples, glimpses, weights, costs = sample_function(
numpy.repeat(numpy.array(encoded_input)[:, None],
batch_size,
axis=1))
messages = []
for i in range(samples.shape[1]):
sample = list(samples[:, i])
try:
true_length = sample.index(char2code['</S>']) + 1
except ValueError:
true_length = len(sample)
sample = sample[:true_length]
cost = costs[:true_length, i].sum()
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, dataset, epochs, batch_size, learning_rate, attention,
n_iter, enc_dim, dec_dim, z_dim, oldmodel, live_plotting):
image_size, channels, data_train, data_valid, data_test = datasets.get_data(dataset)
train_stream = Flatten(DataStream.default_stream(data_train, iteration_scheme=SequentialScheme(data_train.num_examples, batch_size)))
valid_stream = Flatten(DataStream.default_stream(data_valid, iteration_scheme=SequentialScheme(data_valid.num_examples, batch_size)))
test_stream = Flatten(DataStream.default_stream(data_test, iteration_scheme=SequentialScheme(data_test.num_examples, batch_size)))
if name is None:
name = dataset
img_height, img_width = image_size
x_dim = channels * img_height * img_width
rnninits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
# Configure attention mechanism
if attention != "":
read_N, write_N = attention.split(',')
read_N = int(read_N)
write_N = int(write_N)
read_dim = 2 * channels * read_N ** 2
reader = AttentionReader(x_dim=x_dim, dec_dim=dec_dim,
channels=channels, width=img_width, height=img_height,
N=read_N, **inits)
writer = AttentionWriter(input_dim=dec_dim, output_dim=x_dim,
channels=channels, width=img_width, height=img_height,
N=write_N, **inits)
attention_tag = "r%d-w%d" % (read_N, write_N)
else:
read_dim = 2*x_dim
reader = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer = Writer(input_dim=dec_dim, output_dim=x_dim, **inits)
attention_tag = "full"
#----------------------------------------------------------------------
if name is None:
name = dataset
# Learning rate
def lr_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
exp = np.floor(np.log10(value))
leading = ("%e"%value)[0]
return "%s%d" % (leading, -exp)
lr_str = lr_tag(learning_rate)
subdir = name + "-" + time.strftime("%Y%m%d-%H%M%S");
longname = "%s-%s-t%d-enc%d-dec%d-z%d-lr%s" % (dataset, attention_tag, n_iter, enc_dim, dec_dim, z_dim, lr_str)
pickle_file = subdir + "/" + longname + ".pkl"
print("\nRunning experiment %s" % longname)
print(" dataset: %s" % dataset)
print(" subdirectory: %s" % subdir)
print(" learning rate: %g" % learning_rate)
print(" attention: %s" % attention)
print(" n_iterations: %d" % n_iter)
print(" encoder dimension: %d" % enc_dim)
print(" z dimension: %d" % z_dim)
print(" decoder dimension: %d" % dec_dim)
print(" batch size: %d" % batch_size)
print(" epochs: %d" % epochs)
print()
#----------------------------------------------------------------------
encoder_rnn = LSTM(dim=enc_dim, name="RNN_enc", **rnninits)
decoder_rnn = LSTM(dim=dec_dim, name="RNN_dec", **rnninits)
encoder_mlp = MLP([Identity()], [(read_dim+dec_dim), 4*enc_dim], name="MLP_enc", **inits)
decoder_mlp = MLP([Identity()], [ z_dim, 4*dec_dim], name="MLP_dec", **inits)
q_sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, **inits)
draw = DrawModel(
n_iter,
reader=reader,
encoder_mlp=encoder_mlp,
encoder_rnn=encoder_rnn,
sampler=q_sampler,
decoder_mlp=decoder_mlp,
decoder_rnn=decoder_rnn,
writer=writer)
draw.initialize()
#------------------------------------------------------------------------
x = tensor.matrix('features')
x_recons, kl_terms = draw.reconstruct(x)
recons_term = BinaryCrossEntropy().apply(x, x_recons)
recons_term.name = "recons_term"
cost = recons_term + kl_terms.sum(axis=0).mean()
cost.name = "nll_bound"
#------------------------------------------------------------
cg = ComputationGraph([cost])
params = VariableFilter(roles=[PARAMETER])(cg.variables)
algorithm = GradientDescent(
cost=cost,
parameters=params,
step_rule=CompositeRule([
StepClipping(10.),
Adam(learning_rate),
])
#step_rule=RMSProp(learning_rate),
#step_rule=Momentum(learning_rate=learning_rate, momentum=0.95)
)
#------------------------------------------------------------------------
# Setup monitors
monitors = [cost]
for t in range(n_iter):
kl_term_t = kl_terms[t,:].mean()
kl_term_t.name = "kl_term_%d" % t
#x_recons_t = T.nnet.sigmoid(c[t,:,:])
#recons_term_t = BinaryCrossEntropy().apply(x, x_recons_t)
#recons_term_t = recons_term_t.mean()
#recons_term_t.name = "recons_term_%d" % t
monitors +=[kl_term_t]
train_monitors = monitors[:]
train_monitors += [aggregation.mean(algorithm.total_gradient_norm)]
train_monitors += [aggregation.mean(algorithm.total_step_norm)]
# Live plotting...
plot_channels = [
["train_nll_bound", "test_nll_bound"],
["train_kl_term_%d" % t for t in range(n_iter)],
#["train_recons_term_%d" % t for t in range(n_iter)],
["train_total_gradient_norm", "train_total_step_norm"]
]
#------------------------------------------------------------
if not os.path.exists(subdir):
os.makedirs(subdir)
plotting_extensions = []
if live_plotting:
plotting_extensions = [
Plot(name, channels=plot_channels)
]
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
Timing(),
FinishAfter(after_n_epochs=epochs),
TrainingDataMonitoring(
train_monitors,
prefix="train",
after_epoch=True),
# DataStreamMonitoring(
# monitors,
# valid_stream,
## updates=scan_updates,
# prefix="valid"),
DataStreamMonitoring(
monitors,
test_stream,
# updates=scan_updates,
prefix="test"),
#Checkpoint(name, before_training=False, after_epoch=True, save_separately=['log', 'model']),
PartsOnlyCheckpoint("{}/{}".format(subdir,name), before_training=True, after_epoch=True, save_separately=['log', 'model']),
SampleCheckpoint(image_size=image_size[0], channels=channels, save_subdir=subdir, before_training=True, after_epoch=True),
ProgressBar(),
Printing()] + plotting_extensions)
if oldmodel is not None:
print("Initializing parameters with old model %s"%oldmodel)
with open(oldmodel, "rb") as f:
oldmodel = pickle.load(f)
main_loop.model.set_param_values(oldmodel.get_param_values())
del oldmodel
main_loop.run()
def _pokemon_wgan_gp():
import os
os.environ["FUEL_DATA_PATH"] = os.getcwd() + "/data/"
batch_size = 20
data_train = PokemonGenYellowNormal(which_sets=['train'],
sources=['features'])
train_stream = Flatten(DataStream.default_stream(
data_train, iteration_scheme=SequentialScheme(
data_train.num_examples, batch_size)))
features_size = 56 * 56 * 1
inits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.)
}
# print train_stream.get_epoch_iterator(as_dict=True).next()
# raise
inputs = T.matrix('features')
inputs = ((inputs / 255.) * 2. - 1.)
rng = MRG_RandomStreams(123)
prior = Z_prior(dim=512)
gen = Generator(input_dim=512, dims=[512, 512, 512, 512,
features_size],
alpha=0.1, **inits)
dis = Discriminator(dims=[features_size, 512, 512 , 512, 512],
alpha=0.1, **inits)
gan = GAN(dis=dis, gen=gen, prior=prior)
gan.initialize()
# gradient penalty
fake_samples, _ = gan.sampling(inputs.shape[0])
e = rng.uniform(size=(inputs.shape[0], 1))
mixed_input = (e * fake_samples) + (1 - e) * inputs
output_d_mixed = gan._dis.apply(mixed_input)
grad_mixed = T.grad(T.sum(output_d_mixed), mixed_input)
norm_grad_mixed = T.sqrt(T.sum(T.square(grad_mixed), axis=1))
grad_penalty = T.mean(T.square(norm_grad_mixed -1))
y_hat1, y_hat0, z = gan.apply(inputs)
d_loss_real = y_hat1.mean()
d_loss_fake = y_hat0.mean()
d_loss = - d_loss_real + d_loss_fake + 10 * grad_penalty
g_loss = - d_loss_fake
dis_obj = d_loss
gen_obj = g_loss
model = Model([y_hat0, y_hat1])
em_loss = -d_loss_real + d_loss_fake
em_loss.name = "Earth Move loss"
dis_obj.name = 'Discriminator loss'
gen_obj.name = 'Generator loss'
cg = ComputationGraph([gen_obj, dis_obj])
gen_filter = VariableFilter(roles=[PARAMETER],
bricks=gen.linear_transformations)
dis_filter = VariableFilter(roles=[PARAMETER],
bricks=dis.linear_transformations)
gen_params = gen_filter(cg.variables)
dis_params = dis_filter(cg.variables)
# Prepare the dropout
_inputs = []
for brick_ in [gen]:
_inputs.extend(VariableFilter(roles=[INPUT],
bricks=brick_.linear_transformations)(cg.variables))
cg_dropout = apply_dropout(cg, _inputs, 0.02)
gen_obj = cg_dropout.outputs[0]
dis_obj = cg_dropout.outputs[1]
gan.dis_params = dis_params
gan.gen_params = gen_params
# gradient penalty
algo = AdverserialTraning(gen_obj=gen_obj, dis_obj=dis_obj,
model=gan, dis_iter=5, gradient_clip=None,
step_rule=RMSProp(learning_rate=1e-4),
gen_consider_constant=z)
neg_sample = gan.sampling(size=25)
from blocks.monitoring.aggregation import mean
monitor = TrainingDataMonitoring(variables=[mean(gen_obj), mean(dis_obj),
mean(em_loss)],
prefix="train", after_batch=True)
subdir = './exp/' + 'pokemon-wgan-gp' + "-" + time.strftime("%Y%m%d-%H%M%S")
check_point = Checkpoint("{}/{}".format(subdir, 'CIFAR10'),
every_n_epochs=100,
save_separately=['log', 'model'])
neg_sampling = GenerateNegtiveSample(neg_sample,
img_size=(25, 56, 56),
every_n_epochs=10)
if not os.path.exists(subdir):
os.makedirs(subdir)
main_loop = MainLoop(algorithm=algo, model=model,
data_stream=train_stream,
extensions=[Printing(), ProgressBar(), monitor,
check_point, neg_sampling])
main_loop.run()
def main(name, epochs, batch_size, learning_rate):
if name is None:
name = "att-rw"
print("\nRunning experiment %s" % name)
print(" learning rate: %5.3f" % learning_rate)
print()
#------------------------------------------------------------------------
img_height, img_width = 28, 28
read_N = 12
write_N = 14
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.001),
'biases_init': Constant(0.),
}
x_dim = img_height * img_width
reader = ZoomableAttentionWindow(img_height, img_width, read_N)
writer = ZoomableAttentionWindow(img_height, img_width, write_N)
# Parameterize the attention reader and writer
mlpr = MLP(activations=[Tanh(), Identity()],
dims=[x_dim, 50, 5],
name="RMLP",
**inits)
mlpw = MLP(activations=[Tanh(), Identity()],
dims=[x_dim, 50, 5],
name="WMLP",
**inits)
# MLP between the reader and writer
mlp = MLP(activations=[Tanh(), Identity()],
dims=[read_N**2, 300, write_N**2],
name="MLP",
**inits)
for brick in [mlpr, mlpw, mlp]:
brick.allocate()
brick.initialize()
#------------------------------------------------------------------------
x = tensor.matrix('features')
hr = mlpr.apply(x)
hw = mlpw.apply(x)
center_y, center_x, delta, sigma, gamma = reader.nn2att(hr)
r = reader.read(x, center_y, center_x, delta, sigma)
h = mlp.apply(r)
center_y, center_x, delta, sigma, gamma = writer.nn2att(hw)
c = writer.write(h, center_y, center_x, delta, sigma) / gamma
x_recons = T.nnet.sigmoid(c)
cost = BinaryCrossEntropy().apply(x, x_recons)
cost.name = "cost"
#------------------------------------------------------------
cg = ComputationGraph([cost])
params = VariableFilter(roles=[PARAMETER])(cg.variables)
algorithm = GradientDescent(
cost=cost,
params=params,
step_rule=CompositeRule([
RemoveNotFinite(),
Adam(learning_rate),
StepClipping(3.),
])
#step_rule=RMSProp(learning_rate),
#step_rule=Momentum(learning_rate=learning_rate, momentum=0.95)
)
#------------------------------------------------------------------------
# Setup monitors
monitors = [cost]
#for v in [center_y, center_x, log_delta, log_sigma, log_gamma]:
# v_mean = v.mean()
# v_mean.name = v.name
# monitors += [v_mean]
# monitors += [aggregation.mean(v)]
train_monitors = monitors[:]
train_monitors += [aggregation.mean(algorithm.total_gradient_norm)]
train_monitors += [aggregation.mean(algorithm.total_step_norm)]
# Live plotting...
plot_channels = [
["cost"],
]
#------------------------------------------------------------
mnist_train = BinarizedMNIST("train", sources=['features'])
mnist_test = BinarizedMNIST("test", sources=['features'])
#mnist_train = MNIST("train", binary=True, sources=['features'])
#mnist_test = MNIST("test", binary=True, sources=['features'])
main_loop = MainLoop(
model=Model(cost),
data_stream=ForceFloatX(
DataStream(mnist_train,
iteration_scheme=SequentialScheme(
mnist_train.num_examples, batch_size))),
algorithm=algorithm,
extensions=[
Timing(),
FinishAfter(after_n_epochs=epochs),
DataStreamMonitoring(
monitors,
ForceFloatX(
DataStream(mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, batch_size))),
prefix="test"),
TrainingDataMonitoring(train_monitors,
prefix="train",
after_every_epoch=True),
SerializeMainLoop(name + ".pkl"),
#Plot(name, channels=plot_channels),
ProgressBar(),
Printing()
])
main_loop.run()
def add_norm_grads_vars(self):
gradient_norm = aggregation.mean(self.algorithm.total_gradient_norm)
step_norm = aggregation.mean(self.algorithm.total_step_norm)
grad_over_step = gradient_norm / step_norm
grad_over_step.name = 'grad_over_step'
self.add_monitored_vars([gradient_norm, step_norm, grad_over_step])
def run_pretrain(model, hyper_params, cost, train_data, valid_data=None, extra_costs=None):
"""
generic training method for neural networks;
works with any network structure
:return:
"""
from fuel.streams import DataStream
from fuel.schemes import SequentialScheme, ShuffledScheme
from blocks.filter import VariableFilter
from blocks.graph import ComputationGraph
from blocks.roles import WEIGHT
from blocks.algorithms import GradientDescent, Adam, RMSProp, Scale
from blocks.extensions import FinishAfter, Timing, Printing, ProgressBar
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
from blocks.extensions.predicates import OnLogRecord
from blocks.monitoring import aggregation
from blocks.main_loop import MainLoop
from blocks.extensions.training import TrackTheBest
from deepthought.extensions.parameters import BestParams
if extra_costs is None:
extra_costs = []
cg = ComputationGraph([cost])
# TODO: more hyper-params for regularization
# L1 regularization
if hyper_params['l1wdecay'] > 0:
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
cost = cost + hyper_params['l1wdecay'] * sum([abs(W).sum() for W in weights])
cost.name = 'cost'
# set up step_rule
if hyper_params['step_rule'] == 'Adam':
step_rule = Adam(learning_rate=hyper_params['learning_rate'])
elif hyper_params['step_rule'] == 'RMSProp':
step_rule = RMSProp(learning_rate=hyper_params['learning_rate']) #, decay_rate=0.9, max_scaling=1e5)
else:
step_rule = Scale(learning_rate=hyper_params['learning_rate'])
algorithm = GradientDescent(cost=cost, parameters=cg.parameters, step_rule=step_rule)
if 'blocks_print_variable_names' in hyper_params and hyper_params['blocks_print_variable_names']:
print 'cg.variables:', cg.variables
train_monitoring_vars = [cost] + extra_costs + [aggregation.mean(algorithm.total_gradient_norm)]
for var_name in hyper_params['blocks_extensions_train_monitoring_channels']:
for v in cg.variables:
if v.name == var_name:
print 'Monitoring variable:', v
train_monitoring_vars.append(v)
# default extensions
extensions = [Timing(),
FinishAfter(after_n_epochs=hyper_params['max_epochs']),
TrainingDataMonitoring(
train_monitoring_vars,
suffix="train",
after_epoch=True)
]
# additional stuff if validation set is used
if valid_data is not None:
valid_monitoring_vars = [cost] + extra_costs
for var_name in hyper_params['blocks_extensions_valid_monitoring_channels']:
for v in cg.variables:
if v.name == var_name:
print 'Monitoring variable:', v
valid_monitoring_vars.append(v)
extensions.append(
DataStreamMonitoring(
valid_monitoring_vars,
DataStream.default_stream(
valid_data,
iteration_scheme=SequentialScheme(
valid_data.num_examples, hyper_params['batch_size'])),
suffix="valid"))
best_channel = 'cost_valid'
print '#train:', train_data.num_examples, '#valid:', valid_data.num_examples
else:
best_channel = 'cost_train'
print '#train:', train_data.num_examples
# tracking of the best
best_params = BestParams()
best_params.add_condition(['after_epoch'],
predicate=OnLogRecord(best_channel + '_best_so_far'))
extensions.append(TrackTheBest(best_channel))
extensions.append(best_params) # after TrackTheBest!
