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),
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 setup_mainloop(extension):
"""Set up a simple main loop for progress bar tests.
Create a MainLoop, register the given extension, supply it with a
DataStream and a minimal model/cost to optimize.
"""
# Since progressbar2 3.6.0, the `maxval` kwarg has been replaced by
# `max_value`, which has a default value of 100. If we're still using
# `maxval` by accident, this test should fail complaining that
# the progress bar has received a value out of range.
features = [numpy.array(f, dtype=theano.config.floatX)
for f in [[1, 2]] * 101]
dataset = IterableDataset(dict(features=features))
W = shared_floatx([0, 0], name='W')
x = tensor.vector('features')
cost = tensor.sum((x-W)**2)
cost.name = "cost"
algorithm = GradientDescent(cost=cost, parameters=[W],
step_rule=Scale(1e-3))
main_loop = MainLoop(
model=None, data_stream=dataset.get_example_stream(),
algorithm=algorithm,
extensions=[
FinishAfter(after_n_epochs=1),
extension])
return main_loop
def main(save_to, num_batches, continue_=False):
mlp = MLP([Tanh(), Identity()], [1, 10, 1],
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
seed=1)
mlp.initialize()
x = tensor.vector('numbers')
y = tensor.vector('roots')
cost = SquaredError().apply(y[:, None], mlp.apply(x[:, None]))
cost.name = "cost"
main_loop = MainLoop(
GradientDescent(cost=cost,
params=ComputationGraph(cost).parameters,
step_rule=Scale(learning_rate=0.001)),
get_data_stream(range(100)),
model=Model(cost),
extensions=([LoadFromDump(save_to)] if continue_ else []) + [
Timing(),
FinishAfter(after_n_batches=num_batches),
DataStreamMonitoring(
[cost], get_data_stream(range(100, 200)), prefix="test"),
TrainingDataMonitoring([cost], after_epoch=True),
Dump(save_to),
Printing()
])
main_loop.run()
return main_loop
def setup_mainloop(extension):
"""Set up a simple main loop for progress bar tests.
Create a MainLoop, register the given extension, supply it with a
DataStream and a minimal model/cost to optimize.
"""
features = [
numpy.array(f, dtype=theano.config.floatX)
for f in [[1, 2], [3, 4], [5, 6]]
]
dataset = IterableDataset(dict(features=features))
W = shared_floatx([0, 0], name='W')
x = tensor.vector('features')
cost = tensor.sum((x - W)**2)
cost.name = "cost"
algorithm = GradientDescent(cost=cost, params=[W], step_rule=Scale(1e-3))
main_loop = MainLoop(model=None,
data_stream=dataset.get_example_stream(),
algorithm=algorithm,
extensions=[FinishAfter(after_n_epochs=1), extension])
return main_loop
def learning_algorithm(args):
name = args.algorithm
learning_rate = float(args.learning_rate)
momentum = args.momentum
clipping_threshold = args.clipping
clipping = StepClipping(threshold=np.cast[floatX](clipping_threshold))
if name == 'adam':
adam = Adam(learning_rate=learning_rate)
step_rule = CompositeRule([adam, clipping])
learning_rate = adam.learning_rate
elif name == 'rms_prop':
rms_prop = RMSProp(learning_rate=learning_rate)
step_rule = CompositeRule([clipping, rms_prop])
learning_rate = rms_prop.learning_rate
elif name == 'momentum':
sgd_momentum = Momentum(learning_rate=learning_rate, momentum=momentum)
step_rule = CompositeRule([clipping, sgd_momentum])
learning_rate = sgd_momentum.learning_rate
elif name == 'sgd':
sgd = Scale(learning_rate=learning_rate)
step_rule = CompositeRule([clipping, sgd])
learning_rate = sgd.learning_rate
else:
raise NotImplementedError
return step_rule, learning_rate
def test_shared_variable_modifier_two_params():
weights = numpy.array([-1, 1], dtype=floatX)
features = [numpy.array(f, dtype=floatX) for f in [[1, 2], [3, 4], [5, 6]]]
targets = [(weights * f).sum() for f in features]
n_batches = 3
dataset = ContainerDataset(dict(features=features, targets=targets))
x = tensor.vector('features')
y = tensor.scalar('targets')
W = shared_floatx([0, 0], name='W')
cost = ((x * W).sum() - y)**2
cost.name = 'cost'
step_rule = Scale(0.001)
sgd = GradientDescent(cost=cost, params=[W], step_rule=step_rule)
modifier = SharedVariableModifier(
step_rule.learning_rate, lambda _, val: numpy.cast[floatX](val * 0.2))
main_loop = MainLoop(model=None,
data_stream=dataset.get_default_stream(),
algorithm=sgd,
extensions=[FinishAfter(after_n_epochs=1), modifier])
