def do(self, which_callback, *args):
model = self.main_loop.model
f = open(
self.name + '_epoch_' +
str(self.main_loop.log.status['epochs_done']) + '.pkl', 'w')
dump(model, f)
f.close()
def maxout_vae_mnist_test(path_vae_mnist):
# load vae model on mnist
vae_mnist = load(path_vae_mnist)
maxout = Maxout()
x = T.matrix('features')
y = T.imatrix('targets')
batch_size = 128
z, _ = vae_mnist.sampler.sample(vae_mnist.encoder_mlp.apply(x))
predict = maxout.apply(z)
cost = Softmax().categorical_cross_entropy(y.flatten(), predict)
y_hat = Softmax().apply(predict)
cost.name = 'cost'
cg = ComputationGraph(cost)
temp = cg.parameters
for t, i in zip(temp, range(len(temp))):
t.name = t.name+str(i)+"maxout"
error_brick = MisclassificationRate()
error_rate = error_brick.apply(y, y_hat)
# training
step_rule = RMSProp(0.01, 0.9)
#step_rule = Momentum(0.2, 0.9)
train_set = MNIST('train')
test_set = MNIST("test")
data_stream_train = Flatten(DataStream.default_stream(
train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size)))
data_stream_test =Flatten(DataStream.default_stream(
test_set, iteration_scheme=SequentialScheme(test_set.num_examples, batch_size)))
algorithm = GradientDescent(cost=cost, params=cg.parameters,
step_rule=step_rule)
monitor_train = TrainingDataMonitoring(
variables=[cost], data_stream=data_stream_train, prefix="train")
monitor_valid = DataStreamMonitoring(
variables=[cost, error_rate], data_stream=data_stream_test, prefix="test")
extensions = [ monitor_train,
monitor_valid,
FinishAfter(after_n_epochs=50),
Printing(every_n_epochs=1)
]
main_loop = MainLoop(data_stream=data_stream_train,
algorithm=algorithm, model = Model(cost),
extensions=extensions)
main_loop.run()
# save here
from blocks.serialization import dump
with closing(open('../data_mnist/maxout', 'w')) as f:
dump(maxout, f)
def test_protocol0_regression():
"""Check for a regression where protocol 0 dumps fail on load."""
brick = Linear(5, 10)
brick.allocate()
buf = BytesIO()
dump(brick, buf, parameters=list(brick.parameters), protocol=0)
try:
load(buf)
except TypeError:
assert False # Regression
def do(self, callback_name, *args):
"""Pickle the model to the disk.
"""
logger.info("ModelLogger has started")
path = os.path.join(self.folder,
"{}{}.tar".format(self.file_prefix, self.counter))
with open(path, 'wb') as f:
dump(self.main_loop.model, f, use_cpickle=self.use_cpickle)
logger.info("ModelLogger has finished")
self.counter += 1
def test_add_to_dump():
# Create a simple MLP to dump.
mlp = MLP(activations=[None, None],
dims=[10, 10, 10],
weights_init=Constant(1.),
use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
mlp2 = MLP(activations=[None, None],
dims=[10, 10, 10],
weights_init=Constant(1.),
use_bias=False,
name='mlp2')
mlp2.initialize()
# Ensure that adding to dump is working.
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb+') as ff:
add_to_dump(mlp.children[0],
ff,
'child_0',
parameters=[mlp.children[0].W])
add_to_dump(mlp.children[1], ff, 'child_1')
with tarfile.open(f.name, 'r') as tarball:
assert set(tarball.getnames()) == set(
['_pkl', '_parameters', 'child_0', 'child_1'])
# Ensure that we can load any object from the tarball.
with open(f.name, 'rb') as ff:
saved_children_0 = load(ff, 'child_0')
saved_children_1 = load(ff, 'child_1')
assert_allclose(saved_children_0.W.get_value(), numpy.ones((10, 10)))
assert_allclose(saved_children_1.W.get_value(),
numpy.ones((10, 10)) * 2)