# printing and plotting
if hyper_params['blocks_extensions_printing'] is True:
extensions.append(Printing()) # optional
if hyper_params['blocks_extensions_progressbar'] is True:
extensions.append(ProgressBar())
if hyper_params['blocks_extensions_bokeh'] is True:
try:
from blocks_extras.extensions.plot import Plot
bokeh_available = True
except:
bokeh_available = False
print 'bokeh available: ', bokeh_available
if bokeh_available:
extensions.append(Plot(
hyper_params['blocks_extensions_bokeh_plot_title'],
channels=hyper_params['blocks_extensions_bokeh_channels'],
))
main_loop = MainLoop(
algorithm,
DataStream.default_stream(
train_data,
iteration_scheme=ShuffledScheme(
train_data.num_examples, hyper_params['batch_size'])),
model=model,
extensions=extensions)
main_loop.run()
return best_params.values, main_loop.status['best_' + best_channel]
def training(self,
fea2obj,
batch_size,
learning_rate=0.005,
steprule='adagrad',
wait_epochs=5,
kl_weight_init=None,
klw_ep=50,
klw_inc_rate=0,
num_epochs=None):
networkfile = self._config['net']
n_epochs = num_epochs or int(self._config['nepochs'])
reg_weight = float(self._config['loss_weight'])
reg_type = self._config['loss_reg']
numtrain = int(
self._config['num_train']) if 'num_train' in self._config else None
train_stream, num_samples_train = get_comb_stream(
fea2obj, 'train', batch_size, shuffle=True, num_examples=numtrain)
dev_stream, num_samples_dev = get_comb_stream(fea2obj,
'dev',
batch_size=None,
shuffle=False)
logger.info('sources: %s -- number of train/dev samples: %d/%d',
train_stream.sources, num_samples_train, num_samples_dev)
t2idx = fea2obj['targets'].t2idx
klw_init = kl_weight_init or float(
self._config['kld_weight']) if 'kld_weight' in self._config else 1
logger.info('kl_weight_init: %d', klw_init)
kl_weight = shared_floatx(klw_init, 'kl_weight')
entropy_weight = shared_floatx(1., 'entropy_weight')
cost, p_at_1, _, KLD, logpy_xz, pat1_recog, misclassify_rate = build_model_new(
fea2obj, len(t2idx), self._config, kl_weight, entropy_weight)
cg = ComputationGraph(cost)
weights = VariableFilter(roles=[WEIGHT])(cg.parameters)
logger.info('Model weights are: %s', weights)
if 'L2' in reg_type:
cost += reg_weight * l2_norm(weights)
logger.info('applying %s with weight: %f ', reg_type, reg_weight)
dropout = -0.1
if dropout > 0:
cg = apply_dropout(cg, weights, dropout)
cost = cg.outputs[0]
cost.name = 'cost'
logger.info('Our Algorithm is : %s, and learning_rate: %f', steprule,
learning_rate)
if 'adagrad' in steprule:
cnf_step_rule = AdaGrad(learning_rate)
elif 'adadelta' in steprule:
cnf_step_rule = AdaDelta(decay_rate=0.95)
elif 'decay' in steprule:
cnf_step_rule = RMSProp(learning_rate=learning_rate,
decay_rate=0.90)
cnf_step_rule = CompositeRule([cnf_step_rule, StepClipping(1)])
elif 'momentum' in steprule:
cnf_step_rule = Momentum(learning_rate=learning_rate, momentum=0.9)
elif 'adam' in steprule:
cnf_step_rule = Adam(learning_rate=learning_rate)
else:
logger.info('The steprule param is wrong! which is: %s', steprule)
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=cnf_step_rule,
on_unused_sources='warn')
#algorithm.add_updates(updates)
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
monitored_vars = [
cost, gradient_norm, step_norm, p_at_1, KLD, logpy_xz, kl_weight,
pat1_recog
]
train_monitor = TrainingDataMonitoring(variables=monitored_vars,
after_batch=True,
before_first_epoch=True,
prefix='tra')
dev_monitor = DataStreamMonitoring(variables=[
cost, p_at_1, KLD, logpy_xz, pat1_recog, misclassify_rate
],
after_epoch=True,
before_first_epoch=True,
data_stream=dev_stream,
prefix="dev")
extensions = [
dev_monitor,
train_monitor,
Timing(),
TrackTheBest('dev_cost'),
FinishIfNoImprovementAfter('dev_cost_best_so_far',
epochs=wait_epochs),
Printing(after_batch=False), #, ProgressBar()
FinishAfter(after_n_epochs=n_epochs),
saveload.Load(networkfile + '.toload.pkl'),
] + track_best('dev_cost', networkfile + '.best.pkl')
#extensions.append(SharedVariableModifier(kl_weight,
# lambda n, klw: numpy.cast[theano.config.floatX] (klw_inc_rate + klw), after_epoch=False, every_n_epochs=klw_ep, after_batch=False))
# extensions.append(SharedVariableModifier(entropy_weight,
# lambda n, crw: numpy.cast[theano.config.floatX](crw - klw_inc_rate), after_epoch=False, every_n_epochs=klw_ep, after_batch=False))
logger.info('number of parameters in the model: %d',
tensor.sum([p.size for p in cg.parameters]).eval())
logger.info('Lookup table sizes: %s',
[p.size.eval() for p in cg.parameters if 'lt' in p.name])
main_loop = MainLoop(data_stream=train_stream,
algorithm=algorithm,
model=Model(cost),
extensions=extensions)
main_loop.run()
def train(model, get_streams, save_path, num_epochs,
batch_size, lrs, until_which_epoch, grad_clipping):
monitorings = model.monitorings
# Training
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(1e-4)
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.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),
SaveLog(save_path, after_epoch=True),
ProgressBar(),
LRDecay(lr_var, lrs, until_which_epoch,
after_epoch=True),
Printing(after_epoch=True)])
main_loop.run()
def main(mode, save_path, steps, num_batches):
num_states = MarkovChainDataset.num_states
if mode == "train":
# Experiment configuration
rng = numpy.random.RandomState(1)
batch_size = 50
seq_len = 100
dim = 10
feedback_dim = 8
# Build the bricks and initialize them
transition = GatedRecurrent(name="transition",
dim=dim,
activation=Tanh())
generator = SequenceGenerator(Readout(
readout_dim=num_states,
source_names=["states"],
emitter=SoftmaxEmitter(name="emitter"),
feedback_brick=LookupFeedback(num_states,
feedback_dim,
name='feedback'),
name="readout"),
transition,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name="generator")
generator.push_initialization_config()
transition.weights_init = Orthogonal()
generator.initialize()
# Give an idea of what's going on.
logger.info("Parameters:\n" + pprint.pformat(
[(key, value.get_value().shape)
for key, value in Selector(generator).get_params().items()],
width=120))
logger.info("Markov chain entropy: {}".format(
MarkovChainDataset.entropy))
logger.info("Expected min error: {}".format(
-MarkovChainDataset.entropy * seq_len))
# Build the cost computation graph.
x = tensor.lmatrix('data')
cost = aggregation.mean(
generator.cost_matrix(x[:, :]).sum(), x.shape[1])
cost.name = "sequence_log_likelihood"
algorithm = GradientDescent(
cost=cost,
params=list(Selector(generator).get_params().values()),
step_rule=Scale(0.001))
main_loop = MainLoop(algorithm=algorithm,
data_stream=DataStream(
MarkovChainDataset(rng, seq_len),
iteration_scheme=ConstantScheme(batch_size)),
model=Model(cost),
extensions=[
FinishAfter(after_n_batches=num_batches),
TrainingDataMonitoring([cost],
prefix="this_step",
after_batch=True),
TrainingDataMonitoring([cost],
prefix="average",
every_n_batches=100),
Checkpoint(save_path, every_n_batches=500),
Printing(every_n_batches=100)
])
main_loop.run()
elif mode == "sample":
main_loop = cPickle.load(open(save_path, "rb"))
generator = main_loop.model
sample = ComputationGraph(
generator.generate(n_steps=steps, batch_size=1,
iterate=True)).get_theano_function()
states, outputs, costs = [data[:, 0] for data in sample()]
numpy.set_printoptions(precision=3, suppress=True)
print("Generation cost:\n{}".format(costs.sum()))
freqs = numpy.bincount(outputs).astype(floatX)
freqs /= freqs.sum()
print("Frequencies:\n {} vs {}".format(freqs,
MarkovChainDataset.equilibrium))
trans_freqs = numpy.zeros((num_states, num_states), dtype=floatX)
for a, b in zip(outputs, outputs[1:]):
trans_freqs[a, b] += 1
trans_freqs /= trans_freqs.sum(axis=1)[:, None]
print("Transition frequencies:\n{}\nvs\n{}".format(
trans_freqs, MarkovChainDataset.trans_prob))
else:
assert False
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):
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(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()
# FIXME hard-coded for 3-layered LSTM
modifier_functions = {
network.transitions[0].name : modifier_function_0,
network.transitions[1].name : modifier_function_1,
network.transitions[2].name : modifier_function_2
}
#init_state_modifier = SharedVariableModifier(network.transitions[-1].initial_state_, function=modifier_function, after_batch=True)
init_state_modifiers = [SharedVariableModifier(trans.initial_state_, function=modifier_functions[trans.name], after_batch=True) for trans in network.transitions]
#state_function = function([state_to_compare], initial_states[2], updates=[(init_state_2, state_to_compare[0][-1])]) #TODO look at this, this is how it basically works!
monitor_grad = TrainingDataMonitoring(variables=[cross_ent, aggregation.mean(algorithm.total_gradient_norm),
aggregation.mean(algorithm.total_step_norm)], #+initial_states+[state_to_compare_1],
prefix="training", after_batch=True)
early_stopping = EarlyStopping(variables=[cross_ent], data_stream=data_stream_valid,
path="seqgen_" + args.type + "_" + "_".join([str(d) for d in network.hidden_dims]) + ".pkl",
tolerance=4, prefix="validation")
prkwargs = {
#'after_batch':True # use this for prints after every batch
}
main_loop = MainLoop(algorithm=algorithm, data_stream=data_stream, model=cost_model,
extensions=[monitor_grad, early_stopping, FinishAfter(after_n_epochs=args.epochs), ProgressBar(),
Timing(), Printing(**prkwargs)]+init_state_modifiers)
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 run():
# Load Model
net_size = 256 #Hard-code instead of loading model (takes too long to set up network)
#net = vaegan.VAEGAN()
#network_saver = saver.NetworkSaver('vaegan/models/', net=net)
#network_saver.load()
# DATA
train_stream = get_stream(hdf5_file, 'train', batch_size) #TODO jonathan ?
test_stream = get_stream(hdf5_file, 'test', batch_size) #TODO jonathan ?
# MODEL
x = T.TensorType('floatX', [False] * 3)('features')
y = T.tensor3('targets', dtype='floatX')
train_flag = [theano.shared(0)]
x = x.swapaxes(0, 1)
y = y.swapaxes(0, 1)
# More Config
out_size = len(output_columns) - 1 # code_mode=RL-MDN
latent_size = net_size
in_size = latent_size + len(input_columns)
# NN fprop
y_hat, cost, cells = nn_fprop(x, y, in_size, out_size, hidden_size,
num_recurrent_layers, train_flag)
# COST
cg = ComputationGraph(cost)
extra_updates = []
# RMS Prop training optimizer
step_rules = [
RMSProp(learning_rate=learning_rate, decay_rate=decay_rate),
StepClipping(step_clipping)
]
parameters_to_update = cg.parameters
algorithm = GradientDescent(cost=cg.outputs[0],
parameters=parameters_to_update,
step_rule=CompositeRule(step_rules))
algorithm.add_updates(
extra_updates) # TODO jonathan what is this, is this needed?