main_loop.run()
new_value = step_rule.learning_rate.get_value()
assert_allclose(new_value, 0.001 * 0.2**n_batches, atol=1e-5)
def test_shared_variable_modifier():
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')
cost = ((x * W).sum() - y) ** 2
cost.name = 'cost'
step_rule = Scale(0.001)
sgd = GradientDescent(cost=cost, parameters=[W],
step_rule=step_rule)
main_loop = MainLoop(
model=None, data_stream=dataset.get_example_stream(),
algorithm=sgd,
extensions=[
FinishAfter(after_n_epochs=1),
SharedVariableModifier(
step_rule.learning_rate,
lambda n: numpy.cast[theano.config.floatX](10. / n)
)])
main_loop.run()
assert_allclose(step_rule.learning_rate.get_value(),
numpy.cast[theano.config.floatX](10. / n_batches))
def setup_mainloop(extensions):
"""Create a MainLoop, register the given extension, supply it with a
DataStream and a minimal model/cost to optimize.
"""
features = [numpy.array(f, dtype=floatX) for f in [[1, 2], [3, 4], [5, 6]]]
dataset = IterableDataset(dict(features=features))
datastream = DataStream(dataset)
W = shared_floatx([0, 0], name='W')
add_role(W, PARAMETER)
x = tensor.vector('features')
cost = tensor.sum((x - W)**2)
cost.name = "cost"
algorithm = GradientDescent(cost=cost,
parameters=[W],
step_rule=Scale(1e-3))
main_loop = MainLoop(model=Model(cost),
data_stream=datastream,
algorithm=algorithm,
extensions=[
FinishAfter(after_n_epochs=1),
] + extensions)
return main_loop
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,
parameters=[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 train_model(cost,
train_stream,
valid_stream,
valid_freq,
valid_rare,
load_location=None,
save_location=None):
cost.name = 'nll'
perplexity = 2**(cost / tensor.log(2))
perplexity.name = 'ppl'
# Define the model
model = Model(cost)
# Load the parameters from a dumped model
if load_location is not None:
logger.info('Loading parameters...')
model.set_param_values(load_parameter_values(load_location))
cg = ComputationGraph(cost)
algorithm = GradientDescent(cost=cost,
step_rule=Scale(learning_rate=0.01),
params=cg.parameters)
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=[
DataStreamMonitoring([cost, perplexity],
valid_stream,
prefix='valid_all',
every_n_batches=5000),
# Overfitting of rare words occurs between 3000 and 4000 iterations
DataStreamMonitoring([cost, perplexity],
valid_rare,
prefix='valid_rare',
every_n_batches=500),
DataStreamMonitoring([cost, perplexity],
valid_freq,
prefix='valid_frequent',
every_n_batches=5000),
Printing(every_n_batches=500)
])
main_loop.run()
# Save the main loop
if save_location is not None:
logger.info('Saving the main loop...')
dump_manager = MainLoopDumpManager(save_location)
dump_manager.dump(main_loop)
logger.info('Saved')
def run(model_name, port_train, port_valid):
running_on_laptop = socket.gethostname() == 'yop'
X = tensor.tensor4('image_features', dtype='float32')
T = tensor.matrix('targets', dtype='float32')
image_border_size = (100, 100)
if running_on_laptop:
host_plot = 'http://*****:*****@ %s' % (model_name, datetime.datetime.now(), socket.gethostname()), channels=[['loss'], ['error', 'valid_error']], after_epoch=True, server_url=host_plot),
Printing(),
Checkpoint('train2')
]
main_loop = MainLoop(data_stream=train_stream, algorithm=algorithm,
extensions=extensions)
main_loop.run()
def __init__(self,
cost,
params,
subtensor_params={},
step_rule=None,
*args,
**kwargs):
full_params = params
self.subtensor_params = subtensor_params
# For each LookupTable, we replace it by its subtensors appearing in the graph
params = [
param for param in full_params if param not in subtensor_params
]
for _, (_, _, outputs, _) in subtensor_params.iteritems():
params.extend(outputs)
super(GradientDescent, self).__init__(cost=cost,
params=params,
**kwargs)
# self.params contains the list of outputs of the lookup tables
logger.info("Taking the cost gradient")
self.gradients = dict(
equizip(self.params, tensor.grad(self.cost, self.params)))
# We combine the gradients extracted from the same parameter
for param, (subparam, canonized_indices, outputs,
indices) in subtensor_params.iteritems():
# This is necessary if we want to compute the l2 norm correctly (e.g. for StepClipping)
tmp = shared_floatx(param.get_value() * 0.)