# Check the error if using a reserved name.
with open(f.name, 'rb+') as ff:
assert_raises(ValueError, add_to_dump, *[mlp.children[0], ff, '_pkl'])
# Check the error if saving an object with other parameters
with open(f.name, 'rb+') as ff:
assert_raises(
ValueError, add_to_dump, *[mlp2, ff, 'mlp2'],
**dict(parameters=[mlp2.children[0].W, mlp2.children[1].W]))
# Check the warning if adding to a dump with no parameters
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
with open(f.name, 'rb+') as ff:
assert_raises(
ValueError, add_to_dump, *[mlp2, ff, 'mlp2'],
**dict(parameters=[mlp2.children[0].W, mlp2.children[1].W]))
def test_add_to_dump():
# Create a simple MLP to dump.
mlp = MLP(activations=[None, None], dims=[10, 10, 10],
weights_init=Constant(1.), use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
mlp2 = MLP(activations=[None, None], dims=[10, 10, 10],
weights_init=Constant(1.), use_bias=False,
name='mlp2')
mlp2.initialize()
# Ensure that adding to dump is working.
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb+') as ff:
add_to_dump(mlp.children[0], ff, 'child_0',
parameters=[mlp.children[0].W])
add_to_dump(mlp.children[1], ff, 'child_1')
with tarfile.open(f.name, 'r') as tarball:
assert set(tarball.getnames()) == set(['_pkl', '_parameters',
'child_0', 'child_1'])
# Ensure that we can load any object from the tarball.
with open(f.name, 'rb') as ff:
saved_children_0 = load(ff, 'child_0')
saved_children_1 = load(ff, 'child_1')
assert_allclose(saved_children_0.W.get_value(),
numpy.ones((10, 10)))
assert_allclose(saved_children_1.W.get_value(),
numpy.ones((10, 10)) * 2)
# Check the error if using a reserved name.
with open(f.name, 'rb+') as ff:
assert_raises(ValueError, add_to_dump, *[mlp.children[0], ff, '_pkl'])
# Check the error if saving an object with other parameters
with open(f.name, 'rb+') as ff:
assert_raises(ValueError, add_to_dump, *[mlp2, ff, 'mlp2'],
**dict(parameters=[mlp2.children[0].W,
mlp2.children[1].W]))
# Check the warning if adding to a dump with no parameters
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
with open(f.name, 'rb+') as ff:
assert_raises(ValueError, add_to_dump, *[mlp2, ff, 'mlp2'],
**dict(parameters=[mlp2.children[0].W,
mlp2.children[1].W]))
def test_serialization():
# Create a simple brick with two parameters
mlp = MLP(activations=[None, None],
dims=[10, 10, 10],
weights_init=Constant(1.),
use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Check the data using numpy.load
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
assert_allclose(numpy_data['mlp-linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['mlp-linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled
mlp = load(f.name)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that only parameters are saved as NPY files
mlp.random_data = numpy.random.rand(10)
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
# Ensure that parameters can be loaded with correct names
parameter_values = load_parameter_values(f.name)
assert set(parameter_values.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
# Ensure that duplicate names are dealt with
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear.W', 'mlp-linear.W_2', 'pkl'])
# Ensure warnings are raised when __main__ namespace objects are dumped
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
def test_serialization():
# Create a simple brick with two parameters
mlp = MLP(activations=[None, None], dims=[10, 10, 10],
weights_init=Constant(1.), use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Check the data using numpy.load
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
assert_allclose(numpy_data['mlp-linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['mlp-linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled
mlp = load(f.name)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that only parameters are saved as NPY files
mlp.random_data = numpy.random.rand(10)
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear_0.W', 'mlp-linear_1.W', 'pkl'])
# Ensure that parameters can be loaded with correct names
parameter_values = load_parameter_values(f.name)
assert set(parameter_values.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
# Ensure that duplicate names are dealt with
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f)
numpy_data = numpy.load(f.name)
assert set(numpy_data.keys()) == \
set(['mlp-linear.W', 'mlp-linear.W_2', 'pkl'])
# Ensure warnings are raised when __main__ namespace objects are dumped
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
def test_serialization():
# Create a simple MLP to dump.
mlp = MLP(activations=[None, None],
dims=[10, 10, 10],
weights_init=Constant(1.),
use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Ensure warnings are raised when __main__ namespace objects are dumped.
