def _build_trainer(nb_epochs):
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(nb_epochs))
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
use_mask_as_input=use_mask_as_input)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize(initer.UniformInitializer(random_seed=1234))
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size)
trainer = Trainer(optimizer, batch_scheduler)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
nll = views.LossView(
loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(
validset, batch_size=len(validset), keep_mask=True))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}",
nll.mean, nll.stderror))
trainer.append_task(stopping_criteria.MaxEpochStopping(nb_epochs))
return trainer, nll, logger
def main():
parser = build_argparser()
args = parser.parse_args()
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(args.experiment, "hyperparams.json"))
except:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(args.experiment, '..', "hyperparams.json"))
model = load_model(args.experiment)
print(str(model))
with Timer("Loading dataset"):
trainset, validset, testset = datasets.load(hyperparams['dataset'],
keep_on_cpu=True)
print(" (data: {:,}; {:,}; {:,}) ".format(len(trainset), len(validset),
len(testset)),
end="")
# Result files.
result_file = pjoin(args.experiment, "results_estimate.json")
if not os.path.isfile(result_file) or args.force:
with Timer("Evaluating NLL estimate"):
results = {"seed": args.seed}
results['trainset'] = estimate_NLL(model,
trainset,
seed=args.seed,
batch_size=args.batch_size)
results['validset'] = estimate_NLL(model,
validset,
seed=args.seed,
batch_size=args.batch_size)
results['testset'] = estimate_NLL(model,
testset,
seed=args.seed,
batch_size=args.batch_size)
utils.save_dict_to_json_file(result_file,
{"NLL_estimate": results})
else:
print("Loading saved results... (use --force to re-run evaluation)")
results = utils.load_dict_from_json_file(result_file)['NLL_estimate']
for dataset in ['trainset', 'validset', 'testset']:
print("NLL estimate on {}: {:.2f} ± {:.2f}".format(
dataset, results[dataset]['mean'], results[dataset]['stderror']))
def main():
parser = buildArgsParser()
args = parser.parse_args()
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(pjoin(args.experiment, "hyperparams.json"))
except:
hyperparams = smartutils.load_dict_from_json_file(pjoin(args.experiment, '..', "hyperparams.json"))
model = load_model(args.experiment)
print(str(model))
with Timer("Generating {} samples from Conv Deep NADE".format(args.count)):
sample = model.build_sampling_function(seed=args.seed)
samples, probs = sample(args.count, return_probs=True, ordering_seed=args.seed)
if args.out is not None:
outfile = pjoin(args.experiment, args.out)
with Timer("Saving {0} samples to '{1}'".format(args.count, outfile)):
np.save(outfile, samples)
if args.view:
import pylab as plt
from convnade import vizu
if hyperparams["dataset"] == "binarized_mnist":
image_shape = (28, 28)
else:
raise ValueError("Unknown dataset: {0}".format(hyperparams["dataset"]))
plt.figure()
data = vizu.concatenate_images(samples, shape=image_shape, border_size=1, clim=(0, 1))
plt.imshow(data, cmap=plt.cm.gray, interpolation='nearest')
plt.title("Samples")
plt.figure()
data = vizu.concatenate_images(probs, shape=image_shape, border_size=1, clim=(0, 1))
plt.imshow(data, cmap=plt.cm.gray, interpolation='nearest')
plt.title("Probs")
plt.show()
def load_model(experiment_path):
with Timer("Loading model"):
from convnade import DeepConvNadeUsingLasagne, DeepConvNadeWithResidualUsingLasagne
from convnade import DeepConvNADE, DeepConvNADEWithResidual
for model_class in [DeepConvNadeUsingLasagne, DeepConvNadeWithResidualUsingLasagne, DeepConvNADE, DeepConvNADEWithResidual]:
try:
model = model_class.create(experiment_path)
return model
except Exception as e:
print (e)
pass
raise NameError("No model found!")