for (output, indice) in zip(outputs, indices):
tmp = tensor.inc_subtensor(tmp[indice], self.gradients[output])
del self.gradients[output]
self.gradients[subparam] = tmp[canonized_indices]
# We remove the subtensors from the list of parameters
self.params = full_params
logger.info("The cost gradient computation graph is built")
self.step_rule = step_rule if step_rule else Scale()
self.total_gradient_norm = named_copy(l2_norm(self.gradients.values()),
"total_gradient_norm")
self.steps, self.step_rule_updates = (self.step_rule.compute_steps(
self.gradients))
self.total_step_norm = named_copy(l2_norm(self.steps.values()),
"total_step_norm")
def test_composite_rule():
rule = CompositeRule([StepClipping(4), Scale(0.1)])
gradients = {0: shared_floatx(3.0), 1: shared_floatx(4.0)}
result, _ = rule.compute_steps(gradients)
assert_allclose(result[0].eval(), 12 / 50.0)
assert_allclose(result[1].eval(), 16 / 50.0)
class RuleWithUpdates(StepRule):
def __init__(self, updates):
self.updates = updates
def compute_steps(self, previous_steps):
return previous_steps, self.updates
rule = CompositeRule([RuleWithUpdates([(1, 2)]),
RuleWithUpdates([(3, 4)])])
assert rule.compute_steps(None)[1] == [(1, 2), (3, 4)]
def test_restrict():
rule1 = Scale(0.1)
rule2 = Restrict(rule1, (1, 4))
rval, _ = rule2.compute_steps(
OrderedDict((i, shared_floatx(i * i)) for i in range(6)))
assert_allclose(rval[0].eval(), 0.0)
assert_allclose(rval[1].eval(), 0.1)
assert_allclose(rval[2].eval(), 4.0)
assert_allclose(rval[3].eval(), 9.0)
assert_allclose(rval[4].eval(), 1.6)
assert_allclose(rval[5].eval(), 25.0)
steps, updates = Restrict(DummyUpdatesStepRule(), (1, 4)).compute_steps(
OrderedDict((i, shared_floatx(i * i)) for i in range(6)))
assert_allclose(steps[0].eval(), 0.0)
assert_allclose(steps[1].eval(), 3.0)
assert_allclose(steps[2].eval(), 4.0)
assert_allclose(steps[3].eval(), 9.0)
assert_allclose(steps[4].eval(), 18.0)
assert_allclose(steps[5].eval(), 25.0)
assert updates == [(10, 100), (40, 400)]
def setup_algorithms(cost, cg, method, type="ff"):
"""Setup training algorithm.
Parameters
----------
cost : expression
cost expression
cg : ComputationGraph
Computation graph
method : string
training method: SGD, momentum SGD, AdaGrad, RMSprop
learning_rate : float
learning rate for learning method
Returns
-------
algorithm : GradientDescent
Gradient Descent algorithm based on different optimization method
"""
if method == "sgd":
step_rule = Scale(learning_rate=0.01)
elif method == "momentum":
step_rule = Momentum(learning_rate=0.01, momentum=0.95)
elif method == "adagrad":
step_rule = AdaGrad()
elif method == "rmsprop":
step_rule = RMSProp()
if type == "RNN":
step_rule = CompositeRule([StepClipping(1.0), step_rule])
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=step_rule)
return algorithm
def main():
feature_maps = [20, 50]
mlp_hiddens = [50]
conv_sizes = [5, 5]
pool_sizes = [3, 3]
save_to = "DvC.pkl"
batch_size = 500
image_size = (32, 32)
output_size = 2
learningRate = 0.1
num_epochs = 10
num_batches = None
host_plot = 'http://*****:*****@ %s' %
('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['valid_cost', '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, 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,
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)
if mode is "data_server":
data_train_stream = ServerDataStream(('image_features','targets'), False, port=5560)
data_valid_stream = ServerDataStream(('image_features','targets'), False, port=5561)
### Setting up the model
probs = top_mlp.apply(conv_out)
cost = CategoricalCrossEntropy().apply(y.flatten(), probs).copy(name='cost')
error = MisclassificationRate().apply(y.flatten(), probs)
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()]
print("Cost graph is built", file=sys.stderr)
model = Model(cost)
parameters = model.get_parameter_dict()
for brick in model.get_top_bricks(): #{
brick.initialize()
#}
cg = ComputationGraph(cost)
algo = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule(
[StepClipping(10.0),
Scale(0.01)]))
#algo = RMSProp(learning_rate=1.0, decay_rate=0.9)
max_length = chars.shape[0].copy(name="max_length")
observables = [
batch_size, max_length, algo.total_step_norm, algo.total_gradient_norm,
cost
]