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp.foo, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
# Check the parameters.
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
assert_allclose(numpy_data['/mlp/linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['/mlp/linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled.
with open(f.name, 'rb') as ff:
mlp = load(ff)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that duplicate names are dealt with.
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear.W', '/mlp/linear.W_2'])
# Check when we don't dump the main object.
with NamedTemporaryFile(delete=False) as f:
dump(None, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with tarfile.open(f.name, 'r') as tarball:
assert set(tarball.getnames()) == set(['_parameters'])
def test_serialization():
# Create a simple MLP to dump.
mlp = MLP(activations=[None, None], dims=[10, 10, 10],
weights_init=Constant(1.), use_bias=False)
mlp.initialize()
W = mlp.linear_transformations[1].W
W.set_value(W.get_value() * 2)
# Ensure warnings are raised when __main__ namespace objects are dumped.
foo.__module__ = '__main__'
import __main__
__main__.__dict__['foo'] = foo
mlp.foo = foo
with NamedTemporaryFile(delete=False) as f:
with warnings.catch_warnings(record=True) as w:
dump(mlp.foo, f)
assert len(w) == 1
assert '__main__' in str(w[-1].message)
# Check the parameters.
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear_0.W', '/mlp/linear_1.W'])
assert_allclose(numpy_data['/mlp/linear_0.W'], numpy.ones((10, 10)))
assert numpy_data['/mlp/linear_0.W'].dtype == theano.config.floatX
# Ensure that it can be unpickled.
with open(f.name, 'rb') as ff:
mlp = load(ff)
assert_allclose(mlp.linear_transformations[1].W.get_value(),
numpy.ones((10, 10)) * 2)
# Ensure that duplicate names are dealt with.
for child in mlp.children:
child.name = 'linear'
with NamedTemporaryFile(delete=False) as f:
dump(mlp, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with open(f.name, 'rb') as ff:
numpy_data = load_parameters(ff)
assert set(numpy_data.keys()) == \
set(['/mlp/linear.W', '/mlp/linear.W_2'])
# Check when we don't dump the main object.
with NamedTemporaryFile(delete=False) as f:
dump(None, f, parameters=[mlp.children[0].W, mlp.children[1].W])
with tarfile.open(f.name, 'r') as tarball:
assert set(tarball.getnames()) == set(['_parameters'])
def test_load_log():
log = TrainingLog()
log[0]["channel0"] = 0
# test simple TrainingLog pickles
with tempfile.NamedTemporaryFile() as f:
dump(log, f)
f.flush()
log2 = plot.load_log(f.name)
assert log2[0]["channel0"] == 0
# test MainLoop pickles
main_loop = MainLoop(model=None, data_stream=None, algorithm=None, log=log)
with tempfile.NamedTemporaryFile() as f:
dump(main_loop, f)
f.flush()
log2 = plot.load_log(f.name)
assert log2[0]["channel0"] == 0
def test_load_log():
log = TrainingLog()
log[0]['channel0'] = 0
# test simple TrainingLog pickles
with tempfile.NamedTemporaryFile() as f:
dump(log, f)
f.flush()
log2 = plot.load_log(f.name)
assert log2[0]['channel0'] == 0
# test MainLoop pickles
main_loop = MainLoop(model=None, data_stream=None, algorithm=None, log=log)
with tempfile.NamedTemporaryFile() as f:
dump(main_loop, f)
f.flush()
log2 = plot.load_log(f.name)
assert log2[0]['channel0'] == 0
def test_pickle_log():
log1 = TrainingLog()
dump(log1, "log1.pkl")
log2 = load("log1.pkl")
dump(log2, "log2.pkl")
load("log2.pkl") # loading an unresumed log works
log2.resume()
dump(log2, "log3.pkl")
load("log3.pkl") # loading a resumed log does not work