return None
def main():
parser = build_argparser()
args = parser.parse_args()
# Get experiment folder
experiment_path = args.name
if not os.path.isdir(experiment_path):
# If not a directory, it must be the name of the experiment.
experiment_path = pjoin(".", "experiments", args.name)
if not os.path.isdir(experiment_path):
parser.error('Cannot find experiment: {0}!'.format(args.name))
if not os.path.isdir(pjoin(experiment_path, "evaluation")):
parser.error('Cannot find evaluations for experiment: {0}!'.format(
experiment_path))
results_file = pjoin(experiment_path, "results.json")
if not os.path.isfile(results_file) or args.force:
with Timer("Merging NLL evaluations"):
results = merge_NLL_evaluations(
evaluation_dir=pjoin(experiment_path, "evaluation"))
smartutils.save_dict_to_json_file(results_file, {"NLL": results})
else:
print("Loading saved losses... (use --force to re-run evaluation)")
results = smartutils.load_dict_from_json_file(results_file)["NLL"]
nb_orderings = results['nb_orderings']
for dataset in ['validset', 'testset']:
print("NLL estimate on {} ({} orderings): {:.2f} ± {:.2f}".format(
dataset, nb_orderings, results[dataset]['mean'],
results[dataset]['stderror']))
if results[dataset]['incomplete']:
print(
"** Warning **: {} evaluation is incomplete. Missing some orderings or batches."
.format(dataset))
def test_simple_convnade():
nb_kernels = 8
kernel_shape = (2, 2)
hidden_activation = "sigmoid"
consider_mask_as_channel = True
batch_size = 1024
ordering_seed = 1234
max_epoch = 3
nb_orderings = 1
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(max_epoch))
with Timer("Loading/processing binarized MNIST"):
trainset, validset, testset = load_binarized_mnist()
# Extract the center patch (4x4 pixels) of each image.
indices_to_keep = [
348, 349, 350, 351, 376, 377, 378, 379, 404, 405, 406, 407, 432,
433, 434, 435
]
trainset = Dataset(trainset.inputs.get_value()[:, indices_to_keep],
trainset.inputs.get_value()[:, indices_to_keep],
name="trainset")
validset = Dataset(validset.inputs.get_value()[:, indices_to_keep],
validset.inputs.get_value()[:, indices_to_keep],
name="validset")
testset = Dataset(testset.inputs.get_value()[:, indices_to_keep],
testset.inputs.get_value()[:, indices_to_keep],
name="testset")
image_shape = (4, 4)
nb_channels = 1
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
consider_mask_as_channel=True)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize() # By default, uniform initialization.
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size)
trainer = Trainer(optimizer, batch_scheduler)
trainer.append_task(stopping_criteria.MaxEpochStopping(max_epoch))
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
nll = views.LossView(
loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(
validset, batch_size=len(validset)))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}",
nll.mean, nll.stderror))
trainer.build_theano_graph()
with Timer("Training"):
trainer.train()
with Timer("Checking the probs for all possible inputs sum to 1"):
# rng = np.random.RandomState(ordering_seed)
D = np.prod(image_shape)
batch_scheduler = BatchSchedulerWithAutoregressiveMasks(
validset,
batch_size=len(validset),
batch_id=0,
ordering_id=0,
concatenate_mask=model.nb_channels == 2,
seed=42)
nll = views.LossView(
loss=NllUsingBinaryCrossEntropyWithAutoRegressiveMask(
model, validset, batch_scheduler.mod),
batch_scheduler=batch_scheduler)
nlls_xod_given_xoltd = nll.losses.view(Status())
nlls = np.sum(nlls_xod_given_xoltd.reshape(-1, len(validset)), axis=0)
nll_validset = np.mean(nlls)
print("Sum of NLL for validset:", nll_validset)
inputs = cartesian([[0, 1]] * int(D), dtype=np.float32)
dataset = ReconstructionDataset(inputs)
batch_scheduler = BatchSchedulerWithAutoregressiveMasks(