# Construct the main loop and start training!
average_monitoring = TrainingDataMonitoring(observables,
prefix="average",
every_n_batches=10)
checkpoint_after = n_epochs / 5
#checkpoint_after=100;
def __init__(self, learning_rate=1.0, momentum=0.):
scale = Scale(learning_rate=learning_rate)
basic_nesterov_momentum = BasicNesterovMomentum(momentum=momentum)
self.learning_rate = scale.learning_rate
self.momentum = basic_nesterov_momentum.momentum
self.components = [scale, basic_nesterov_momentum]
def train(algorithm, learning_rate, clipping, momentum, layer_size, epochs,
test_cost, experiment_path, initialization, init_width, weight_noise,
z_prob, z_prob_states, z_prob_cells, drop_prob_igates,
ogates_zoneout, batch_size, stoch_depth, share_mask, gaussian_drop,
rnn_type, num_layers, norm_cost_coeff, penalty, testing, seq_len,
decrease_lr_after_epoch, lr_decay, **kwargs):
print '.. PTB experiment'
print '.. arguments:', ' '.join(sys.argv)
t0 = time.time()
###########################################
#
# LOAD DATA
#
###########################################
def onehot(x, numclasses=None):
""" Convert integer encoding for class-labels (starting with 0 !)
to one-hot encoding.
The output is an array whose 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)
alphabetsize = 10000
data = np.load('penntree_char_and_word.npz')
trainset = data['train_words']
validset = data['valid_words']
testset = data['test_words']
if testing:
trainset = trainset[:3000]
validset = validset[:3000]
if share_mask:
if not z_prob:
raise ValueError('z_prob must be provided when using share_mask')
if z_prob_cells or z_prob_states:
raise ValueError(
'z_prob_states and z_prob_cells must not be provided when using share_mask (use z_prob instead)'
)
z_prob_cells = z_prob
# we don't want to actually use these masks, so this is to debug
z_prob_states = None
else:
if z_prob:
raise ValueError('z_prob is only used with share_mask')
z_prob_cells = z_prob_cells or '1'
z_prob_states = z_prob_states or '1'
# rng = np.random.RandomState(seed)
###########################################
#
# MAKE STREAMS
#
###########################################
def prep_dataset(dataset):
dataset = dataset[:(len(dataset) - (len(dataset) %
(seq_len * batch_size)))]
dataset = dataset.reshape(batch_size, -1, seq_len).transpose((1, 0, 2))
stream = DataStream(
IndexableDataset(indexables=OrderedDict([('data', dataset)])),
iteration_scheme=SequentialExampleScheme(dataset.shape[0]))
stream = Transpose(stream, [(1, 0)])
stream = SampleDropsNPWord(stream, z_prob_states, z_prob_cells,
drop_prob_igates, layer_size, num_layers,
False, stoch_depth, share_mask,
gaussian_drop, alphabetsize)
stream.sources = ('data', ) * 3 + stream.sources + (
'zoneouts_states', 'zoneouts_cells', 'zoneouts_igates')
return (stream, )
train_stream, = prep_dataset(trainset)
valid_stream, = prep_dataset(validset)
test_stream, = prep_dataset(testset)
####################
data = train_stream.get_epoch_iterator(as_dict=True).next()
####################
###########################################
#
# BUILD MODEL
#
###########################################
print '.. building model'
x = T.tensor3('data')
y = x
zoneouts_states = T.tensor3('zoneouts_states')
zoneouts_cells = T.tensor3('zoneouts_cells')
zoneouts_igates = T.tensor3('zoneouts_igates')
x.tag.test_value = data['data']
zoneouts_states.tag.test_value = data['zoneouts_states']
zoneouts_cells.tag.test_value = data['zoneouts_cells']
zoneouts_igates.tag.test_value = data['zoneouts_igates']
if init_width and not initialization == 'uniform':
raise ValueError('Width is only for uniform init, whassup?')