def test_pickle_log():
log1 = TrainingLog()
with open('log1.tar', 'wb') as f:
dump(log1, f)
with open('log1.tar', 'rb') as f:
log2 = load(f)
with open('log2.tar', 'wb') as f:
dump(log2, f)
with open('log2.tar', 'rb') as f:
load(f) # loading an unresumed log works
log2.resume()
with open('log3.tar', 'wb') as f:
dump(log2, f)
with open('log3.tar', 'rb') as f:
load(f) # loading a resumed log does not work
os.remove('log1.tar')
os.remove('log2.tar')
os.remove('log3.tar')
DataStream(
mnist,
iteration_scheme=ShuffledScheme(mnist.num_examples, batch_size)
),
which_sources=sources
)
# import ipdb; ipdb.set_trace()
test_stream = Flatten(
DataStream(
mnist_test,
iteration_scheme=ShuffledScheme(mnist_test.num_examples, batch_size)
),
which_sources=sources
)
"Print data loaded"
if train:
cost = create_network()
model, algorithm, extensions = prepare_opti(cost, test_stream)
main_loop = MainLoop(
algorithm=algorithm,
data_stream=training_stream,
model=model,
extensions=extensions
)
main_loop.run()
dump(main_loop.model, open('pixelvae.pkl', 'w'))
else:
model = load(open('pixelvae.pkl', 'r'))
def train(save_to, num_epochs, feature_maps=None, mlp_hiddens=None,
conv_sizes=None, pool_sizes=None, batch_size=500):
# Initialize the training set
train = CIFAR10(("train",))
train_stream = DataStream.default_stream(
train, iteration_scheme=ShuffledScheme(
train.num_examples, batch_size))
test = CIFAR10(("test",))
test_stream = DataStream.default_stream(
test,
iteration_scheme=ShuffledScheme(
test.num_examples, batch_size))
# ConvMLP Parameters
image_size = (32, 32)
num_channels = 3
num_conv = 3 # Number of Convolutional Layers
if feature_maps is None:
feature_maps = [20, 30, 30]
if not len(feature_maps) == num_conv:
raise ValueError('Must specify more feature maps')
if conv_sizes is None:
conv_sizes = [5] * num_conv
if pool_sizes is None:
pool_sizes = [2] * num_conv
if mlp_hiddens is None:
mlp_hiddens = [500]
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 = ConvMLP(conv_activations, num_channels, 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()
for i in range(num_conv):
convnet.layers[i].weights_init = Uniform(width=.2)
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])
# Apply Dropout to outputs of rectifiers
from blocks.roles import OUTPUT
vs = VariableFilter(roles=[OUTPUT])(cg.variables)
vs1 = [v for v in vs if v.name.startswith('rectifier')]
vs1 = vs1[0: -2] # Only first two layers
cg = apply_dropout(cg, vs1, 0.5)
# Train with simple SGD
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=AdaDelta())
# `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],
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,
train_stream,
model=model,
extensions=extensions)
main_loop.run()
classifier_fn = 'convmlp_cifar10.zip'
with open(classifier_fn, 'w') as f:
dump(convnet, f)
elif dataset == "cifar10":
data = CIFAR10(("train",))
data_test = CIFAR10(("test",))
training_stream = DataStream(
data,
iteration_scheme=ShuffledScheme(data.num_examples, batch_size)
)
test_stream = DataStream(
data_test,
iteration_scheme=ShuffledScheme(data_test.num_examples, batch_size)
)
logger.info("Dataset: {} loaded".format(dataset))
if train:
cost, cost_bits_dim = create_network()
model, algorithm, extensions = prepare_opti(cost, test_stream, cost_bits_dim)
# import ipdb; ipdb.set_trace()
main_loop = MainLoop(
algorithm=algorithm,
data_stream=training_stream,
model=model,
extensions=extensions
)
main_loop.run()
with open(path+'/'+'pixelcnn.pkl', 'w') as f:
dump(main_loop.model, f)
model = main_loop.model
else:
model = load(open('pixelcnn_cifar10_2016-07-19/pixelcnn_cifar10_epoch_165.pkl', 'r'))
elif dataset == "cifar10":
data = CIFAR10(("train", ))
data_test = CIFAR10(("test", ))
else:
pass # Add CIFAR 10
training_stream = DataStream(data,
iteration_scheme=ShuffledScheme(
data.num_examples, batch_size))
test_stream = DataStream(data_test,
iteration_scheme=ShuffledScheme(
data_test.num_examples, batch_size))
logger.info("Dataset: {} loaded".format(dataset))
if train:
cost, cost_bits_dim = create_network()
model, algorithm, extensions = prepare_opti(cost, test_stream,
cost_bits_dim)
main_loop = MainLoop(algorithm=algorithm,
data_stream=training_stream,
model=model,
extensions=extensions)
main_loop.run()
with open(path + '/' + 'pixelcnn.pkl', 'w') as f:
dump(main_loop.model, f)
model = main_loop.model
else:
model = load(
open('pixelcnn_cifar10_2016-07-19/pixelcnn_cifar10_epoch_165.pkl',
'r'))
def test_vae():
activation = Rectifier()
full_weights_init = Orthogonal()
weights_init = full_weights_init
layers = [784, 400, 20]
encoder_layers = layers[:-1]
encoder_mlp = MLP([activation] * (len(encoder_layers)-1),
encoder_layers,
name="MLP_enc", biases_init=Constant(0.), weights_init=weights_init)
enc_dim = encoder_layers[-1]
z_dim = layers[-1]
#sampler = Qlinear(input_dim=enc_dim, output_dim=z_dim, biases_init=Constant(0.), weights_init=full_weights_init)
sampler = Qsampler(input_dim=enc_dim, output_dim=z_dim, biases_init=Constant(0.), weights_init=full_weights_init)
decoder_layers = layers[:] ## includes z_dim as first layer
decoder_layers.reverse()
decoder_mlp = MLP([activation] * (len(decoder_layers)-2) + [Rectifier()],
decoder_layers,
name="MLP_dec", biases_init=Constant(0.), weights_init=weights_init)
vae = VAEModel(encoder_mlp, sampler, decoder_mlp)
vae.initialize()
x = T.matrix('features')
batch_size = 124
x_recons, kl_terms = vae.reconstruct(x)
recons_term = BinaryCrossEntropy().apply(x, T.clip(x_recons, 1e-5, 1 - 1e-5))
recons_term.name = "recons_term"
cost = recons_term + kl_terms.mean()
cost.name = "cost"
cg = ComputationGraph(cost)
temp = cg.parameters
for t, i in zip(temp, range(len(temp))):
t.name = t.name+str(i)+"vae_mnist"
step_rule = RMSProp(0.001, 0.95)
train_set = MNIST('train')
train_set.sources = ("features", )
test_set = MNIST("test")
test_set.sources = ("features", )
data_stream = Flatten(DataStream.default_stream(
train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size)))
data_stream_monitoring = Flatten(DataStream.default_stream(
train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size)))
data_stream_test = Flatten(DataStream.default_stream(
test_set, iteration_scheme=SequentialScheme(test_set.num_examples, batch_size)))
algorithm = GradientDescent(cost=cost, params=cg.parameters,
step_rule=step_rule)
monitor_train = TrainingDataMonitoring(
variables=[cost], prefix="train", every_n_batches=10)
monitor_valid = DataStreamMonitoring(
variables=[cost], data_stream=data_stream_test, prefix="valid", every_n_batches=10)
# drawing_samples = ImagesSamplesSave("../data_mnist", vae, (28, 28), every_n_epochs=1)
extensions = [ monitor_train,
monitor_valid,
FinishAfter(after_n_batches=1500),
Printing(every_n_batches=10)
]
main_loop = MainLoop(data_stream=data_stream,
algorithm=algorithm, model = Model(cost),
extensions=extensions)
main_loop.run()
from blocks.serialization import dump
with closing(open('../data_mnist/model_0', 'w')) as f:
dump(vae, f)
def do(self, which_callback, *args) :
model = self.main_loop.model
f = open(self.name + '_epoch_' +