# Extensions
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
monitored_vars = [
cost, step_rules[0].learning_rate, gradient_norm, step_norm
]
test_monitor = DataStreamMonitoring(variables=[cost],
after_epoch=True,
before_first_epoch=True,
data_stream=test_stream,
prefix="test")
train_monitor = TrainingDataMonitoring(variables=monitored_vars,
after_epoch=True,
before_first_epoch=True,
prefix='train')
set_train_flag = SetTrainFlag(after_epoch=True,
before_epoch=True,
flag=train_flag)
# plot = Plot('Plotting example', channels=[['cost']], after_batch=True, open_browser=True)
extensions = [
set_train_flag,
test_monitor,
train_monitor,
Timing(),
Printing(after_epoch=True),
FinishAfter(after_n_epochs=nepochs),
saveload.Load(load_path),
saveload.Checkpoint(last_path, every_n_epochs=10000),
] + track_best('test_cost',
save_path) #+ track_best('train_cost', last_path)
if learning_rate_decay not in (0, 1):
extensions.append(
SharedVariableModifier(step_rules[0].learning_rate,
lambda n, lr: np.cast[theano.config.floatX]
(learning_rate_decay * lr),
after_epoch=False,
every_n_epochs=lr_decay_every_n_epochs,
after_batch=False))
print 'number of parameters in the model: ' + str(
T.sum([p.size for p in cg.parameters]).eval())
# Finally build the main loop and train the model
mainLoop = MainLoop(data_stream=train_stream,
algorithm=algorithm,
model=Model(cost),
extensions=extensions)
mainLoop.run()
def main(name, dataset, epochs, batch_size, learning_rate, attention, n_iter,
enc_dim, dec_dim, z_dim, oldmodel, live_plotting):
image_size, channels, data_train, data_valid, data_test = datasets.get_data(
dataset)
train_stream = Flatten(
DataStream.default_stream(data_train,
iteration_scheme=SequentialScheme(
data_train.num_examples, batch_size)))
valid_stream = Flatten(
DataStream.default_stream(data_valid,
iteration_scheme=SequentialScheme(
data_valid.num_examples, batch_size)))
test_stream = Flatten(
DataStream.default_stream(data_test,
iteration_scheme=SequentialScheme(
data_test.num_examples, batch_size)))
if name is None:
name = dataset
img_height, img_width = image_size
x_dim = channels * img_height * img_width
rnninits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
# Configure attention mechanism
if attention != "":
read_N, write_N = attention.split(',')
read_N = int(read_N)
write_N = int(write_N)
read_dim = 2 * channels * read_N**2
reader = AttentionReader(x_dim=x_dim,
dec_dim=dec_dim,
channels=channels,
width=img_width,
height=img_height,
N=read_N,
**inits)
writer = AttentionWriter(input_dim=dec_dim,
output_dim=x_dim,
channels=channels,
width=img_width,
height=img_height,
N=write_N,
**inits)
attention_tag = "r%d-w%d" % (read_N, write_N)
else:
read_dim = 2 * x_dim
reader = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer = Writer(input_dim=dec_dim, output_dim=x_dim, **inits)
attention_tag = "full"
#----------------------------------------------------------------------
if name is None:
name = dataset
# Learning rate
def lr_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
exp = np.floor(np.log10(value))
leading = ("%e" % value)[0]
return "%s%d" % (leading, -exp)
lr_str = lr_tag(learning_rate)
subdir = name + "-" + time.strftime("%Y%m%d-%H%M%S")
longname = "%s-%s-t%d-enc%d-dec%d-z%d-lr%s" % (
dataset, attention_tag, n_iter, enc_dim, dec_dim, z_dim, lr_str)
pickle_file = subdir + "/" + longname + ".pkl"
print("\nRunning experiment %s" % longname)
print(" dataset: %s" % dataset)
print(" subdirectory: %s" % subdir)
print(" learning rate: %g" % learning_rate)
print(" attention: %s" % attention)
print(" n_iterations: %d" % n_iter)
print(" encoder dimension: %d" % enc_dim)
print(" z dimension: %d" % z_dim)
print(" decoder dimension: %d" % dec_dim)
print(" batch size: %d" % batch_size)
print(" epochs: %d" % epochs)
print()
#----------------------------------------------------------------------
encoder_rnn = LSTM(dim=enc_dim, name="RNN_enc", **rnninits)
decoder_rnn = LSTM(dim=dec_dim, name="RNN_dec", **rnninits)
encoder_mlp = MLP([Identity()], [(read_dim + dec_dim), 4 * enc_dim],
name="MLP_enc",
**inits)
decoder_mlp = MLP([Identity()], [z_dim, 4 * dec_dim],
name="MLP_dec",
**inits)
q_sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, **inits)
draw = DrawModel(n_iter,
reader=reader,
encoder_mlp=encoder_mlp,
encoder_rnn=encoder_rnn,
sampler=q_sampler,
decoder_mlp=decoder_mlp,
decoder_rnn=decoder_rnn,
writer=writer)
draw.initialize()
#------------------------------------------------------------------------
x = tensor.matrix('features')
x_recons, kl_terms = draw.reconstruct(x)
recons_term = BinaryCrossEntropy().apply(x, x_recons)
recons_term.name = "recons_term"
cost = recons_term + kl_terms.sum(axis=0).mean()
cost.name = "nll_bound"
#------------------------------------------------------------
cg = ComputationGraph([cost])
params = VariableFilter(roles=[PARAMETER])(cg.variables)
algorithm = GradientDescent(
cost=cost,
parameters=params,
step_rule=CompositeRule([
StepClipping(10.),
Adam(learning_rate),
])
#step_rule=RMSProp(learning_rate),
#step_rule=Momentum(learning_rate=learning_rate, momentum=0.95)
)
#------------------------------------------------------------------------
# Setup monitors
monitors = [cost]
for t in range(n_iter):
kl_term_t = kl_terms[t, :].mean()
kl_term_t.name = "kl_term_%d" % t
#x_recons_t = T.nnet.sigmoid(c[t,:,:])
#recons_term_t = BinaryCrossEntropy().apply(x, x_recons_t)
#recons_term_t = recons_term_t.mean()
#recons_term_t.name = "recons_term_%d" % t
monitors += [kl_term_t]
train_monitors = monitors[:]
train_monitors += [aggregation.mean(algorithm.total_gradient_norm)]
train_monitors += [aggregation.mean(algorithm.total_step_norm)]
# Live plotting...
plot_channels = [
["train_nll_bound", "test_nll_bound"],
["train_kl_term_%d" % t for t in range(n_iter)],
#["train_recons_term_%d" % t for t in range(n_iter)],
["train_total_gradient_norm", "train_total_step_norm"]
]
#------------------------------------------------------------
if not os.path.exists(subdir):
os.makedirs(subdir)
plotting_extensions = []
if live_plotting:
plotting_extensions = [Plot(name, channels=plot_channels)]
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
Timing(),
FinishAfter(after_n_epochs=epochs),
TrainingDataMonitoring(
train_monitors, prefix="train", after_epoch=True),
# DataStreamMonitoring(
# monitors,
# valid_stream,
## updates=scan_updates,
# prefix="valid"),
DataStreamMonitoring(
monitors,
test_stream,
# updates=scan_updates,
prefix="test"),
#Checkpoint(name, before_training=False, after_epoch=True, save_separately=['log', 'model']),
Checkpoint("{}/{}".format(subdir, name),
save_main_loop=False,
before_training=True,
after_epoch=True,
save_separately=['log', 'model']),
SampleCheckpoint(image_size=image_size[0],
channels=channels,
save_subdir=subdir,
before_training=True,
after_epoch=True),
ProgressBar(),
Printing()
] + plotting_extensions)
if oldmodel is not None:
print("Initializing parameters with old model %s" % oldmodel)
with open(oldmodel, "rb") as f:
oldmodel = pickle.load(f)
main_loop.model.set_parameter_values(oldmodel.get_param_values())
del oldmodel
main_loop.run()
def test_training_data_monitoring():
weights = numpy.array([-1, 1], dtype=theano.config.floatX)
features = [
numpy.array(f, dtype=theano.config.floatX)
for f in [[1, 2], [3, 5], [5, 8]]
]
targets = numpy.array([(weights * f).sum() for f in features])
n_batches = 3
dataset = IterableDataset(dict(features=features, targets=targets))
x = tensor.vector('features')
y = tensor.scalar('targets')
W = shared_floatx([0, 0], name='W')
V = shared_floatx(7, name='V')
W_sum = W.sum().copy(name='W_sum')
cost = ((x * W).sum() - y)**2
cost.name = 'cost'
class TrueCostExtension(TrainingExtension):
def before_batch(self, data):
self.main_loop.log.current_row['true_cost'] = ((
(W.get_value() * data["features"]).sum() - data["targets"])**2)
# Note, that unlike a Theano variable, a monitored
# quantity can't be reused in more than one TrainingDataMonitoring
ftt1 = MeanFeaturesTimesTarget(requires=[x, y], name='ftt1')
ftt2 = MeanFeaturesTimesTarget(requires=[x, y], name='ftt2')
main_loop = MainLoop(model=None,
data_stream=dataset.get_example_stream(),
algorithm=GradientDescent(cost=cost,
parameters=[W],
step_rule=Scale(0.001)),
extensions=[
FinishAfter(after_n_epochs=1),
TrainingDataMonitoring([W_sum, cost, V, ftt1],
prefix="train1",
after_batch=True),
TrainingDataMonitoring(
[aggregation.mean(W_sum), cost, ftt2],
prefix="train2",
after_epoch=True),
TrueCostExtension()
])
main_loop.run()
# Check monitoring of a shared varible
assert_allclose(main_loop.log.current_row['train1_V'], 7.0)
for i in range(n_batches):
# The ground truth is written to the log before the batch is
# processed, where as the extension writes after the batch is
# processed. This is why the iteration numbers differs here.
assert_allclose(main_loop.log[i]['true_cost'],
main_loop.log[i + 1]['train1_cost'])
assert_allclose(
main_loop.log[n_batches]['train2_cost'],
sum([main_loop.log[i]['true_cost']
for i in range(n_batches)]) / n_batches)
assert_allclose(
main_loop.log[n_batches]['train2_W_sum'],
sum([
main_loop.log[i]['train1_W_sum'] for i in range(1, n_batches + 1)
]) / n_batches)
# Check monitoring of non-Theano quantites
for i in range(n_batches):
assert_allclose(main_loop.log[i + 1]['train1_ftt1'],
features[i] * targets[i])
assert_allclose(main_loop.log[n_batches]['train2_ftt2'],
(features * targets[:, None]).mean(axis=0))
def main(dataset):
#----------------------------------------------------------------------------
epochs = 50
batch_size = 200
learning_rate = 3e-4
attention = '16,16'
n_iter = 8
enc_dim = 1024
dec_dim = 1024
z_dim = 100
oldmodel = None
#dataset = 'sketch'
data_dir = '/home/ubuntu/svrt_data/'+dataset
name = dataset
#----------------------------------------------------------------------------
#----------------------------------------------------------------------------
# image_size, channels, data_train, data_valid, data_test = datasets.get_data(dataset)
# train_ind = np.arange(data_train.num_examples)
# test_ind = np.arange(data_test.num_examples)
# rng = np.random.RandomState(seed=1)
# rng.shuffle(train_ind)
# rng.shuffle(test_ind)
# train_stream = Flatten(DataStream.default_stream(
# data_train, iteration_scheme=ShuffledScheme(train_ind, batch_size)))
# test_stream = Flatten(DataStream.default_stream(
# data_test, iteration_scheme=ShuffledScheme(test_ind, batch_size)))
#Get shuffled data
test_X, train_X, test_y, train_y = package_sketch_images.import_sketch(data_dir)
data_test = package_sketch_images.assign_datastream(test_X,test_y)
data_train = package_sketch_images.assign_datastream(train_X,train_y)
image_size = (int(np.sqrt(test_X.shape[1])),int(np.sqrt(test_X.shape[1])))
channels = 1
target_categories = np.unique(train_y).shape[0]
train_ind = np.arange(data_train.num_examples)
test_ind = np.arange(data_test.num_examples)
rng = np.random.RandomState(seed=1)
rng.shuffle(train_ind)
rng.shuffle(test_ind)
#####
#Comparisons to humans:
#Is there a neural signature for changes in read delta parameter (glimpse size)?
#Do machines/humans make similar mistakes?
#Learning time::: compare this somehow...
#####
#Convert datasets into fuel
#valid_stream = Flatten(DataStream.default_stream(data_valid, iteration_scheme=SequentialScheme(data_valid.num_examples, batch_size)))
test_stream = Flatten(DataStream.default_stream(data_test, iteration_scheme=ShuffledScheme(test_ind, batch_size)))
train_stream = Flatten(DataStream.default_stream(data_train, iteration_scheme=ShuffledScheme(train_ind, batch_size)))
if name is None:
name = dataset
img_height, img_width = image_size
x_dim = channels * img_height * img_width
rnninits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
# Configure attention mechanism
if attention != "":
read_N, write_N = attention.split(',')
read_N = int(read_N)
write_N = int(write_N)
read_dim = 2 * channels * read_N ** 2
reader = AttentionReader(x_dim=x_dim, dec_dim=dec_dim,
channels=channels, width=img_width, height=img_height,
N=read_N, **inits)
writer = AttentionWriter(input_dim=dec_dim, output_dim=x_dim,
channels=channels, width=img_width, height=img_height,
N=write_N, **inits)
attention_tag = "r%d-w%d" % (read_N, write_N)
else:
read_dim = 2*x_dim
reader = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer = Writer(input_dim=dec_dim, output_dim=x_dim, **inits)
attention_tag = "full"
#----------------------------------------------------------------------
if name is None:
name = dataset
# Learning rate
def lr_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
exp = np.floor(np.log10(value))
leading = ("%e"%value)[0]
return "%s%d" % (leading, -exp)
lr_str = lr_tag(learning_rate)
subdir = name
longname = "%s-%s-t%d-enc%d-dec%d-z%d-lr%s" % (dataset, attention_tag, n_iter, enc_dim, dec_dim, z_dim, lr_str)
pickle_file = subdir + "/" + longname + ".pkl"
print("\nRunning experiment %s" % longname)
print(" dataset: %s" % dataset)
print(" subdirectory: %s" % subdir)
print(" learning rate: %g" % learning_rate)
print(" attention: %s" % attention)
print(" n_iterations: %d" % n_iter)
print(" encoder dimension: %d" % enc_dim)
print(" z dimension: %d" % z_dim)
print(" decoder dimension: %d" % dec_dim)
print(" batch size: %d" % batch_size)
print(" epochs: %d" % epochs)
print()
#----------------------------------------------------------------------
encoder_rnn = LSTM(dim=enc_dim, name="RNN_enc", **rnninits)
#/////
#Insert a conv/deconv before the encoder MLP? -- add normalization at some point
conv_layer = Convolutional(
filter_size=(3, 3),
num_filters=30,
border_mode='half',
step=(1,1))
act = Rectifier()
pool_layer = MaxPooling(
pooling_size=(2, 2),
step=(1,1),
padding=(1,1))
encoder_cnn = ConvolutionalSequence(
[
conv_layer,
act,
pool_layer,
],
num_channels=1,
image_size=(read_N, read_N),
**inits)
dummy_cnn = encoder_cnn
dummy_cnn.initialize()
cnn_output_dim = np.prod(dummy_cnn.get_dim('output')) #Take product now so that you can flatten later
cnn_mlp = MLP([Identity()], [cnn_output_dim, read_N ** 2],name="CNN_encoder", **inits) #convert CNN feature maps to encoder_mlp dimensions
flattener = Flattener()
#/////
encoder_mlp = MLP([Identity()], [(read_dim+enc_dim), 4*enc_dim], name="LSTM_encoder", **inits) #260 read_dim+dec_dim
classifier_mlp = MLP([Identity(),Softmax()], [4*dec_dim, z_dim, target_categories], name="classifier", **inits)
q_sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, **inits)
draw = DrawClassifierModel(
n_iter,
reader=reader,
writer=writer,
encoder_cnn=encoder_cnn,
cnn_mlp=cnn_mlp,
encoder_mlp=encoder_mlp,
encoder_rnn=encoder_rnn,
sampler = q_sampler,
classifier=classifier_mlp,
flattener=flattener)
draw.initialize()
#------------------------------------------------------------------------
x = tensor.matrix('features')
y = tensor.imatrix('targets')
probs, h_enc, c_enc, center_y, center_x, delta = draw.reconstruct(x)
trim_probs = probs #Only take information from the last iteration
labels = y #tensor.lt(y, .5)
#Apply a max to probs (get position of max index)
#Do the same for labels/dont use one hot
cost = (CategoricalCrossEntropy().apply(labels, trim_probs).copy(name='cost'))
error_rate = tensor.neq(y.argmax(axis=1), trim_probs.argmax(axis=1)).mean(dtype=theano.config.floatX)
cost.name = "BCE"
error_rate.name = "error_rate"
guesses = labels.argmax(axis=1) #tensor.lt(y, .5)#T.sum(y)#.argmax(axis=0)
ps = trim_probs
guesses.name = "guesses"
ps.name = "probs_shape"
#------------------------------------------------------------
cg = ComputationGraph([cost])
params = VariableFilter(roles=[PARAMETER])(cg.variables)
algorithm = GradientDescent(
cost=cost,
parameters=params,
step_rule=CompositeRule([
StepClipping(10.),
Adam(learning_rate),
])
)
#------------------------------------------------------------------------
# Setup monitors
#monitors = [cost,error_rate,guesses,ps]
monitors = [cost,error_rate]
#monitors = [cost]
train_monitors = monitors[:]
train_monitors += [aggregation.mean(algorithm.total_gradient_norm)]
train_monitors += [aggregation.mean(algorithm.total_step_norm)]