if initialization == 'glorot':
weights_init = NormalizedInitialization()
elif initialization == 'uniform':
weights_init = Uniform(width=init_width)
elif initialization == 'ortho':
weights_init = OrthogonalInitialization()
else:
raise ValueError('No such initialization')
if rnn_type.lower() == 'lstm':
in_to_hids = [
Linear(layer_size if l > 0 else alphabetsize,
layer_size * 4,
name='in_to_hid%d' % l,
weights_init=weights_init,
biases_init=Constant(0.0)) for l in range(num_layers)
]
recurrent_layers = [
DropLSTM(dim=layer_size,
weights_init=weights_init,
activation=Tanh(),
model_type=6,
name='rnn%d' % l,
ogates_zoneout=ogates_zoneout) for l in range(num_layers)
]
elif rnn_type.lower() == 'gru':
in_to_hids = [
Linear(layer_size if l > 0 else alphabetsize,
layer_size * 3,
name='in_to_hid%d' % l,
weights_init=weights_init,
biases_init=Constant(0.0)) for l in range(num_layers)
]
recurrent_layers = [
DropGRU(dim=layer_size,
weights_init=weights_init,
activation=Tanh(),
name='rnn%d' % l) for l in range(num_layers)
]
elif rnn_type.lower() == 'srnn': # FIXME!!! make ReLU
in_to_hids = [
Linear(layer_size if l > 0 else alphabetsize,
layer_size,
name='in_to_hid%d' % l,
weights_init=weights_init,
biases_init=Constant(0.0)) for l in range(num_layers)
]
recurrent_layers = [
DropSimpleRecurrent(dim=layer_size,
weights_init=weights_init,
activation=Rectifier(),
name='rnn%d' % l) for l in range(num_layers)
]
else:
raise NotImplementedError
hid_to_out = Linear(layer_size,
alphabetsize,
name='hid_to_out',
weights_init=weights_init,
biases_init=Constant(0.0))
for layer in in_to_hids:
layer.initialize()
for layer in recurrent_layers:
layer.initialize()
hid_to_out.initialize()
layer_input = x #in_to_hid.apply(x)
init_updates = OrderedDict()
for l, (in_to_hid, layer) in enumerate(zip(in_to_hids, recurrent_layers)):
rnn_embedding = in_to_hid.apply(layer_input)
if rnn_type.lower() == 'lstm':
states_init = theano.shared(
np.zeros((batch_size, layer_size), dtype=floatX))
cells_init = theano.shared(
np.zeros((batch_size, layer_size), dtype=floatX))
states_init.name, cells_init.name = "states_init", "cells_init"
states, cells = layer.apply(
rnn_embedding,
zoneouts_states[:, :, l * layer_size:(l + 1) * layer_size],
zoneouts_cells[:, :, l * layer_size:(l + 1) * layer_size],
zoneouts_igates[:, :, l * layer_size:(l + 1) * layer_size],
states_init, cells_init)
init_updates.update([(states_init, states[-1]),
(cells_init, cells[-1])])
elif rnn_type.lower() in ['gru', 'srnn']:
# untested!
states_init = theano.shared(
np.zeros((batch_size, layer_size), dtype=floatX))
states_init.name = "states_init"
states = layer.apply(rnn_embedding, zoneouts_states,
zoneouts_igates, states_init)
init_updates.update([(states_init, states[-1])])
else:
raise NotImplementedError
layer_input = states
y_hat_pre_softmax = hid_to_out.apply(T.join(0, [states_init], states[:-1]))
shape_ = y_hat_pre_softmax.shape
y_hat = Softmax().apply(y_hat_pre_softmax.reshape((-1, alphabetsize)))
####################
###########################################
#
# SET UP COSTS AND MONITORS
#
###########################################
cost = CategoricalCrossEntropy().apply(y.reshape((-1, alphabetsize)),
y_hat).copy('cost')
bpc = (cost / np.log(2.0)).copy(name='bpr')
perp = T.exp(cost).copy(name='perp')
cost_train = cost.copy(name='train_cost')
cg_train = ComputationGraph([cost_train])
###########################################
#
# 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':
for l in range(num_layers):
assert 'rnn%d_apply_states' % l in [
o.name
for o in VariableFilter(roles=[OUTPUT])(cg_train.variables)
]
for output in VariableFilter(roles=[OUTPUT])(cg_train.variables):
for l in range(num_layers):
if output.name == 'rnn%d_apply_states' % l:
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') #should this be cost_train.outputs[0]? no.