dataset,
batch_size=len(dataset),
batch_id=0,
ordering_id=0,
concatenate_mask=model.nb_channels == 2,
seed=42)
nll = views.LossView(
loss=NllUsingBinaryCrossEntropyWithAutoRegressiveMask(
model, dataset, batch_scheduler.mod),
batch_scheduler=batch_scheduler)
nlls_xod_given_xoltd = nll.losses.view(Status())
nlls = np.sum(nlls_xod_given_xoltd.reshape(-1, len(dataset)), axis=0)
p_x = np.exp(np.logaddexp.reduce(-nlls))
print("Sum of p(x) for all x:", p_x)
assert_almost_equal(p_x, 1., decimal=5)
def main():
parser = buildArgsParser()
args = parser.parse_args()
# Extract experiments hyperparameters
hyperparams = dict(vars(args))
# Remove hyperparams that should not be part of the hash
del hyperparams['max_epoch']
del hyperparams['keep']
del hyperparams['force']
del hyperparams['name']
# Get/generate experiment name
experiment_name = args.name
if experiment_name is None:
experiment_name = utils.generate_uid_from_string(repr(hyperparams))
# Create experiment folder
experiment_path = pjoin(".", "experiments", experiment_name)
resuming = False
if os.path.isdir(experiment_path) and not args.force:
resuming = True
print("### Resuming experiment ({0}). ###\n".format(experiment_name))
# Check if provided hyperparams match those in the experiment folder
hyperparams_loaded = utils.load_dict_from_json_file(pjoin(experiment_path, "hyperparams.json"))
if hyperparams != hyperparams_loaded:
print("{\n" + "\n".join(["{}: {}".format(k, hyperparams[k]) for k in sorted(hyperparams.keys())]) + "\n}")
print("{\n" + "\n".join(["{}: {}".format(k, hyperparams_loaded[k]) for k in sorted(hyperparams_loaded.keys())]) + "\n}")
print("The arguments provided are different than the one saved. Use --force if you are certain.\nQuitting.")
sys.exit(1)
else:
if os.path.isdir(experiment_path):
shutil.rmtree(experiment_path)
os.makedirs(experiment_path)
utils.save_dict_to_json_file(pjoin(experiment_path, "hyperparams.json"), hyperparams)
with Timer("Loading dataset"):
trainset, validset, testset = datasets.load(args.dataset)
image_shape = (28, 28)
nb_channels = 1 + (args.use_mask_as_input is True)
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(trainset, args.batch_size,
use_mask_as_input=args.use_mask_as_input,
seed=args.ordering_seed)
print("{} updates per epoch.".format(len(batch_scheduler)))
with Timer("Building model"):
if args.use_lasagne:
if args.with_residual:
model = DeepConvNadeWithResidualUsingLasagne(image_shape=image_shape,
nb_channels=nb_channels,
convnet_blueprint=args.convnet_blueprint,
fullnet_blueprint=args.fullnet_blueprint,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input)
else:
model = DeepConvNadeUsingLasagne(image_shape=image_shape,
nb_channels=nb_channels,
convnet_blueprint=args.convnet_blueprint,
fullnet_blueprint=args.fullnet_blueprint,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input,
use_batch_norm=args.batch_norm)
elif args.with_residual:
model = DeepConvNADEWithResidual(image_shape=image_shape,
nb_channels=nb_channels,
convnet_blueprint=args.convnet_blueprint,
fullnet_blueprint=args.fullnet_blueprint,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input)
else:
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input)
if args.blueprints_seed is not None:
convnet_blueprint, fullnet_blueprint = generate_blueprints(args.blueprint_seed, image_shape[0])
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
else:
if args.convnet_blueprint is not None:
builder.build_convnet_from_blueprint(args.convnet_blueprint)
if args.fullnet_blueprint is not None:
builder.build_fullnet_from_blueprint(args.fullnet_blueprint)
model = builder.build()
# print(str(model.convnet))
# print(str(model.fullnet))
model.initialize(weigths_initializer_factory(args.weights_initialization,