# Live plotting...
#------------------------------------------------------------
if not os.path.exists(subdir):
os.makedirs(subdir)
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
Timing(),
FinishAfter(after_n_epochs=epochs),
TrainingDataMonitoring(
train_monitors,
prefix="train",
after_epoch=True),
# DataStreamMonitoring(
# monitors,
# valid_stream,
## updates=scan_updates,
# prefix="valid"),
DataStreamMonitoring(
monitors,
test_stream,
# updates=scan_updates,
prefix="test"),
#Checkpoint(name, before_training=True, after_epoch=True, save_separately=['log', 'model']),
PartsOnlyCheckpoint("{}/{}".format(subdir,name), before_training=True, after_epoch=True, save_separately=['log', 'model']),
ProgressBar(),
Printing()])
if oldmodel is not None:
print("Initializing parameters with old model %s"%oldmodel)
with open(oldmodel, "rb") as f:
oldmodel = pickle.load(f)
main_loop.model.set_parameter_values(oldmodel.get_parameter_values())
del oldmodel
main_loop.run()
def create_main_loop(save_to,
num_epochs,
unit_order=None,
batch_size=500,
num_batches=None):
image_size = (28, 28)
output_size = 10
convnet = create_lenet_5()
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
probs = convnet.apply(x)
case_costs = CasewiseCrossEntropy().apply(y.flatten(), probs)
cost = case_costs.mean().copy(name='cost')
error_rate = (MisclassificationRate().apply(y.flatten(),
probs).copy(name='error_rate'))
cg = ComputationGraph([cost, error_rate, case_costs])
# Apply regularization to the cost
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
cost = cost + sum([0.0003 * (W**2).sum() for W in weights])
cost.name = 'cost_with_regularization'
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))
# Generate pics for biases
biases = VariableFilter(roles=[BIAS])(cg.parameters)
# Train with simple SGD
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=AdaDelta())
synpic_extension = SynpicExtension(synpic_parameters=biases,
case_costs=case_costs,
case_labels=y,
pics=x,
batch_size=batch_size,
pic_size=image_size,
label_count=output_size,
after_batch=True)
# Impose an orderint for the SaveImages extension
if unit_order is not None:
with open(unit_order, 'rb') as handle:
histograms = pickle.load(handle)
unit_order = compute_unit_order(histograms)
# `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),
synpic_extension,
SaveImages(picsources=[synpic_extension],
title="LeNet-5: batch {i}, " +
"cost {cost_with_regularization:.2f}, " +
"trainerr {error_rate:.3f}",
data=[cost, error_rate],
graph='error_rate',
graph_len=500,
unit_order=unit_order,
after_batch=True),
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.synpic = synpic_extension
return main_loop
def main(feature_maps=None, mlp_hiddens=None,
conv_sizes=None, pool_sizes=None, batch_size=None,
num_batches=None):
if feature_maps is None:
feature_maps = [32, 48, 64, 96, 96, 128]
if mlp_hiddens is None:
mlp_hiddens = [1000]
if conv_sizes is None:
conv_sizes = [9, 7, 5, 3, 2, 1]
if pool_sizes is None:
pool_sizes = [2, 2, 2, 2, 1, 1]
if batch_size is None:
batch_size = 64
conv_steps=[2, 1, 1, 1, 1, 1] #same as stride
image_size = (128, 128)
output_size = 2
learningRate = 0.001
drop_prob = 0.4
weight_noise = 0.75
num_epochs = 150
num_batches = None
host_plot='http://*****:*****@ %s' % (graph_name, datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']], after_epoch=True, server_url=host_plot))
PLOT_AVAILABLE = True
except ImportError:
PLOT_AVAILABLE = False
extensions.append(Checkpoint(save_to, after_epoch=True, after_training=True, save_separately=['log']))
logger.info("Building the model")
model = Model(cost)
########### Loading images #####################
main_loop = MainLoop(
algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
def main(job_id, params):
config = ConfigParser.ConfigParser()
config.readfp(open('./params'))
max_epoch = int(config.get('hyperparams', 'max_iter', 100))
base_lr = float(config.get('hyperparams', 'base_lr', 0.01))
train_batch = int(config.get('hyperparams', 'train_batch', 256))
valid_batch = int(config.get('hyperparams', 'valid_batch', 512))
test_batch = int(config.get('hyperparams', 'valid_batch', 512))
W_sd = float(config.get('hyperparams', 'W_sd', 0.01))
W_mu = float(config.get('hyperparams', 'W_mu', 0.0))
b_sd = float(config.get('hyperparams', 'b_sd', 0.01))
b_mu = float(config.get('hyperparams', 'b_mu', 0.0))
hidden_units = int(config.get('hyperparams', 'hidden_units', 32))
input_dropout_ratio = float(
config.get('hyperparams', 'input_dropout_ratio', 0.2))
dropout_ratio = float(config.get('hyperparams', 'dropout_ratio', 0.2))
weight_decay = float(config.get('hyperparams', 'weight_decay', 0.001))
max_norm = float(config.get('hyperparams', 'max_norm', 100.0))
solver = config.get('hyperparams', 'solver_type', 'rmsprop')
data_file = config.get('hyperparams', 'data_file')
side = config.get('hyperparams', 'side', 'b')
# Spearmint optimization parameters:
if params:
base_lr = float(params['base_lr'][0])
dropout_ratio = float(params['dropout_ratio'][0])
hidden_units = params['hidden_units'][0]
weight_decay = params['weight_decay'][0]
if 'adagrad' in solver:
solver_type = CompositeRule([
AdaGrad(learning_rate=base_lr),
VariableClipping(threshold=max_norm)
])
else:
solver_type = CompositeRule([
RMSProp(learning_rate=base_lr),
VariableClipping(threshold=max_norm)
])
input_dim = {'l': 11427, 'r': 10519, 'b': 10519 + 11427}
data_file = config.get('hyperparams', 'data_file')
if 'b' in side:
train = H5PYDataset(data_file, which_set='train')
valid = H5PYDataset(data_file, which_set='valid')
test = H5PYDataset(data_file, which_set='test')
x_l = tensor.matrix('l_features')
x_r = tensor.matrix('r_features')
x = tensor.concatenate([x_l, x_r], axis=1)
else:
train = H5PYDataset(data_file,
which_set='train',
sources=['{}_features'.format(side), 'targets'])
valid = H5PYDataset(data_file,
which_set='valid',
sources=['{}_features'.format(side), 'targets'])
test = H5PYDataset(data_file,
which_set='test',
sources=['{}_features'.format(side), 'targets'])
x = tensor.matrix('{}_features'.format(side))
y = tensor.lmatrix('targets')
# Define a feed-forward net with an input, two hidden layers, and a softmax output:
model = MLP(activations=[
Rectifier(name='h1'),
Rectifier(name='h2'),
Softmax(name='output'),
],
dims=[input_dim[side], hidden_units, hidden_units, 2],
weights_init=IsotropicGaussian(std=W_sd, mean=W_mu),
biases_init=IsotropicGaussian(b_sd, b_mu))
# Don't forget to initialize params:
model.initialize()
# y_hat is the output of the neural net with x as its inputs
y_hat = model.apply(x)
# Define a cost function to optimize, and a classification error rate.
# Also apply the outputs from the net and corresponding targets:
cost = CategoricalCrossEntropy().apply(y.flatten(), y_hat)
error = MisclassificationRate().apply(y.flatten(), y_hat)
error.name = 'error'
# This is the model: before applying dropout
model = Model(cost)
# Need to define the computation graph for the cost func:
cost_graph = ComputationGraph([cost])
# This returns a list of weight vectors for each layer
W = VariableFilter(roles=[WEIGHT])(cost_graph.variables)
# Add some regularization to this model:
cost += weight_decay * l2_norm(W)
cost.name = 'entropy'
# computational graph with l2 reg
cost_graph = ComputationGraph([cost])
# Apply dropout to inputs:
inputs = VariableFilter([INPUT])(cost_graph.variables)
dropout_inputs = [
input for input in inputs if input.name.startswith('linear_')
]
dropout_graph = apply_dropout(cost_graph, [dropout_inputs[0]],
input_dropout_ratio)
dropout_graph = apply_dropout(dropout_graph, dropout_inputs[1:],
dropout_ratio)
dropout_cost = dropout_graph.outputs[0]
dropout_cost.name = 'dropout_entropy'
# Learning Algorithm (notice: we use the dropout cost for learning):
algo = GradientDescent(step_rule=solver_type,
params=dropout_graph.parameters,
cost=dropout_cost)
# algo.step_rule.learning_rate.name = 'learning_rate'
# Data stream used for training model:
training_stream = Flatten(
DataStream.default_stream(dataset=train,
iteration_scheme=ShuffledScheme(
train.num_examples,
batch_size=train_batch)))
training_monitor = TrainingDataMonitoring([
dropout_cost,
aggregation.mean(error),
aggregation.mean(algo.total_gradient_norm)
],
after_batch=True)
# Use the 'valid' set for validation during training:
validation_stream = Flatten(
DataStream.default_stream(dataset=valid,
iteration_scheme=ShuffledScheme(
valid.num_examples,
batch_size=valid_batch)))
validation_monitor = DataStreamMonitoring(variables=[cost, error],
data_stream=validation_stream,
prefix='validation',
after_epoch=True)
test_stream = Flatten(
DataStream.default_stream(
dataset=test,
iteration_scheme=ShuffledScheme(test.num_examples,
batch_size=test_batch)))
test_monitor = DataStreamMonitoring(variables=[error],
data_stream=test_stream,
prefix='test',
after_training=True)
plotting = Plot('AdniNet_{}'.format(side),
channels=[
['dropout_entropy', 'validation_entropy'],
['error', 'validation_error'],
],
after_batch=False)
# Checkpoint class used to save model and log:
stamp = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d-%H:%M')
checkpoint = Checkpoint('./models/{}net/{}'.format(side, stamp),
save_separately=['model', 'log'],
every_n_epochs=1)
# Home-brewed class for early stopping when we detect we have started to overfit
early_stopper = FinishIfOverfitting(error_name='error',
validation_name='validation_error',
threshold=0.1,
epochs=5,
burn_in=100)
# The main loop will train the network and output reports, etc
main_loop = MainLoop(data_stream=training_stream,
model=model,
algorithm=algo,
extensions=[
validation_monitor,
training_monitor,
plotting,
FinishAfter(after_n_epochs=max_epoch),
early_stopper,
Printing(),
ProgressBar(),
checkpoint,
test_monitor,
])
main_loop.run()
ve = float(main_loop.log.last_epoch_row['validation_error'])
te = float(main_loop.log.last_epoch_row['error'])
spearmint_loss = ve + abs(te - ve)
print 'Spearmint Loss: {}'.format(spearmint_loss)
return spearmint_loss
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,
on_unused_sources='warn')
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(),
# ErrorPerVideo(model, after_epoch=True, on_interrupt=True),
LRDecay(lr_var, lrs, until_which_epoch,
after_epoch=True),
Printing(after_epoch=True)])
main_loop.run()
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 train(step_rule, layer_size, epochs, seed, experiment_path, initialization,
weight_noise, to_watch, patience, z_prob, z_prob_states,
z_prob_cells, drop_igates, ogates_zoneout, batch_size, stoch_depth,
share_mask, gaussian_drop, rnn_type, num_layers, norm_cost_coeff,
penalty, seq_len, input_drop, **kwargs):
print '.. CharPTB 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 STREAMS
#
###########################################
rng = np.random.RandomState(seed)
stream_args = dict(rng=rng,
pool_size=pool_size,
maximum_frames=maximum_frames,
pretrain_alignment=pretrain_alignment,
uniform_alignment=uniform_alignment,
window_features=window_features)
if share_mask:
z_prob_cells = z_prob
# we don't want to actually use these masks, so this is to debug
z_prob_states = None
print '.. initializing iterators'
train_stream = get_ptb_stream('train', batch_size, seq_len, z_prob_states,
z_prob_cells, z_prob_igates, layer_size,
False)
train_stream_evaluation = get_ptb_stream('train', batch_size, seq_len,
z_prob_states, z_prob_cells,
z_prob_igates, layer_size, True)
dev_stream = get_ptb_stream('valid', batch_size, seq_len, z_prob_states,
z_prob_cells, z_prob_igates, layer_size, True)
data = train_stream.get_epoch_iterator(as_dict=True).next()
###########################################
#
# 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']
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,
layer_size * 4,
name='in_to_hid',
weights_init=weights_init,
biases_init=Constant(0.0))
recurrent_layer = ZoneoutLSTM(dim=layer_size,
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,
layer_size * 3,
name='in_to_hid',
weights_init=weights_init,
biases_init=Constant(0.0))
recurrent_layer = ZoneoutGRU(dim=layer_size,
weights_init=weights_init,
activation=Tanh(),
name='rnn')
elif rnn_type.lower() == 'srnn': #FIXME!!! make ReLU
in_to_hid = Linear(50,
layer_size,
name='in_to_hid',
weights_init=weights_init,
biases_init=Constant(0.0))
recurrent_layer = ZoneoutSimpleRecurrent(dim=layer_size,
weights_init=weights_init,
activation=Rectifier(),
name='rnn')
else:
raise NotImplementedError
hid_to_out = Linear(layer_size,
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 AND MONITORS
#
###########################################
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))
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_valid = cost_train
cost_train += norm_cost_coeff * norm_cost
cost_train = cost_train.copy('cost_train')
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')
###########################################
#
# MAKE MODEL
#
###########################################
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)
]
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 = []
###########################################
#
# LOADING PRETRAINED MODELS (Mohammad Pezeshki)
#
###########################################
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
###########################################
#
# MOAR EXTENSIONS
#
###########################################
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_monitor, cost_per_character],
data_stream=test_stream,
prefix="test")
extensions.append(test_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 LOOP
#
###########################################
main_loop = MainLoop(model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
t1 = time.time()
print "Building time: %f" % (t1 - t0)
main_loop.run()
print "Execution time: %f" % (time.time() - t1)
def build_and_run(save_to,modelconfig,experimentconfig):
""" part of this is adapted from lasagne tutorial"""
n, num_filters, image_size, num_blockstack = modelconfig['depth'], modelconfig['num_filters'], modelconfig['image_size'], modelconfig['num_blockstack']
print("Amount of bottlenecks: %d" % n)
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('image_features')
#target_value = T.ivector('targets')
target_var = T.lmatrix('targets')
target_vec = T.extra_ops.to_one_hot(target_var[:,0],2)
#target_var = T.matrix('targets')
# Create residual net model
print("Building model...")
network = build_cnn(input_var, image_size, n, num_blockstack, num_filters)
get_info(network)
prediction = lasagne.utils.as_theano_expression(lasagne.layers.get_output(network))
test_prediction = lasagne.utils.as_theano_expression(lasagne.layers.get_output(network,deterministic=True))
# Loss function -> The objective to minimize
print("Instanciation of loss function...")