str(self.main_loop.log.status['epochs_done']) + '.pkl', 'w')
dump(model, f)
f.close()
def maxout_mnist_test():
# if it is working
# do a class
x = T.tensor4('features')
y = T.imatrix('targets')
batch_size = 128
# maxout convolutional layers
# layer0
filter_size = (8, 8)
activation = Maxout_(num_pieces=2).apply
pooling_size = 4
pooling_step = 2
pad = 0
image_size = (28, 28)
num_channels = 1
num_filters = 48
layer0 = ConvolutionalLayer(activation, filter_size, num_filters,
pooling_size=(pooling_size, pooling_size),
pooling_step=(pooling_step, pooling_step),
pad=pad,
image_size=image_size,
num_channels=num_channels,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01),
name="layer_0")
layer0.initialize()
num_filters = 48
filter_size = (8,8)
pooling_size = 4
pooling_step = 2
pad = 3
image_size = (layer0.get_dim('output')[1],
layer0.get_dim('output')[2])
num_channels = layer0.get_dim('output')[0]
layer1 = ConvolutionalLayer(activation, filter_size, num_filters,
pooling_size=(pooling_size, pooling_size),
pooling_step=(pooling_step, pooling_step),
pad=pad,
image_size=image_size,
num_channels=num_channels,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01),
name="layer_1")
layer1.initialize()
num_filters = 24
filter_size=(5,5)
pooling_size = 2
pooling_step = 2
pad = 3
activation = Maxout_(num_pieces=4).apply
image_size = (layer1.get_dim('output')[1],
layer1.get_dim('output')[2])
num_channels = layer1.get_dim('output')[0]
layer2 = ConvolutionalLayer(activation, filter_size, num_filters,
pooling_size=(pooling_size, pooling_size),
pooling_step=(pooling_step, pooling_step),
pad = pad,
image_size=image_size,
num_channels=num_channels,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01),
name="layer_2")
layer2.initialize()
conv_layers = [layer0, layer1, layer2]
output_conv = x
for layer in conv_layers :
output_conv = layer.apply(output_conv)
output_conv = Flattener().apply(output_conv)
mlp_layer = Linear(54, 10,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01), name="layer_5")
mlp_layer.initialize()
output_mlp = mlp_layer.apply(output_conv)
params, names = build_params(conv_layers, [mlp_layer])
cost = Softmax().categorical_cross_entropy(y.flatten(), output_mlp)
cost.name = 'cost'
cg_ = ComputationGraph(cost)
weights = VariableFilter(roles=[WEIGHT])(cg_.variables)
cost = cost + 0.001*sum([sum(p**2) for p in weights])
cg = ComputationGraph(cost)
error_rate = errors(output_mlp, y)
error_rate.name = 'error'
# training
step_rule = RMSProp(0.01, 0.9)
#step_rule = Momentum(0.2, 0.9)
train_set = MNIST('train')
test_set = MNIST("test")
data_stream = DataStream.default_stream(
train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size))
data_stream_monitoring = DataStream.default_stream(
train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size))
data_stream_test =DataStream.default_stream(
test_set, iteration_scheme=SequentialScheme(test_set.num_examples, batch_size))
algorithm = GradientDescent(cost=cost, params=cg.parameters,
step_rule=step_rule)
monitor_train = DataStreamMonitoring(
variables=[cost, error_rate], data_stream=data_stream_monitoring, prefix="train")
monitor_valid = DataStreamMonitoring(
variables=[cost, error_rate], data_stream=data_stream_test, prefix="test")
extensions = [ monitor_train,
monitor_valid,
FinishAfter(after_n_epochs=50),
Printing(every_n_epochs=1)
]
main_loop = MainLoop(data_stream=data_stream,
algorithm=algorithm, model = Model(cost),
extensions=extensions)
main_loop.run()
from blocks.serialization import dump
with closing(open('../data_mnist/maxout', 'w')) as f:
dump(vae, f)