cg_train = ComputationGraph([cost_train])
###########################################
#
# 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')
model = Model(cost_train)
learning_rate = float(learning_rate)
clipping = StepClipping(threshold=np.cast[floatX](clipping))
if algorithm == 'adam':
adam = Adam(learning_rate=learning_rate)
learning_rate = adam.learning_rate
step_rule = CompositeRule([adam, clipping])
elif algorithm == 'rms_prop':
rms_prop = RMSProp(learning_rate=learning_rate)
learning_rate = rms_prop.learning_rate
step_rule = CompositeRule([clipping, rms_prop])
elif algorithm == 'momentum':
sgd_momentum = Momentum(learning_rate=learning_rate, momentum=momentum)
learning_rate = sgd_momentum.learning_rate
step_rule = CompositeRule([clipping, sgd_momentum])
elif algorithm == 'sgd':
sgd = Scale(learning_rate=learning_rate)
learning_rate = sgd.learning_rate
step_rule = CompositeRule([clipping, sgd])
else:
raise NotImplementedError
algorithm = GradientDescent(step_rule=step_rule,
cost=cost_train,
parameters=cg_train.parameters)
# theano_func_kwargs={"mode": theano.compile.MonitorMode(post_func=detect_nan)})
algorithm.add_updates(init_updates)
def cond_number(x):
_, _, sing_vals = T.nlinalg.svd(x, True, True)
sing_mags = abs(sing_vals)
return T.max(sing_mags) / T.min(sing_mags)
def rms(x):
return (x * x).mean().sqrt()
whysplode_cond = []
whysplode_rms = []
for i, p in enumerate(init_updates):
v = p.get_value()
if p.get_value().shape == 2:
whysplode_cond.append(
cond_number(p).copy(
'ini%d:%s_cond(%s)' %
(i, p.name, "x".join(map(str,
p.get_value().shape)))))
whysplode_rms.append(
rms(p).copy('ini%d:%s_rms(%s)' %
(i, p.name, "x".join(map(str,
p.get_value().shape)))))
for i, p in enumerate(cg_train.parameters):
v = p.get_value()
if p.get_value().shape == 2:
whysplode_cond.append(
cond_number(p).copy(
'ini%d:%s_cond(%s)' %
(i, p.name, "x".join(map(str,
p.get_value().shape)))))
whysplode_rms.append(
rms(p).copy('ini%d:%s_rms(%s)' %
(i, p.name, "x".join(map(str,
p.get_value().shape)))))
observed_vars = [
cost_train, cost, bpc, perp, learning_rate,
aggregation.mean(
algorithm.total_gradient_norm).copy("gradient_norm_mean")
] # + whysplode_rms
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_inits = [p.clone() for p in init_updates]
cg_dev = ComputationGraph([cost, bpc, perp] +
init_updates.values()).replace(
zip(init_updates.keys(), dev_inits))
dev_cost, dev_bpc, dev_perp = cg_dev.outputs[:3]
dev_init_updates = OrderedDict(zip(dev_inits, cg_dev.outputs[3:]))
dev_monitor = DataStreamMonitoring(variables=[dev_cost, dev_bpc, dev_perp],
data_stream=valid_stream,
prefix="dev",
updates=dev_init_updates)
# noone does this
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 = []
extensions.extend(
[FinishAfter(after_n_epochs=epochs), train_monitor, dev_monitor])
if test_cost:
test_inits = [p.clone() for p in init_updates]
cg_test = ComputationGraph([cost, bpc, perp] +
init_updates.values()).replace(
zip(init_updates.keys(), test_inits))
test_cost, test_bpc, test_perp = cg_test.outputs[:3]
test_init_updates = OrderedDict(zip(test_inits, cg_test.outputs[3:]))
test_monitor = DataStreamMonitoring(
variables=[test_cost, test_bpc, test_perp],
data_stream=test_stream,
prefix="test",
updates=test_init_updates)
extensions.extend([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_cost', model, experiment_path, every_n_epochs=1))
extensions.append(SaveLog(every_n_epochs=1))
extensions.append(ProgressBar())
extensions.append(Printing())
class RollsExtension(TrainingExtension):
""" rolls the cell and state activations between epochs so that first batch gets correct initial activations """
def __init__(self, shvars):
self.shvars = shvars
def before_epoch(self):
for v in self.shvars:
v.set_value(np.roll(v.get_value(), 1, 0))
extensions.append(
RollsExtension(init_updates.keys() + dev_init_updates.keys() +
(test_init_updates.keys() if test_cost else [])))
class LearningRateSchedule(TrainingExtension):