#loss = CategoricalCrossEntropy().apply(target_var.flatten(), prediction)
#test_loss = CategoricalCrossEntropy().apply(target_var.flatten(), test_prediction)
# loss = lasagne.objectives.categorical_crossentropy(prediction, target_var.flatten()).mean()
# test_loss = lasagne.objectives.categorical_crossentropy(test_prediction, target_var.flatten()).mean()
loss = lasagne.objectives.squared_error(prediction,target_vec).mean()
test_loss = lasagne.objectives.squared_error(test_prediction,target_vec).mean()
# loss = tensor.nnet.binary_crossentropy(prediction, target_var).mean()
# test_loss = tensor.nnet.binary_crossentropy(test_prediction, target_var).mean()
test_loss.name = "loss"
# loss.name = 'x-ent_error'
# loss.name = 'sqr_error'
layers = lasagne.layers.get_all_layers(network)
#l1 and l2 regularization
#pondlayers = {x:0.000025 for i,x in enumerate(layers)}
#l1_penality = lasagne.regularization.regularize_layer_params_weighted(pondlayers, lasagne.regularization.l2)
#l2_penality = lasagne.regularization.regularize_layer_params(layers[len(layers)/4:], lasagne.regularization.l1) * 25e-6
#reg_penalty = l1_penality + l2_penality
#reg_penalty.name = 'reg_penalty'
#loss = loss + reg_penalty
loss.name = 'reg_loss'
error_rate = MisclassificationRate().apply(target_var.flatten(), test_prediction).copy(
name='error_rate')
# Load the dataset
print("Loading data...")
istest = 'test' in experimentconfig.keys()
if istest:
print("Using test stream")
train_stream, valid_stream, test_stream = get_stream(experimentconfig['batch_size'],image_size,test=istest)
# Defining step rule and algorithm
if 'step_rule' in experimentconfig.keys() and not experimentconfig['step_rule'] is None :
step_rule = experimentconfig['step_rule'](learning_rate=experimentconfig['learning_rate'])
else :
step_rule=Scale(learning_rate=experimentconfig['learning_rate'])
params = map(lasagne.utils.as_theano_expression,lasagne.layers.get_all_params(network, trainable=True))
print("Initializing algorithm")
algorithm = GradientDescent(
cost=loss, gradients={var:T.grad(loss,var) for var in params},#parameters=cg.parameters, #params
step_rule=step_rule)
#algorithm.add_updates(extra_updates)
grad_norm = aggregation.mean(algorithm.total_gradient_norm)
grad_norm.name = "grad_norm"
print("Initializing extensions...")
plot = Plot(save_to, channels=[['train_loss','valid_loss'],
['train_grad_norm'],
#['train_grad_norm','train_reg_penalty'],
['train_error_rate','valid_error_rate']], server_url='http://hades.calculquebec.ca:5042')
checkpoint = Checkpoint('models/best_'+save_to+'.tar')
# checkpoint.add_condition(['after_n_batches=25'],
checkpoint.add_condition(['after_epoch'],
predicate=OnLogRecord('valid_error_rate_best_so_far'))
#Defining extensions
extensions = [Timing(),
FinishAfter(after_n_epochs=experimentconfig['num_epochs'],
after_n_batches=experimentconfig['num_batches']),
TrainingDataMonitoring([test_loss, error_rate, grad_norm], # reg_penalty],
prefix="train", after_epoch=True), #after_n_epochs=1
DataStreamMonitoring([test_loss, error_rate],valid_stream,prefix="valid", after_epoch=True), #after_n_epochs=1
plot,
#Checkpoint(save_to,after_n_epochs=5),
#ProgressBar(),
# Plot(save_to, channels=[['train_loss','valid_loss'], ['train_error_rate','valid_error_rate']], server_url='http://hades.calculquebec.ca:5042'), #'grad_norm'
# after_batch=True),
Printing(after_epoch=True),
TrackTheBest('valid_error_rate',min), #Keep best
checkpoint, #Save best
FinishIfNoImprovementAfter('valid_error_rate_best_so_far', epochs=5)] # Early-stopping
# model = Model(loss)
# print("Model",model)
main_loop = MainLoop(
algorithm,
train_stream,
# model=model,
extensions=extensions)
print("Starting main loop...")
main_loop.run()
False,
port=5551)
########### DEFINE THE ALGORITHM #############
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=Adam())
extensions = [
Timing(),
FinishAfter(after_n_epochs=num_epochs),
DataStreamMonitoring([cost, error_rate, error_rate2],
stream_valid,
prefix="valid"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint("catsVsDogs128.pkl"),
ProgressBar(),
Printing()
]
#Adding a live plot with the bokeh server
extensions.append(
Plot('CatsVsDogs_128_Layer3',
channels=[['train_error_rate', 'valid_error_rate'],
['valid_cost', 'valid_error_rate2'],
['train_total_gradient_norm']],
after_epoch=True))
def main(mode, save_path, steps, num_batches):
num_states = MarkovChainDataset.num_states
if mode == "train":
# Experiment configuration
rng = numpy.random.RandomState(1)
batch_size = 50
seq_len = 100
dim = 10
feedback_dim = 8
# Build the bricks and initialize them
transition = GatedRecurrent(name="transition", dim=dim,
activation=Tanh())
generator = SequenceGenerator(
Readout(readout_dim=num_states, source_names=["states"],
emitter=SoftmaxEmitter(name="emitter"),
feedback_brick=LookupFeedback(
num_states, feedback_dim, name='feedback'),
name="readout"),
transition,
weights_init=IsotropicGaussian(0.01), biases_init=Constant(0),
name="generator")
generator.push_initialization_config()
transition.weights_init = Orthogonal()
generator.initialize()
# Give an idea of what's going on.
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in Selector(generator).get_params().items()],
width=120))
logger.info("Markov chain entropy: {}".format(
MarkovChainDataset.entropy))
logger.info("Expected min error: {}".format(
-MarkovChainDataset.entropy * seq_len))
# Build the cost computation graph.
x = tensor.lmatrix('data')
cost = aggregation.mean(generator.cost_matrix(x[:, :]).sum(),
x.shape[1])
cost.name = "sequence_log_likelihood"
algorithm = GradientDescent(
cost=cost, params=list(Selector(generator).get_params().values()),
step_rule=Scale(0.001))
main_loop = MainLoop(
algorithm=algorithm,
data_stream=DataStream(
MarkovChainDataset(rng, seq_len),
iteration_scheme=ConstantScheme(batch_size)),
model=Model(cost),
extensions=[FinishAfter(after_n_batches=num_batches),
TrainingDataMonitoring([cost], prefix="this_step",
after_batch=True),
TrainingDataMonitoring([cost], prefix="average",
every_n_batches=100),
Checkpoint(save_path, every_n_batches=500),
Printing(every_n_batches=100)])
main_loop.run()
elif mode == "sample":
main_loop = cPickle.load(open(save_path, "rb"))
generator = main_loop.model
sample = ComputationGraph(generator.generate(
n_steps=steps, batch_size=1, iterate=True)).get_theano_function()
states, outputs, costs = [data[:, 0] for data in sample()]
numpy.set_printoptions(precision=3, suppress=True)
print("Generation cost:\n{}".format(costs.sum()))
freqs = numpy.bincount(outputs).astype(theano.config.floatX)
freqs /= freqs.sum()
print("Frequencies:\n {} vs {}".format(freqs,
MarkovChainDataset.equilibrium))
trans_freqs = numpy.zeros((num_states, num_states),
dtype=theano.config.floatX)
for a, b in zip(outputs, outputs[1:]):
trans_freqs[a, b] += 1
trans_freqs /= trans_freqs.sum(axis=1)[:, None]
print("Transition frequencies:\n{}\nvs\n{}".format(
trans_freqs, MarkovChainDataset.trans_prob))
else:
assert False
def initialize_all(config, save_path, bokeh_name, params, bokeh_server, bokeh,
test_tag, use_load_ext, load_log, fast_start):
root_path, extension = os.path.splitext(save_path)
data = Data(**config['data'])
train_conf = config['training']
recognizer = create_model(config, data, test_tag)
# Separate attention_params to be handled differently
# when regularization is applied
attention = recognizer.generator.transition.attention
attention_params = Selector(attention).get_parameters().values()
logger.info(
"Initialization schemes for all bricks.\n"
"Works well only in my branch with __repr__ added to all them,\n"
"there is an issue #463 in Blocks to do that properly.")
def show_init_scheme(cur):
result = dict()
for attr in dir(cur):
if attr.endswith('_init'):
result[attr] = getattr(cur, attr)
for child in cur.children:
result[child.name] = show_init_scheme(child)
return result
logger.info(pprint.pformat(show_init_scheme(recognizer)))
prediction, prediction_mask = add_exploration(recognizer, data, train_conf)
#
# Observables:
#
primary_observables = [] # monitored each batch
secondary_observables = [] # monitored every 10 batches
validation_observables = [] # monitored on the validation set
cg = recognizer.get_cost_graph(batch=True,
prediction=prediction,
prediction_mask=prediction_mask)
labels, = VariableFilter(applications=[recognizer.cost], name='labels')(cg)
labels_mask, = VariableFilter(applications=[recognizer.cost],
name='labels_mask')(cg)
gain_matrix = VariableFilter(
theano_name=RewardRegressionEmitter.GAIN_MATRIX)(cg)
if len(gain_matrix):
gain_matrix, = gain_matrix
primary_observables.append(named_copy(gain_matrix.min(), 'min_gain'))
primary_observables.append(named_copy(gain_matrix.max(), 'max_gain'))
batch_cost = cg.outputs[0].sum()
batch_size = named_copy(recognizer.recordings.shape[1], "batch_size")
# Assumes constant batch size. `aggregation.mean` is not used because
# of Blocks #514.
cost = batch_cost / batch_size
cost.name = "sequence_total_cost"
logger.info("Cost graph is built")
# Fetch variables useful for debugging.
# It is important not to use any aggregation schemes here,
# as it's currently impossible to spread the effect of
# regularization on their variables, see Blocks #514.
cost_cg = ComputationGraph(cost)
r = recognizer
energies, = VariableFilter(applications=[r.generator.readout.readout],
name="output_0")(cost_cg)
bottom_output = VariableFilter(applications=[r.bottom.apply],
name="output")(cost_cg)[-1]
attended, = VariableFilter(applications=[r.generator.transition.apply],
name="attended")(cost_cg)
attended_mask, = VariableFilter(applications=[
r.generator.transition.apply
],
name="attended_mask")(cost_cg)
weights, = VariableFilter(applications=[r.generator.evaluate],
name="weights")(cost_cg)
max_recording_length = named_copy(r.recordings.shape[0],
"max_recording_length")
# To exclude subsampling related bugs
max_attended_mask_length = named_copy(attended_mask.shape[0],
"max_attended_mask_length")
max_attended_length = named_copy(attended.shape[0], "max_attended_length")
max_num_phonemes = named_copy(labels.shape[0], "max_num_phonemes")
min_energy = named_copy(energies.min(), "min_energy")
max_energy = named_copy(energies.max(), "max_energy")
mean_attended = named_copy(abs(attended).mean(), "mean_attended")
mean_bottom_output = named_copy(
abs(bottom_output).mean(), "mean_bottom_output")
weights_penalty = named_copy(monotonicity_penalty(weights, labels_mask),
"weights_penalty")
weights_entropy = named_copy(entropy(weights, labels_mask),
"weights_entropy")
mask_density = named_copy(labels_mask.mean(), "mask_density")
cg = ComputationGraph([
cost, weights_penalty, weights_entropy, min_energy, max_energy,
mean_attended, mean_bottom_output, batch_size, max_num_phonemes,
mask_density
])
# Regularization. It is applied explicitly to all variables
# of interest, it could not be applied to the cost only as it
# would not have effect on auxiliary variables, see Blocks #514.
reg_config = config['regularization']
regularized_cg = cg
if reg_config.get('dropout'):
logger.info('apply dropout')
regularized_cg = apply_dropout(cg, [bottom_output], 0.5)
if reg_config.get('noise'):
logger.info('apply noise')
noise_subjects = [
p for p in cg.parameters if p not in attention_params
]
regularized_cg = apply_noise(cg, noise_subjects, reg_config['noise'])
train_cost = regularized_cg.outputs[0]
if reg_config.get("penalty_coof", .0) > 0:
# big warning!!!
# here we assume that:
# regularized_weights_penalty = regularized_cg.outputs[1]
train_cost = (train_cost + reg_config.get("penalty_coof", .0) *
regularized_cg.outputs[1] / batch_size)
if reg_config.get("decay", .0) > 0:
train_cost = (
train_cost + reg_config.get("decay", .0) *
l2_norm(VariableFilter(roles=[WEIGHT])(cg.parameters))**2)
train_cost = named_copy(train_cost, 'train_cost')
gradients = None
if reg_config.get('adaptive_noise'):
logger.info('apply adaptive noise')
if ((reg_config.get("penalty_coof", .0) > 0)
or (reg_config.get("decay", .0) > 0)):
logger.error('using adaptive noise with alignment weight panalty '
'or weight decay is probably stupid')
train_cost, regularized_cg, gradients, noise_brick = apply_adaptive_noise(
cg,
cg.outputs[0],
variables=cg.parameters,
num_examples=data.get_dataset('train').num_examples,
parameters=SpeechModel(
regularized_cg.outputs[0]).get_parameter_dict().values(),
**reg_config.get('adaptive_noise'))
train_cost.name = 'train_cost'
adapt_noise_cg = ComputationGraph(train_cost)
model_prior_mean = named_copy(
VariableFilter(applications=[noise_brick.apply],
name='model_prior_mean')(adapt_noise_cg)[0],
'model_prior_mean')
model_cost = named_copy(
VariableFilter(applications=[noise_brick.apply],
name='model_cost')(adapt_noise_cg)[0], 'model_cost')
model_prior_variance = named_copy(
VariableFilter(applications=[noise_brick.apply],
name='model_prior_variance')(adapt_noise_cg)[0],
'model_prior_variance')
regularized_cg = ComputationGraph(
[train_cost, model_cost] + regularized_cg.outputs +
[model_prior_mean, model_prior_variance])
primary_observables += [
regularized_cg.outputs[1], # model cost
regularized_cg.outputs[2], # task cost
regularized_cg.outputs[-2], # model prior mean
regularized_cg.outputs[-1]
] # model prior variance
# Model is weird class, we spend lots of time arguing with Bart
# what it should be. However it can already nice things, e.g.
# one extract all the parameters from the computation graphs
# and give them hierahical names. This help to notice when a
# because of some bug a parameter is not in the computation
# graph.
model = SpeechModel(train_cost)
if params:
logger.info("Load parameters from " + params)
# please note: we cannot use recognizer.load_params
# as it builds a new computation graph that dies not have
# shapred variables added by adaptive weight noise
param_values = load_parameter_values(params)
model.set_parameter_values(param_values)
parameters = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat([(key, parameters[key].get_value().shape)
for key in sorted(parameters.keys())],
width=120))
# Define the training algorithm.
clipping = StepClipping(train_conf['gradient_threshold'])
clipping.threshold.name = "gradient_norm_threshold"
rule_names = train_conf.get('rules', ['momentum'])
core_rules = []
if 'momentum' in rule_names:
logger.info("Using scaling and momentum for training")
core_rules.append(Momentum(train_conf['scale'],
train_conf['momentum']))
if 'adadelta' in rule_names:
logger.info("Using AdaDelta for training")
core_rules.append(
AdaDelta(train_conf['decay_rate'], train_conf['epsilon']))
max_norm_rules = []
if reg_config.get('max_norm', False) > 0:
logger.info("Apply MaxNorm")
maxnorm_subjects = VariableFilter(roles=[WEIGHT])(cg.parameters)
if reg_config.get('max_norm_exclude_lookup', False):
maxnorm_subjects = [
v for v in maxnorm_subjects
if not isinstance(get_brick(v), LookupTable)
]
logger.info("Parameters covered by MaxNorm:\n" + pprint.pformat(
[name for name, p in parameters.items() if p in maxnorm_subjects]))
logger.info("Parameters NOT covered by MaxNorm:\n" + pprint.pformat([
name for name, p in parameters.items() if not p in maxnorm_subjects
]))
max_norm_rules = [
Restrict(VariableClipping(reg_config['max_norm'], axis=0),
maxnorm_subjects)
]
burn_in = []
if train_conf.get('burn_in_steps', 0):
burn_in.append(BurnIn(num_steps=train_conf['burn_in_steps']))
algorithm = GradientDescent(
cost=train_cost,
parameters=parameters.values(),
gradients=gradients,
step_rule=CompositeRule(
[clipping] + core_rules + max_norm_rules +
# Parameters are not changed at all
# when nans are encountered.
[RemoveNotFinite(0.0)] + burn_in),
on_unused_sources='warn')
logger.debug("Scan Ops in the gradients")
gradient_cg = ComputationGraph(algorithm.gradients.values())
for op in ComputationGraph(gradient_cg).scans:
logger.debug(op)
# More variables for debugging: some of them can be added only
# after the `algorithm` object is created.
secondary_observables += list(regularized_cg.outputs)
if not 'train_cost' in [v.name for v in secondary_observables]:
secondary_observables += [train_cost]
secondary_observables += [
algorithm.total_step_norm, algorithm.total_gradient_norm,
clipping.threshold
]
for name, param in parameters.items():
num_elements = numpy.product(param.get_value().shape)
norm = param.norm(2) / num_elements**0.5
grad_norm = algorithm.gradients[param].norm(2) / num_elements**0.5
step_norm = algorithm.steps[param].norm(2) / num_elements**0.5
stats = tensor.stack(norm, grad_norm, step_norm, step_norm / grad_norm)
stats.name = name + '_stats'
secondary_observables.append(stats)
primary_observables += [
train_cost, algorithm.total_gradient_norm, algorithm.total_step_norm,
clipping.threshold, max_recording_length, max_attended_length,
max_attended_mask_length
]
validation_observables += [
rename(aggregation.mean(batch_cost, batch_size), cost.name),
rename(aggregation.sum_(batch_size), 'num_utterances'),
weights_entropy, weights_penalty
]
def attach_aggregation_schemes(variables):
# Aggregation specification has to be factored out as a separate
# function as it has to be applied at the very last stage
# separately to training and validation observables.
result = []
for var in variables:
if var.name == 'weights_penalty':
result.append(
rename(aggregation.mean(var, batch_size),
'weights_penalty_per_recording'))
elif var.name == 'weights_entropy':
result.append(
rename(aggregation.mean(var, labels_mask.sum()),
'weights_entropy_per_label'))
else:
result.append(var)
return result
mon_conf = config['monitoring']
# Build main loop.
logger.info("Initialize extensions")
extensions = []
if use_load_ext and params:
extensions.append(
Load(params, load_iteration_state=True, load_log=True))
if load_log and params:
extensions.append(LoadLog(params))
extensions += [
Timing(after_batch=True),
CGStatistics(),
#CodeVersion(['lvsr']),
]
extensions.append(
TrainingDataMonitoring(primary_observables, after_batch=True))
average_monitoring = TrainingDataMonitoring(
attach_aggregation_schemes(secondary_observables),
prefix="average",
every_n_batches=10)
extensions.append(average_monitoring)
validation = DataStreamMonitoring(
attach_aggregation_schemes(validation_observables),
data.get_stream("valid", shuffle=False),
prefix="valid").set_conditions(
before_first_epoch=not fast_start,
every_n_epochs=mon_conf['validate_every_epochs'],
every_n_batches=mon_conf['validate_every_batches'],
after_training=False)
extensions.append(validation)
per = PhonemeErrorRate(recognizer, data, **config['monitoring']['search'])
per_monitoring = DataStreamMonitoring(
[per],
data.get_stream("valid", batches=False, shuffle=False),
prefix="valid").set_conditions(
before_first_epoch=not fast_start,
every_n_epochs=mon_conf['search_every_epochs'],
every_n_batches=mon_conf['search_every_batches'],
after_training=False)
extensions.append(per_monitoring)
track_the_best_per = TrackTheBest(
per_monitoring.record_name(per)).set_conditions(
before_first_epoch=True, after_epoch=True)
track_the_best_cost = TrackTheBest(
validation.record_name(cost)).set_conditions(before_first_epoch=True,
after_epoch=True)
extensions += [track_the_best_cost, track_the_best_per]
extensions.append(
AdaptiveClipping(algorithm.total_gradient_norm.name,
clipping,
train_conf['gradient_threshold'],
decay_rate=0.998,
burnin_period=500))
extensions += [
SwitchOffLengthFilter(
data.length_filter,
after_n_batches=train_conf.get('stop_filtering')),
FinishAfter(after_n_batches=train_conf['num_batches'],
after_n_epochs=train_conf['num_epochs']).add_condition(
["after_batch"], _gradient_norm_is_none),
]
channels = [
# Plot 1: training and validation costs
[
average_monitoring.record_name(train_cost),
validation.record_name(cost)
],
# Plot 2: gradient norm,
[
average_monitoring.record_name(algorithm.total_gradient_norm),
average_monitoring.record_name(clipping.threshold)
],
# Plot 3: phoneme error rate
[per_monitoring.record_name(per)],
# Plot 4: training and validation mean weight entropy
[
average_monitoring._record_name('weights_entropy_per_label'),
validation._record_name('weights_entropy_per_label')
],
# Plot 5: training and validation monotonicity penalty
[
average_monitoring._record_name('weights_penalty_per_recording'),
validation._record_name('weights_penalty_per_recording')
]
]
if bokeh:
extensions += [
Plot(bokeh_name if bokeh_name else os.path.basename(save_path),
channels,
every_n_batches=10,
server_url=bokeh_server),
]
extensions += [
Checkpoint(save_path,
before_first_epoch=not fast_start,
after_epoch=True,
every_n_batches=train_conf.get('save_every_n_batches'),
save_separately=["model", "log"],
use_cpickle=True).add_condition(
['after_epoch'],
OnLogRecord(track_the_best_per.notification_name),
(root_path + "_best" + extension, )).add_condition(
['after_epoch'],
OnLogRecord(track_the_best_cost.notification_name),
(root_path + "_best_ll" + extension, )),
ProgressBar()
]
extensions.append(EmbedIPython(use_main_loop_run_caller_env=True))
if config['net']['criterion']['name'].startswith('mse'):
extensions.append(
LogInputsGains(labels, cg, recognizer.generator.readout.emitter,
data))
if train_conf.get('patience'):
patience_conf = train_conf['patience']
if not patience_conf.get('notification_names'):
# setdefault will not work for empty list
patience_conf['notification_names'] = [
track_the_best_per.notification_name,
track_the_best_cost.notification_name
]
extensions.append(Patience(**patience_conf))
extensions.append(
Printing(every_n_batches=1, attribute_filter=PrintingFilterList()))
return model, algorithm, data, extensions
def main(save_to, num_epochs):
mlp = MLP([Tanh(), Softmax()], [784, 100, 10],
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
mlp.initialize()
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
#attention --->
patch_shape = (16, 16);
image_shape = (784,100);
import numpy
import theano.tensor as T
n_spatial_dims = 2
cropper = SoftRectangularCropper(n_spatial_dims=n_spatial_dims,
patch_shape=patch_shape,
image_shape=image_shape,
kernel=Gaussian())
batch_size = 10
scales = 1.3**numpy.arange(-7, 6)
n_patches = len(scales)
locations = (numpy.ones((n_patches, batch_size, 2)) * image_shape/2).astype(numpy.float32)
scales = numpy.tile(scales[:, numpy.newaxis, numpy.newaxis], (1, batch_size, 2)).astype(numpy.float32)
Tpatches = T.stack(*[cropper.apply(x, T.constant(location), T.constant(scale))[0]
for location, scale in zip(locations, scales)])
patches = theano.function([x], Tpatches)(batch['features'])
import ipdb as pdb; pdb.set_trace()
probs = mlp.apply(tensor.flatten(patches, outdim=2))
cost = CategoricalCrossEntropy().apply(y.flatten(), probs)
error_rate = MisclassificationRate().apply(y.flatten(), probs)
cg = ComputationGraph([cost])
W1, W2 = VariableFilter(roles=[WEIGHT])(cg.variables)
cost = cost + .00005 * (W1 ** 2).sum() + .00005 * (W2 ** 2).sum()
cost.name = 'final_cost'
mnist_train = MNIST(("train",))
mnist_test = MNIST(("test",))
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=Scale(learning_rate=0.1))
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"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
Printing()]
if BLOCKS_EXTRAS_AVAILABLE:
extensions.append(Plot(
'MNIST example',
channels=[
['test_final_cost',
'test_misclassificationrate_apply_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()
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(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()
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()
def main(save_to, num_epochs):
mlp = MLP([Tanh(), Softmax()], [784, 100, 10],
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0))
mlp.initialize()
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
probs = mlp.apply(tensor.flatten(x, outdim=2))
cost = CategoricalCrossEntropy().apply(y.flatten(), probs)
error_rate = MisclassificationRate().apply(y.flatten(), probs)
cg = ComputationGraph([cost])
W1, W2 = VariableFilter(roles=[WEIGHT])(cg.variables)
cost = cost + .00005 * (W1 ** 2).sum() + .00005 * (W2 ** 2).sum()
cost.name = 'final_cost'
mnist_train = MNIST(("train",))
mnist_test = MNIST(("test",))
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=Scale(learning_rate=0.1))
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"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to, save_separately=['log'], after_batch=True),
Printing()]
if BLOCKS_EXTRAS_AVAILABLE:
extensions.append(Plot(
'MNIST example',
channels=[
['test_final_cost',
'test_misclassificationrate_apply_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()
import cPickle
import pandas
with open('mnist_log.pkl') as f:
log = cPickle.load(f)
data_frame = pandas.DataFrame.from_dict(log, orient='index')
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)
error_rate = error.copy(name='error_rate')
error_rate2 = error.copy(name='error_rate2')
cg = ComputationGraph([cost, error_rate])
### Gradient Descent
algorithm = GradientDescent(cost=cost, parameters=cg.parameters, step_rule=Scale(learning_rate=learning_rate))
extensions = [Timing(),
FinishAfter(after_n_epochs=num_epochs),
DataStreamMonitoring(
[cost, error_rate, error_rate2],
data_valid_stream,
prefix="valid"),
TrainingDataMonitoring(
[cost, error_rate,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
ProgressBar(),
Printing()]
### Plotting extensions
if mode == ("GPU_run" or "data_server"):
try:
from plot import Plot
extensions.append(Plot('%s %s @ %s' % (graph_name, datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']], after_epoch=True, server_url=host_plot))
PLOT_AVAILABLE = True
except ImportError:
def main(name, epochs, batch_size, learning_rate, attention, n_iter, enc_dim,
dec_dim, z_dim, oldmodel, image_size):
datasource = name
if datasource == 'mnist':
if image_size is not None:
raise Exception('image size for data source %s is pre configured' %
datasource)
image_size = 28
else:
if image_size is None:
raise Exception('Undefined image size for data source %s' %
datasource)
x_dim = image_size * image_size
img_height = img_width = image_size
rnninits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
if attention != "":
read_N, write_N = attention.split(',')
read_N = int(read_N)
write_N = int(write_N)
read_dim = 2 * read_N**2
reader = AttentionReader(x_dim=x_dim,
dec_dim=dec_dim,
width=img_width,
height=img_height,
N=read_N,
**inits)
writer = AttentionWriter(input_dim=dec_dim,
output_dim=x_dim,
width=img_width,
height=img_height,
N=write_N,
**inits)
attention_tag = "r%d-w%d" % (read_N, write_N)
else:
read_dim = 2 * x_dim
reader = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer = Writer(input_dim=dec_dim, output_dim=x_dim, **inits)
attention_tag = "full"
#----------------------------------------------------------------------
# Learning rate
def lr_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
exp = np.floor(np.log10(value))
leading = ("%e" % value)[0]
return "%s%d" % (leading, -exp)
lr_str = lr_tag(learning_rate)
name = "%s-%s-t%d-enc%d-dec%d-z%d-lr%s" % (name, attention_tag, n_iter,
enc_dim, dec_dim, z_dim, lr_str)
print("\nRunning experiment %s" % name)
print(" learning rate: %g" % learning_rate)
print(" attention: %s" % attention)
print(" n_iterations: %d" % n_iter)
print(" encoder dimension: %d" % enc_dim)
print(" z dimension: %d" % z_dim)
print(" decoder dimension: %d" % dec_dim)
print(" batch size: %d" % batch_size)
print()
#----------------------------------------------------------------------
encoder_rnn = LSTM(dim=enc_dim, name="RNN_enc", **rnninits)
decoder_rnn = LSTM(dim=dec_dim, name="RNN_dec", **rnninits)
encoder_mlp = MLP([Identity()], [(read_dim + dec_dim), 4 * enc_dim],
name="MLP_enc",
**inits)
decoder_mlp = MLP([Identity()], [z_dim, 4 * dec_dim],
name="MLP_dec",
**inits)
q_sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, **inits)
draw = DrawModel(n_iter,
reader=reader,
encoder_mlp=encoder_mlp,
encoder_rnn=encoder_rnn,
sampler=q_sampler,
decoder_mlp=decoder_mlp,
decoder_rnn=decoder_rnn,
writer=writer)
draw.initialize()
#------------------------------------------------------------------------
x = tensor.matrix('features')
#x_recons = 1. + x
x_recons, kl_terms = draw.reconstruct(x)
#x_recons, _, _, _, _ = draw.silly(x, n_steps=10, batch_size=100)
#x_recons = x_recons[-1,:,:]
#samples = draw.sample(100)
#x_recons = samples[-1, :, :]
#x_recons = samples[-1, :, :]
recons_term = BinaryCrossEntropy().apply(x, x_recons)
recons_term.name = "recons_term"
cost = recons_term + kl_terms.sum(axis=0).mean()
cost.name = "nll_bound"
#------------------------------------------------------------
cg = ComputationGraph([cost])
params = VariableFilter(roles=[PARAMETER])(cg.variables)
algorithm = GradientDescent(
cost=cost,
params=params,
step_rule=CompositeRule([
StepClipping(10.),
Adam(learning_rate),
])
#step_rule=RMSProp(learning_rate),
#step_rule=Momentum(learning_rate=learning_rate, momentum=0.95)
)
#algorithm.add_updates(scan_updates)
#------------------------------------------------------------------------
# Setup monitors
monitors = [cost]
for t in range(n_iter):
kl_term_t = kl_terms[t, :].mean()
kl_term_t.name = "kl_term_%d" % t
#x_recons_t = T.nnet.sigmoid(c[t,:,:])
#recons_term_t = BinaryCrossEntropy().apply(x, x_recons_t)
#recons_term_t = recons_term_t.mean()
#recons_term_t.name = "recons_term_%d" % t
monitors += [kl_term_t]
train_monitors = monitors[:]
train_monitors += [aggregation.mean(algorithm.total_gradient_norm)]
train_monitors += [aggregation.mean(algorithm.total_step_norm)]