""" Lets you set a number to divide learning rate by each epoch + when to start doing that """
def __init__(self):
self.epoch_number = 0
def after_epoch(self):
self.epoch_number += 1
if self.epoch_number > decrease_lr_after_epoch:
learning_rate.set_value(learning_rate.get_value() / lr_decay)
if bool(lr_decay) != bool(decrease_lr_after_epoch):
raise ValueError(
'Need to define both lr_decay and decrease_lr_after_epoch')
if lr_decay and decrease_lr_after_epoch:
extensions.append(LearningRateSchedule())
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 run(get_model, model_name):
train_stream = ServerDataStream(
('cases', 'image_features', 'image_targets', 'multiplier'),
False,
hwm=10)
valid_stream = ServerDataStream(
('cases', 'image_features', 'image_targets', 'multiplier'),
False,
hwm=10,
port=5558)
input_var = tensor.tensor4('image_features')
target_var = tensor.tensor4('image_targets')
multiply_var = tensor.matrix('multiplier')
multiply_var = T.addbroadcast(multiply_var, 1)
test_prediction, prediction, params = get_model(input_var, target_var,
multiply_var)
loss = binary_crossentropy(prediction, target_var).mean()
loss.name = 'loss'
valid_error = T.neq((test_prediction > 0.5) * 1., target_var).mean()
valid_error.name = 'error'
scale = Scale(0.1)
algorithm = GradientDescent(
cost=loss,
parameters=params,
step_rule=scale,
#step_rule=Adam(),
on_unused_sources='ignore')
host_plot = 'http://localhost:5006'
extensions = [
Timing(),
TrainingDataMonitoring([loss], after_epoch=True),
DataStreamMonitoring(variables=[loss, valid_error],
data_stream=valid_stream,
prefix="valid"),
Plot('%s %s %s' %
(model_name, datetime.date.today(), time.strftime('%H:%M')),
channels=[['loss', 'valid_loss'], ['valid_error']],
after_epoch=True,
server_url=host_plot),
Printing(),
# Checkpoint('train'),
FinishAfter(after_n_epochs=10)
]
main_loop = MainLoop(data_stream=train_stream,
algorithm=algorithm,
extensions=extensions)
cg = ComputationGraph(test_prediction)
while True:
main_loop.run()
scale.learning_rate.set_value(
numpy.float32(scale.learning_rate.get_value() * 0.7))
numpy.savez('best_weights.npz',
[param.get_value() for param in cg.shared_variables])
def main(save_to,
num_epochs,
feature_maps=None,
mlp_hiddens=None,
conv_sizes=None,
pool_sizes=None,
batch_size=500):
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.get_dim('output')))
x = tensor.tensor4('features')
y = tensor.lmatrix('targets')
# Normalize input and apply the convnet
probs = convnet.apply(x)
cost = named_copy(CategoricalCrossEntropy().apply(y.flatten(), probs),
'cost')
error_rate = named_copy(MisclassificationRate().apply(y.flatten(), probs),
'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),
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()
#print(cg.variables)
print("Created ComputationGraph, parameters:")
#print(cg.parameters)
for p in cg.parameters:
print(str(p), p.dtype, p.shape.tag.test_value)
print("Created ComputationGraph, inputs:")
print(cg.inputs)
algorithm = GradientDescent(
cost=cost,
parameters=cg.parameters,
step_rule=CompositeRule([
StepClipping(10.0),
Scale(0.01),
]),
)
print("Defined Algorithm")
model = Model(cost)
print("Defined Model")
obs_max_length = named_copy(x.shape[0], "obs_max_length")
observables = [
cost,
obs_max_length,
#min_energy, max_energy,
#mean_activation,
]
seq_gen.initialize()
# z markov_tutorial
x = tensor.lvector('features')
x = x.reshape((x.shape[0], 1))
cost = aggregation.mean(seq_gen.cost_matrix(x[:, :]).sum(), x.shape[1])
cost.name = "negative log-likelihood"
cost_cg = ComputationGraph(cost)
print VariableFilter(roles=[WEIGHT])(cost_cg.variables)
# theano.printing.pydotprint(cost, outfile="./pics/symbolic_graph_unopt.png", var_with_name_simple=True)
algorithm = GradientDescent(cost=cost,
parameters=list(
Selector(seq_gen).get_parameters().values()),
step_rule=Scale(0.001))
# AUDIOSCOPE OBSERVABLES (some)
observables = []
observables += cost_cg.outputs