# Live plotting...
plot_channels = [
["train_nll_bound", "test_nll_bound"],
["train_kl_term_%d" % t for t in range(n_iter)],
#["train_recons_term_%d" % t for t in range(n_iter)],
["train_total_gradient_norm", "train_total_step_norm"]
]
#------------------------------------------------------------
if datasource == 'mnist':
train_ds = BinarizedMNIST("train",
sources=['features'],
flatten=['features'])
test_ds = BinarizedMNIST("test",
sources=['features'],
flatten=['features'])
else:
datasource_fname = os.path.join(fuel.config.data_path, datasource,
datasource + '.hdf5')
train_ds = H5PYDataset(datasource_fname,
which_set='train',
sources=['features'],
flatten=['features'])
test_ds = H5PYDataset(datasource_fname,
which_set='test',
sources=['features'],
flatten=['features'])
train_stream = DataStream(train_ds,
iteration_scheme=SequentialScheme(
train_ds.num_examples, batch_size))
test_stream = DataStream(test_ds,
iteration_scheme=SequentialScheme(
test_ds.num_examples, batch_size))
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
Timing(),
FinishAfter(after_n_epochs=epochs),
TrainingDataMonitoring(train_monitors,
prefix="train",
after_epoch=True),
# DataStreamMonitoring(
# monitors,
# valid_stream,
## updates=scan_updates,
# prefix="valid"),
DataStreamMonitoring(
monitors,
test_stream,
# updates=scan_updates,
prefix="test"),
MyCheckpoint(image_size=image_size,
path=name + ".pkl",
before_training=False,
after_epoch=True,
save_separately=['log', 'model']),
#Dump(name),
# Plot(name, channels=plot_channels),
ProgressBar(),
Printing()
])
if oldmodel is not None:
print("Initializing parameters with old model %s" % oldmodel)
with open(oldmodel, "rb") as f:
oldmodel = pickle.load(f)
main_loop.model.set_param_values(oldmodel.get_param_values())
del oldmodel
main_loop.run()
def main(save_to, num_epochs,
weight_decay=0.0001, noise_pressure=0, subset=None, num_batches=None,
batch_size=None, histogram=None, resume=False):
output_size = 10
prior_noise_level = -10
noise_step_rule = Scale(1e-6)
noise_rate = theano.shared(numpy.asarray(1e-5, dtype=theano.config.floatX))
convnet = create_res_net(out_noise=True, tied_noise=True, tied_sigma=True,
noise_rate=noise_rate,
prior_noise_level=prior_noise_level)
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
test_probs = convnet.apply(x)
test_cost = (CategoricalCrossEntropy().apply(y.flatten(), test_probs)
.copy(name='cost'))
test_error_rate = (MisclassificationRate().apply(y.flatten(), test_probs)
.copy(name='error_rate'))
test_confusion = (ConfusionMatrix().apply(y.flatten(), test_probs)
.copy(name='confusion'))
test_confusion.tag.aggregation_scheme = Sum(test_confusion)
test_cg = ComputationGraph([test_cost, test_error_rate])
# Apply dropout to all layer outputs except final softmax
# dropout_vars = VariableFilter(
# roles=[OUTPUT], bricks=[Convolutional],
# theano_name_regex="^conv_[25]_apply_output$")(test_cg.variables)
# drop_cg = apply_dropout(test_cg, dropout_vars, 0.5)
# Apply 0.2 dropout to the pre-averaging layer
# dropout_vars_2 = VariableFilter(
# roles=[OUTPUT], bricks=[Convolutional],
# theano_name_regex="^conv_8_apply_output$")(test_cg.variables)
# train_cg = apply_dropout(test_cg, dropout_vars_2, 0.2)
# Apply 0.2 dropout to the input, as in the paper
# train_cg = apply_dropout(test_cg, [x], 0.2)
# train_cg = drop_cg
# train_cg = apply_batch_normalization(test_cg)
# train_cost, train_error_rate, train_components = train_cg.outputs
with batch_normalization(convnet):
with training_noise(convnet):
train_probs = convnet.apply(x)
train_cost = (CategoricalCrossEntropy().apply(y.flatten(), train_probs)
.copy(name='cost'))
train_components = (ComponentwiseCrossEntropy().apply(y.flatten(),
train_probs).copy(name='components'))
train_error_rate = (MisclassificationRate().apply(y.flatten(),
train_probs).copy(name='error_rate'))
train_cg = ComputationGraph([train_cost,
train_error_rate, train_components])
population_updates = get_batch_normalization_updates(train_cg)
bn_alpha = 0.9
extra_updates = [(p, p * bn_alpha + m * (1 - bn_alpha))
for p, m in population_updates]
# for annealing
nit_penalty = theano.shared(numpy.asarray(noise_pressure, dtype=theano.config.floatX))
nit_penalty.name = 'nit_penalty'
# Compute noise rates for training graph
train_logsigma = VariableFilter(roles=[LOG_SIGMA])(train_cg.variables)
train_mean_log_sigma = tensor.concatenate([n.flatten() for n in train_logsigma]).mean()
train_mean_log_sigma.name = 'mean_log_sigma'
train_nits = VariableFilter(roles=[NITS])(train_cg.auxiliary_variables)
train_nit_rate = tensor.concatenate([n.flatten() for n in train_nits]).mean()
train_nit_rate.name = 'nit_rate'
train_nit_regularization = nit_penalty * train_nit_rate
train_nit_regularization.name = 'nit_regularization'
# Apply regularization to the cost
trainable_parameters = VariableFilter(roles=[WEIGHT, BIAS])(
train_cg.parameters)
mask_parameters = [p for p in trainable_parameters
if get_brick(p).name == 'mask']
noise_parameters = VariableFilter(roles=[NOISE])(train_cg.parameters)
biases = VariableFilter(roles=[BIAS])(train_cg.parameters)
weights = VariableFilter(roles=[WEIGHT])(train_cg.variables)
nonmask_weights = [p for p in weights if get_brick(p).name != 'mask']
l2_norm = sum([(W ** 2).sum() for W in nonmask_weights])
l2_norm.name = 'l2_norm'
l2_regularization = weight_decay * l2_norm
l2_regularization.name = 'l2_regularization'
# testversion
test_cost = test_cost + l2_regularization
test_cost.name = 'cost_with_regularization'
# Training version of cost
train_cost_without_regularization = train_cost
train_cost_without_regularization.name = 'cost_without_regularization'
train_cost = train_cost + l2_regularization + train_nit_regularization
train_cost.name = 'cost_with_regularization'
cifar10_train = CIFAR10(("train",))
cifar10_train_stream = RandomPadCropFlip(
NormalizeBatchLevels(DataStream.default_stream(
cifar10_train, iteration_scheme=ShuffledScheme(
cifar10_train.num_examples, batch_size)),
which_sources=('features',)),
(32, 32), pad=4, which_sources=('features',))
test_batch_size = 128
cifar10_test = CIFAR10(("test",))
cifar10_test_stream = NormalizeBatchLevels(DataStream.default_stream(
cifar10_test,
iteration_scheme=ShuffledScheme(
cifar10_test.num_examples, test_batch_size)),
which_sources=('features',))
momentum = Momentum(0.01, 0.9)
# Create a step rule that doubles the learning rate of biases, like Caffe.
# scale_bias = Restrict(Scale(2), biases)
# step_rule = CompositeRule([scale_bias, momentum])
# Create a step rule that reduces the learning rate of noise
scale_mask = Restrict(noise_step_rule, mask_parameters)
step_rule = CompositeRule([scale_mask, momentum])
# from theano.compile.nanguardmode import NanGuardMode
# Train with simple SGD
algorithm = GradientDescent(
cost=train_cost, parameters=trainable_parameters,
step_rule=step_rule)
algorithm.add_updates(extra_updates)
#,
# theano_func_kwargs={
# 'mode': NanGuardMode(
# nan_is_error=True, inf_is_error=True, big_is_error=True)})
exp_name = save_to.replace('.%d', '')
# `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),
EpochSchedule(momentum.learning_rate, [
(0, 0.01), # Warm up with 0.01 learning rate
(50, 0.1), # Then go back to 0.1
(100, 0.01),
(150, 0.001)
# (83, 0.01), # Follow the schedule in the paper
# (125, 0.001)
]),
EpochSchedule(noise_step_rule.learning_rate, [
(0, 1e-2),
(2, 1e-1),
(4, 1)
# (0, 1e-6),
# (2, 1e-5),
# (4, 1e-4)
]),
EpochSchedule(noise_rate, [
(0, 1e-2),
(2, 1e-1),
(4, 1)
# (0, 1e-6),
# (2, 1e-5),
# (4, 1e-4),
# (6, 3e-4),
# (8, 1e-3), # Causes nit rate to jump
# (10, 3e-3),
# (12, 1e-2),
# (15, 3e-2),
# (19, 1e-1),
# (24, 3e-1),
# (30, 1)
]),
NoiseExtension(
noise_parameters=noise_parameters),
NoisyDataStreamMonitoring(
[test_cost, test_error_rate, test_confusion],
cifar10_test_stream,
noise_parameters=noise_parameters,
prefix="test"),
TrainingDataMonitoring(
[train_cost, train_error_rate, train_nit_rate,
train_cost_without_regularization,
l2_regularization,
train_nit_regularization,
momentum.learning_rate,
train_mean_log_sigma,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
every_n_batches=17),
# after_epoch=True),
Plot('Training performance for ' + exp_name,
channels=[
['train_cost_with_regularization',
'train_cost_without_regularization',
'train_nit_regularization',
'train_l2_regularization'],
['train_error_rate'],
['train_total_gradient_norm'],
['train_mean_log_sigma'],
],
every_n_batches=17),
Plot('Test performance for ' + exp_name,
channels=[[
'train_error_rate',
'test_error_rate',
]],
after_epoch=True),
EpochCheckpoint(save_to, use_cpickle=True, after_epoch=True),
ProgressBar(),
Printing()]
if histogram:
attribution = AttributionExtension(
components=train_components,
parameters=cg.parameters,
components_size=output_size,
after_batch=True)
extensions.insert(0, attribution)
if resume:
extensions.append(Load(exp_name, True, True))
model = Model(train_cost)
main_loop = MainLoop(
algorithm,
cifar10_train_stream,
model=model,
extensions=extensions)
main_loop.run()
if histogram:
save_attributions(attribution, filename=histogram)
with open('execution-log.json', 'w') as outfile:
json.dump(main_loop.log, outfile, cls=NumpyEncoder)
cg = ComputationGraph(cost)
if dropout > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(cg.variables)
cg = apply_dropout(cg, inputs, dropout)
cost = cg.outputs[0]
# Learning algorithm
step_rules = [RMSProp(learning_rate=learning_rate, decay_rate=decay_rate),
StepClipping(step_clipping)]
algorithm = GradientDescent(cost=cost, parameters=cg.parameters,
step_rule=CompositeRule(step_rules))
# Extensions
gradient_norm = aggregation.mean(algorithm.total_gradient_norm)
step_norm = aggregation.mean(algorithm.total_step_norm)
monitored_vars = [cost, gradient_norm, step_norm]
dev_monitor = DataStreamMonitoring(variables=[cost], after_epoch=True,
before_first_epoch=True, data_stream=dev_stream, prefix="dev")
train_monitor = TrainingDataMonitoring(variables=monitored_vars, after_batch=True,
before_first_epoch=True, prefix='tra')
extensions = [dev_monitor, train_monitor, Timing(), Printing(after_batch=True),
FinishAfter(after_n_epochs=nepochs),
saveload.Load(load_path),
saveload.Checkpoint(last_path),
] + track_best('dev_cost', save_path)
if learning_rate_decay not in (0, 1):
def main(save_to, model, train, test, num_epochs, input_size = (150,150), learning_rate=0.01,
batch_size=50, num_batches=None, flatten_stream=False):
"""
save_to : where to save trained model
model : model given in input must be already initialised (works with convnet and mlp)
input_size : the shape of the reshaped image in input (before flattening is applied if flatten_stream is True)
"""
if flatten_stream :
x = tensor.matrix('image_features')
else :
x = tensor.tensor4('image_features')
y = tensor.lmatrix('targets')
#Data augmentation
#insert data augmentation here
#Generating stream
train_stream = DataStream.default_stream(
train,
iteration_scheme=ShuffledScheme(train.num_examples, batch_size)
)
test_stream = DataStream.default_stream(
test,
iteration_scheme=ShuffledScheme(test.num_examples, batch_size)
)
#Reshaping procedure
#Add a crop option in scikitresize so that the image is not deformed
#Resize to desired square shape
train_stream = ScikitResize(train_stream, input_size, which_sources=('image_features',))
test_stream = ScikitResize(test_stream, input_size, which_sources=('image_features',))
#Flattening the stream
if flatten_stream is True:
train_stream = Flatten(train_stream, which_sources=('image_features',))
test_stream = Flatten(test_stream, which_sources=('image_features',))
# Apply input to model
probs = model.apply(x)
#Defining cost and various indices to watch
#print(probs)
#cost = SquaredError().apply(y.flatten(),probs)
cost = CategoricalCrossEntropy().apply(y.flatten(), probs).copy(name='cost')
error_rate = MisclassificationRate().apply(y.flatten(), probs).copy(
name='error_rate')
#Building Computation Graph
cg = ComputationGraph([cost, error_rate])
# Train with simple SGD
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=Scale(learning_rate=learning_rate))
#Defining extensions
extensions = [Timing(),
FinishAfter(after_n_epochs=num_epochs,
after_n_batches=num_batches),
TrainingDataMonitoring([cost, error_rate,aggregation.mean(algorithm.total_gradient_norm)], prefix="train", every_n_batches=5),
DataStreamMonitoring([cost, error_rate],test_stream,prefix="test", every_n_batches=25),
Checkpoint(save_to),
ProgressBar(),
Printing(every_n_batches=5)]
# `Timing` extension reports time for reading data, aggregating a batch
# and monitoring;
# `ProgressBar` displays a nice progress bar during training.
model = Model(cost)
main_loop = MainLoop(
algorithm,
train_stream,
model=model,
extensions=extensions)
main_loop.run()
def main(save_to, num_epochs):
batch_size = 128
dim = 100
n_steps = 20
i2h1 = MLP([Identity()], [784, dim], biases_init=Constant(0.), weights_init=IsotropicGaussian(.001))
h2o1 = MLP([Rectifier(), Logistic()], [dim, dim, 784],
biases_init=Constant(0.), weights_init=IsotropicGaussian(.001))
rec1 = SimpleRecurrent(dim=dim, activation=Tanh(), weights_init=Orthogonal())
i2h1.initialize()
h2o1.initialize()
rec1.initialize()
x = tensor.tensor3('features')
x1 = x[1:, :, :]
x2 = x[:-1, :, :]
preproc = i2h1.apply(x1)
h1 = rec1.apply(preproc)
x_hat = h2o1.apply(h1)
cost = tensor.nnet.binary_crossentropy(x_hat, x2).mean()
# cost = CategoricalCrossEntropy().apply(y.flatten(), probs)
cost.name = 'final_cost'
cg = ComputationGraph([cost, ])
mnist_train = MNIST("train", subset=slice(0, 50000), sources=('features', ))
mnist_valid = MNIST("train", subset=slice(50000, 60000), sources=('features',))
mnist_test = MNIST("test")
trainstream = Mapping(Flatten(DataStream(mnist_train,
iteration_scheme=SequentialScheme(50000, batch_size))),
_meanize(n_steps))
validstream = Mapping(Flatten(DataStream(mnist_valid,
iteration_scheme=SequentialScheme(10000,
batch_size))),
_meanize(n_steps))
teststream = Mapping(Flatten(DataStream(mnist_test,
iteration_scheme=SequentialScheme(10000,
batch_size))),
_meanize(n_steps))
algorithm = GradientDescent(
cost=cost, params=cg.parameters,
step_rule=CompositeRule([Adam(), StepClipping(100)]))
main_loop = MainLoop(
algorithm,
trainstream,
extensions=[Timing(),
FinishAfter(after_n_epochs=num_epochs),
# DataStreamMonitoring(
# [cost, ],
# teststream,
# prefix="test"),
DataStreamMonitoringAndSaving(
[cost, ],
validstream,
[i2h1, h2o1, rec1],
'best_'+save_to+'.pkl',
cost_name=cost.name,
after_epoch=True,
prefix='valid'),
TrainingDataMonitoring(
[cost,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
# Plot(
# save_to,
# channels=[
# ['test_final_cost',
# 'test_misclassificationrate_apply_error_rate'],
# ['train_total_gradient_norm']]),
Printing()])
main_loop.run()
def main(name, epochs, batch_size, learning_rate,
attention, n_iter, enc_dim, dec_dim, z_dim, oldmodel):
datasource = name
if datasource == 'mnist':
x_dim = 28*28
img_height, img_width = (28, 28)
elif datasource == 'sketch':
x_dim = 56*56
img_height, img_width = (56, 56)
else:
raise Exception('Unknown name %s'%datasource)
rnninits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
if attention != "":
read_N, write_N = attention.split(',')
read_N = int(read_N)
write_N = int(write_N)
read_dim = 2*read_N**2
reader = AttentionReader(x_dim=x_dim, dec_dim=dec_dim,
width=img_width, height=img_height,
N=read_N, **inits)
writer = AttentionWriter(input_dim=dec_dim, output_dim=x_dim,
width=img_width, height=img_height,
N=write_N, **inits)
attention_tag = "r%d-w%d" % (read_N, write_N)
else:
read_dim = 2*x_dim
reader = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer = Writer(input_dim=dec_dim, output_dim=x_dim, **inits)
attention_tag = "full"
#----------------------------------------------------------------------
# Learning rate
def lr_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
exp = np.floor(np.log10(value))
leading = ("%e"%value)[0]
return "%s%d" % (leading, -exp)
lr_str = lr_tag(learning_rate)
name = "DRAW-%s-%s-t%d-enc%d-dec%d-z%d-lr%s" % (name, attention_tag, n_iter, enc_dim, dec_dim, z_dim, lr_str)
print("\nRunning experiment %s" % name)
print(" learning rate: %5.5f" % learning_rate)
print(" attention: %s" % attention)
print(" n_iterations: %d" % n_iter)
print(" encoder dimension: %d" % enc_dim)
print(" z dimension: %d" % z_dim)
print(" decoder dimension: %d" % dec_dim)
print()
#----------------------------------------------------------------------
encoder_rnn = LSTM(dim=enc_dim, name="RNN_enc", **rnninits)
decoder_rnn = LSTM(dim=dec_dim, name="RNN_dec", **rnninits)
encoder_mlp = MLP([Identity()], [(read_dim+dec_dim), 4*enc_dim], name="MLP_enc", **inits)
decoder_mlp = MLP([Identity()], [ z_dim, 4*dec_dim], name="MLP_dec", **inits)
q_sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, **inits)
draw = DrawModel(
n_iter,
reader=reader,
encoder_mlp=encoder_mlp,
encoder_rnn=encoder_rnn,
sampler=q_sampler,
decoder_mlp=decoder_mlp,
decoder_rnn=decoder_rnn,
writer=writer)
draw.initialize()
#------------------------------------------------------------------------
x = tensor.matrix('features')
#x_recons = 1. + x
x_recons, kl_terms = draw.reconstruct(x)
#x_recons, _, _, _, _ = draw.silly(x, n_steps=10, batch_size=100)
#x_recons = x_recons[-1,:,:]
#samples = draw.sample(100)
#x_recons = samples[-1, :, :]
#x_recons = samples[-1, :, :]
nll_term = BinaryCrossEntropy().apply(x, x_recons)
nll_term.name = "nll_term"
kld_term = kl_terms.sum(axis=0).mean()
kld_term.name = "kld_term"
nll_bound = nll_term + kld_term
nll_bound.name = "nll_bound"
# grab the computation graph for the VFE bound on NLL
cg = ComputationGraph([nll_bound])
params = VariableFilter(roles=[PARAMETER])(cg.variables)
# apply some l2 regularization to the model parameters
reg_term = 1e-5 * sum([tensor.sum(p**2.0) for p in params])
reg_term.name = "reg_term"
# compute the final cost of VFE + regularization
cost = nll_bound + reg_term
cost.name = "full_cost"
algorithm = GradientDescent(
cost=cost,
params=params,
step_rule=CompositeRule([
StepClipping(10.),
Adam(learning_rate),
])
#step_rule=RMSProp(learning_rate),
#step_rule=Momentum(learning_rate=learning_rate, momentum=0.95)
)
#algorithm.add_updates(scan_updates)
#------------------------------------------------------------------------
# Setup monitors
monitors = [cost, nll_bound]
for t in range(n_iter):
kl_term_t = kl_terms[t,:].mean()
kl_term_t.name = "kl_term_%d" % t
#x_recons_t = T.nnet.sigmoid(c[t,:,:])
#recons_term_t = BinaryCrossEntropy().apply(x, x_recons_t)
#recons_term_t = recons_term_t.mean()
#recons_term_t.name = "recons_term_%d" % t
monitors +=[kl_term_t]
train_monitors = monitors[:]
train_monitors += [aggregation.mean(algorithm.total_gradient_norm)]
train_monitors += [aggregation.mean(algorithm.total_step_norm)]
# Live plotting...
plot_channels = [
["train_nll_bound", "valid_nll_bound"],
["train_kl_term_%d" % t for t in range(n_iter)],
#["train_recons_term_%d" % t for t in range(n_iter)],
["train_total_gradient_norm", "train_total_step_norm"]
]
#------------------------------------------------------------
if datasource == 'mnist':
mnist_train = BinarizedMNIST("train", sources=['features'], flatten=['features'])
mnist_valid = BinarizedMNIST("test", sources=['features'], flatten=['features'])
# mnist_test = BinarizedMNIST("test", sources=['features'], flatten=['features'])
train_stream = DataStream(mnist_train, iteration_scheme=SequentialScheme(mnist_train.num_examples, batch_size))
valid_stream = DataStream(mnist_valid, iteration_scheme=SequentialScheme(mnist_valid.num_examples, batch_size))
# test_stream = DataStream(mnist_test, iteration_scheme=SequentialScheme(mnist_test.num_examples, batch_size))
else:
raise Exception('Unknown name %s'%datasource)
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
Timing(),
FinishAfter(after_n_epochs=epochs),
TrainingDataMonitoring(
train_monitors,
prefix="train",
after_epoch=True),
DataStreamMonitoring(
monitors,
valid_stream,
prefix="valid"),
# DataStreamMonitoring(
# monitors,
# test_stream,
# prefix="test"),
Checkpoint(name+".pkl", after_epoch=True, save_separately=['log', 'model']),
# Dump(name),
Plot(name, channels=plot_channels),
ProgressBar(),
Printing()])
if oldmodel is not None:
print("Initializing parameters with old model %s"%oldmodel)
with open(oldmodel, "rb") as f:
oldmodel = pickle.load(f)
main_loop.model.set_param_values(oldmodel.get_param_values())
del oldmodel
main_loop.run()
def main(name, epochs, batch_size, learning_rate,
attention, n_iter, enc_dim, dec_dim, z_dim):
# Learning rate
def lr_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
exp = np.floor(np.log10(value))
leading = ("%e"%value)[0]
return "%s%d" % (leading, -exp)
if name is None:
tag = "watt" if attention else "woatt"
lr_str = lr_tag(learning_rate)
name = "%s-t%d-enc%d-dec%d-z%d-lr%s" % (tag, n_iter, enc_dim, dec_dim, z_dim, lr_str)
print("\nRunning experiment %s" % name)
print(" learning rate: %5.3f" % learning_rate)
print(" attention: %s" % attention)
print(" n_iterations: %d" % n_iter)
print(" encoder dimension: %d" % enc_dim)
print(" z dimension: %d" % z_dim)
print(" decoder dimension: %d" % dec_dim)
print()
#------------------------------------------------------------------------
x_dim = 28*28
img_height, img_width = (28, 28)
rnninits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
#'weights_init': Orthogonal(),
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
if attention:
read_N = 4
write_N = 7
read_dim = 2*read_N**2
reader = AttentionReader(x_dim=x_dim, dec_dim=dec_dim,
width=img_width, height=img_height,
N=read_N, **inits)
writer = AttentionWriter(input_dim=dec_dim, output_dim=x_dim,
width=img_width, height=img_height,
N=read_N, **inits)
else:
read_dim = 2*x_dim
reader = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer = Writer(input_dim=dec_dim, output_dim=x_dim, **inits)
encoder_rnn = LSTM(dim=enc_dim, name="RNN_enc", **rnninits)
decoder_rnn = LSTM(dim=dec_dim, name="RNN_dec", **rnninits)
encoder_mlp = MLP([Tanh()], [(read_dim+dec_dim), 4*enc_dim], name="MLP_enc", **inits)
decoder_mlp = MLP([Tanh()], [ z_dim, 4*dec_dim], name="MLP_dec", **inits)
q_sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, **inits)
draw = DrawModel(
n_iter,
reader=reader,
encoder_mlp=encoder_mlp,
encoder_rnn=encoder_rnn,
sampler=q_sampler,
decoder_mlp=decoder_mlp,
decoder_rnn=decoder_rnn,
writer=writer)
draw.initialize()
#------------------------------------------------------------------------
x = tensor.matrix('features')
#x_recons = 1. + x
x_recons, kl_terms = draw.reconstruct(x)
#x_recons, _, _, _, _ = draw.silly(x, n_steps=10, batch_size=100)
#x_recons = x_recons[-1,:,:]
#samples = draw.sample(100)
#x_recons = samples[-1, :, :]
#x_recons = samples[-1, :, :]
recons_term = BinaryCrossEntropy().apply(x, x_recons)
recons_term.name = "recons_term"
cost = recons_term + kl_terms.sum(axis=0).mean()
cost.name = "nll_bound"
#------------------------------------------------------------
cg = ComputationGraph([cost])
params = VariableFilter(roles=[PARAMETER])(cg.variables)
algorithm = GradientDescent(
cost=cost,
params=params,
step_rule=CompositeRule([
StepClipping(3.),
Adam(learning_rate),
])
#step_rule=RMSProp(learning_rate),
#step_rule=Momentum(learning_rate=learning_rate, momentum=0.95)
)
#algorithm.add_updates(scan_updates)
#------------------------------------------------------------------------
# Setup monitors
monitors = [cost]
"""
for t in range(n_iter):
kl_term_t = kl_terms[t,:].mean()
kl_term_t.name = "kl_term_%d" % t
x_recons_t = T.nnet.sigmoid(c[t,:,:])
recons_term_t = BinaryCrossEntropy().apply(x, x_recons_t)
recons_term_t = recons_term_t.mean()
recons_term_t.name = "recons_term_%d" % t
monitors +=[kl_term_t, recons_term_t]
"""
train_monitors = monitors[:]
train_monitors += [aggregation.mean(algorithm.total_gradient_norm)]
train_monitors += [aggregation.mean(algorithm.total_step_norm)]
# Live plotting...
plot_channels = [
["train_nll_bound", "test_nll_bound"],
["train_kl_term_%d" % t for t in range(n_iter)],
["train_recons_term_%d" % t for t in range(n_iter)],
["train_total_gradient_norm", "train_total_step_norm"]
]
#------------------------------------------------------------
mnist_train = BinarizedMNIST("train", sources=['features'])
mnist_test = BinarizedMNIST("test", sources=['features'])
main_loop = MainLoop(
model=Model(cost),
data_stream=ForceFloatX(DataStream(mnist_train,
iteration_scheme=SequentialScheme(
mnist_train.num_examples, batch_size))),
algorithm=algorithm,
extensions=[
Timing(),
FinishAfter(after_n_epochs=epochs),
DataStreamMonitoring(
monitors,
ForceFloatX(DataStream(mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, batch_size))),
## updates=scan_updates,
prefix="test"),
TrainingDataMonitoring(
train_monitors,
prefix="train",
after_every_epoch=True),
SerializeMainLoop(name+".pkl"),
Plot(name, channels=plot_channels),
ProgressBar(),
Printing()])
main_loop.run()
def main(save_to, num_epochs,
regularization=0.0003, subset=None, num_batches=None,
histogram=None, resume=False):
batch_size = 500
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)
cost = (CategoricalCrossEntropy().apply(y.flatten(), probs)
.copy(name='cost'))
components = (ComponentwiseCrossEntropy().apply(y.flatten(), probs)
.copy(name='components'))
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)
cg = ComputationGraph([cost, error_rate, components])
# Apply regularization to the cost
weights = VariableFilter(roles=[WEIGHT])(cg.variables)
l2_norm = sum([(W ** 2).sum() for W in weights])
l2_norm.name = 'l2_norm'
cost = cost + regularization * l2_norm
cost.name = 'cost_with_regularization'
if subset:
start = 30000 - subset // 2
mnist_train = MNIST(("train",), subset=slice(start, start+subset))
else:
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=AdaDelta(decay_rate=0.99))
# `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, confusion],
mnist_test_stream,
prefix="test"),
TrainingDataMonitoring(
[cost, error_rate, l2_norm,
aggregation.mean(algorithm.total_gradient_norm)],
prefix="train",
after_epoch=True),
Checkpoint(save_to),
ProgressBar(),
Printing()]
if histogram:
attribution = AttributionExtension(
components=components,
parameters=cg.parameters,
components_size=output_size,
after_batch=True)
extensions.insert(0, attribution)
if resume:
extensions.append(Load(save_to, True, True))
model = Model(cost)
main_loop = MainLoop(
algorithm,
mnist_train_stream,
model=model,
extensions=extensions)
main_loop.run()
if histogram:
save_attributions(attribution, filename=histogram)
with open('execution-log.json', 'w') as outfile:
json.dump(main_loop.log, outfile, cls=NumpyEncoder)
def main(save_to,
num_epochs,
regularization=0.001,
subset=None,
num_batches=None,
batch_size=None,
histogram=None,
resume=False):
output_size = 10
convnet = create_all_conv_net()
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
probs = convnet.apply(x)
test_cost = (CategoricalCrossEntropy().apply(y.flatten(),
probs).copy(name='cost'))
test_components = (ComponentwiseCrossEntropy().apply(
y.flatten(), probs).copy(name='components'))
test_error_rate = (MisclassificationRate().apply(
y.flatten(), probs).copy(name='error_rate'))
test_confusion = (ConfusionMatrix().apply(y.flatten(),
probs).copy(name='confusion'))
test_confusion.tag.aggregation_scheme = Sum(test_confusion)
test_cg = ComputationGraph([test_cost, test_error_rate, test_components])
# Apply dropout to all layer outputs except final softmax
dropout_vars = VariableFilter(
roles=[OUTPUT],
bricks=[Convolutional],
theano_name_regex="^conv_[25]_apply_output$")(test_cg.variables)
drop_cg = apply_dropout(test_cg, dropout_vars, 0.5)
# Apply 0.2 dropout to the pre-averaging layer
# dropout_vars_2 = VariableFilter(
# roles=[OUTPUT], bricks=[Convolutional],
# theano_name_regex="^conv_8_apply_output$")(drop_cg.variables)
# train_cg = apply_dropout(drop_cg, dropout_vars_2, 0.2)
# Apply 0.2 dropout to the input, as in the paper
# train_cg = apply_dropout(drop_cg, [x], 0.2)
train_cg = drop_cg
# train_cg = test_cg
train_cost, train_error_rate, train_components = train_cg.outputs
# Apply regularization to the cost
biases = VariableFilter(roles=[BIAS])(train_cg.parameters)
weights = VariableFilter(roles=[WEIGHT])(train_cg.variables)
l2_norm = sum([(W**2).sum() for W in weights])
l2_norm.name = 'l2_norm'
l2_regularization = regularization * l2_norm
l2_regularization.name = 'l2_regularization'
test_cost = test_cost + l2_regularization
test_cost.name = 'cost_with_regularization'
# Training version of cost
train_cost_without_regularization = train_cost
train_cost_without_regularization.name = 'cost_without_regularization'
train_cost = train_cost + regularization * l2_norm
train_cost.name = 'cost_with_regularization'
cifar10_train = CIFAR10(("train", ))
#cifar10_train_stream = RandomPadCropFlip(
# NormalizeBatchLevels(DataStream.default_stream(
# cifar10_train, iteration_scheme=ShuffledScheme(
# cifar10_train.num_examples, batch_size)),
# which_sources=('features',)),
# (32, 32), pad=5, which_sources=('features',))
cifar10_train_stream = NormalizeBatchLevels(DataStream.default_stream(
cifar10_train,
iteration_scheme=ShuffledScheme(cifar10_train.num_examples,
batch_size)),
which_sources=('features', ))
test_batch_size = 1000
cifar10_test = CIFAR10(("test", ))
cifar10_test_stream = NormalizeBatchLevels(DataStream.default_stream(
cifar10_test,
iteration_scheme=ShuffledScheme(cifar10_test.num_examples,
test_batch_size)),
which_sources=('features', ))
momentum = Momentum(0.002, 0.9)
# Create a step rule that doubles the learning rate of biases, like Caffe.
# scale_bias = Restrict(Scale(2), biases)
# step_rule = CompositeRule([scale_bias, momentum])
# step_rule = CompositeRule([StepClipping(100), momentum])
step_rule = momentum
# Train with simple SGD
algorithm = GradientDescent(cost=train_cost,
parameters=train_cg.parameters,
step_rule=step_rule)
# `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),
EpochSchedule(momentum.learning_rate, [(1, 0.005), (3, 0.01),
(5, 0.02), (200, 0.002),
(250, 0.0002), (300, 0.00002)]),
DataStreamMonitoring([test_cost, test_error_rate, test_confusion],
cifar10_test_stream,
prefix="test"),
TrainingDataMonitoring([
train_cost, train_error_rate, train_cost_without_regularization,
l2_regularization, momentum.learning_rate,
aggregation.mean(algorithm.total_gradient_norm)
],
prefix="train",
every_n_batches=10),
# after_epoch=True),
Plot('Training performance for ' + save_to,
channels=[
[
'train_cost_with_regularization',
'train_cost_without_regularization',
'train_l2_regularization'
],
['train_error_rate'],
['train_total_gradient_norm'],
],
every_n_batches=10),
# after_batch=True),
Plot('Test performance for ' + save_to,
channels=[[
'train_error_rate',
'test_error_rate',
]],
after_epoch=True),
Checkpoint(save_to),
ProgressBar(),
Printing()
]
if histogram:
attribution = AttributionExtension(components=train_components,
parameters=cg.parameters,
components_size=output_size,
after_batch=True)
extensions.insert(0, attribution)
if resume:
extensions.append(Load(save_to, True, True))
model = Model(train_cost)
main_loop = MainLoop(algorithm,
cifar10_train_stream,
model=model,
extensions=extensions)
main_loop.run()
if histogram:
save_attributions(attribution, filename=histogram)
with open('execution-log.json', 'w') as outfile:
json.dump(main_loop.log, outfile, cls=NumpyEncoder)
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(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.weghts_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)
params = model.get_params()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in params.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, params=cg.parameters,
step_rule=CompositeRule([StepClipping(10.0), Scale(0.01)]))
# Fetch variables useful for debugging
generator = reverser.generator
(energies,) = VariableFilter(
application=generator.readout.readout,
name="output")(cg.variables)
(activations,) = VariableFilter(
application=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, 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"))
# 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),
Plot(os.path.basename(save_path),
[[average_monitoring.record_name(cost)],
[average_monitoring.record_name(cost_per_character)]],
every_n_batches=10),
# 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_param_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(input_.shape[1], 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)