observables.append(algorithm.total_step_norm)
observables.append(algorithm.total_gradient_norm)
print observables
# AUDIOSCOPE EXTENSIONS
extensions = []
extensions.append(Timing(after_batch=True))
extensions.append(TrainingDataMonitoring(list(observables), after_batch=True))
averaging_frequency = 1000
average_monitor = TrainingDataMonitoring(observables,
def main():
mlp_hiddens = [1000]
filter_sizes = [(9, 9), (5, 5), (5, 5)]
feature_maps = [80, 50, 20]
pooling_sizes = [(3, 3), (2, 2), (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(num_epochs,
feature_maps=None,
mlp_hiddens=None,
conv_sizes=None,
pool_sizes=None,
batch_size=500,
num_batches=None):
############# Architecture #############
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, 32)
batch_size = 50
output_size = 2
learningRate = 0.1
num_epochs = 10
num_batches = None
delta = 0.01
drop_prob = 0.5
weight_noise = 0.75
# 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')
probs = (convnet.apply(x)).copy(name='probs')
# Computational Graph just for cost for drop_out and noise application
cg_probs = ComputationGraph([probs])
inputs = VariableFilter(roles=[INPUT])(cg_probs.variables)
weights = VariableFilter(roles=[FILTER, WEIGHT])(cg_probs.variables)
############# Regularization #############
#regularization = 0
logger.info('Applying regularization')
regularization = delta * sum([(W**2).mean() for W in weights])
probs.name = "reg_probs"
############# Guaussian Noise #############
logger.info('Applying Gaussian noise')
cg_train = apply_noise(cg_probs, weights, weight_noise)
############# Dropout #############
logger.info('Applying dropout')
cg_probs = apply_dropout(cg_probs, inputs, drop_prob)
dropped_out = VariableFilter(roles=[DROPOUT])(cg_probs.variables)
inputs_referenced = [var.tag.replacement_of for var in dropped_out]
set(inputs) == set(inputs_referenced)
############# Batch normalization #############
# recalculate probs after dropout and noise and regularization:
probs = cg_probs.outputs[0] + regularization
cost = (CategoricalCrossEntropy().apply(y.flatten(),
probs).copy(name='cost'))
error_rate = (MisclassificationRate().apply(y.flatten(),
probs).copy(name='error_rate'))
cg = ComputationGraph([probs, cost, error_rate])
cg = apply_batch_normalization(cg)
########### 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(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_rotate,
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, 20)))
stream_data_test = create_data(
DogsVsCats(('train', ), subset=slice(20, 30)))
# Train with simple SGD
algorithm = GradientDescent(cost=cost,
parameters=cg.parameters,
step_rule=Scale(learning_rate=learningRate))
#algorithm = GradientDescent(cost=cost, parameters=cg.parameters,step_rule=Adam(0.001))
#algorithm.add_updates(extra_updates)
# `Timing` extension reports time for reading data, aggregating a batch and monitoring;
# `ProgressBar` displays a nice progress bar during training.
extensions = []
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())
logger.info("Building the model")
model = Model(cost)
main_loop = MainLoop(algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
mnist_train = MNIST("train")
train_stream = Flatten(
DataStream.default_stream(dataset=mnist_train,
iteration_scheme=SequentialScheme(
mnist_train.num_examples, 128)), )
# load testing data
mnist_test = MNIST("test")
test_stream = Flatten(
DataStream.default_stream(dataset=mnist_test,
iteration_scheme=SequentialScheme(
mnist_test.num_examples, 1024)), )
# train the model
from blocks.model import Model
main_loop = MainLoop(model=Model(cost),
data_stream=train_stream,
algorithm=GradientDescent(
cost=cost,
params=ComputationGraph(cost).parameters,
step_rule=Scale(learning_rate=0.1)),
extensions=[
FinishAfter(after_n_epochs=5),
DataStreamMonitoring(variables=[cost, error_rate],
data_stream=test_stream,
prefix="test"),
Printing()
])
main_loop.run()
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()