seed=args.initialization_seed))
print(str(model))
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(model, trainset)
optimizer = optimizer_factory(hyperparams, loss)
with Timer("Building trainer"):
trainer = Trainer(optimizer, batch_scheduler)
if args.max_epoch is not None:
trainer.append_task(stopping_criteria.MaxEpochStopping(args.max_epoch))
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
model.deterministic = True # For batch normalization, see https://github.com/Lasagne/Lasagne/blob/master/lasagne/layers/normalization.py#L198
nll = views.LossView(loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(validset, batch_size=0.1*len(validset),
use_mask_as_input=args.use_mask_as_input,
keep_mask=True,
seed=args.ordering_seed+1))
# trainer.append_task(tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}", nll.mean, nll.stderror, each_k_update=100))
trainer.append_task(tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}", nll.mean, nll.stderror))
# direction_norm = views.MonitorVariable(T.sqrt(sum(map(lambda d: T.sqr(d).sum(), loss.gradients.values()))))
# trainer.append_task(tasks.Print("||d|| : {0:.4f}", direction_norm, each_k_update=50))
# Save training progression
def save_model(*args):
trainer.save(experiment_path)
trainer.append_task(stopping_criteria.EarlyStopping(nll.mean, lookahead=args.lookahead, eps=args.lookahead_eps, callback=save_model))
trainer.build_theano_graph()
if resuming:
with Timer("Loading"):
trainer.load(experiment_path)
with Timer("Training"):
trainer.train()
trainer.save(experiment_path)
model.save(experiment_path)
def test_new_fprop_matches_old_fprop():
nb_kernels = 8
kernel_shape = (2, 2)
hidden_activation = "sigmoid"
use_mask_as_input = True
batch_size = 1024
ordering_seed = 1234
max_epoch = 10
nb_orderings = 1
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(max_epoch))
with Timer("Loading/processing binarized MNIST"):
trainset, validset, testset = load_binarized_mnist()
# Extract the center patch (4x4 pixels) of each image.
indices_to_keep = [
348, 349, 350, 351, 376, 377, 378, 379, 404, 405, 406, 407, 432,
433, 434, 435
]
trainset = Dataset(trainset.inputs.get_value()[:, indices_to_keep],
trainset.inputs.get_value()[:, indices_to_keep],
name="trainset")
validset = Dataset(validset.inputs.get_value()[:, indices_to_keep],
validset.inputs.get_value()[:, indices_to_keep],
name="validset")
testset = Dataset(testset.inputs.get_value()[:, indices_to_keep],
testset.inputs.get_value()[:, indices_to_keep],
name="testset")
image_shape = (4, 4)
nb_channels = 1 + (use_mask_as_input is True)
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
use_mask_as_input=use_mask_as_input)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize() # By default, uniform initialization.
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size, use_mask_as_input=use_mask_as_input)
trainer = Trainer(optimizer, batch_scheduler)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
trainer.append_task(stopping_criteria.MaxEpochStopping(max_epoch))
trainer.build_theano_graph()
with Timer("Training"):
trainer.train()
mask_o_lt_d = batch_scheduler._shared_batch_mask
fprop_output, fprop_pre_output = model.fprop(
trainset.inputs, mask_o_lt_d, return_output_preactivation=True)
model_output = model.get_output(
T.concatenate([trainset.inputs * mask_o_lt_d, mask_o_lt_d], axis=1))
assert_array_equal(model_output.eval(), fprop_pre_output.eval())
print(np.sum(abs(model_output.eval() - fprop_pre_output.eval())))
def test_save_load_convnade():
nb_kernels = 8
kernel_shape = (2, 2)
hidden_activation = "hinge"
use_mask_as_input = True
batch_size = 1024
ordering_seed = 1234
max_epoch = 5
nb_orderings = 1
with Timer("Loading/processing binarized MNIST"):
trainset, validset, testset = load_binarized_mnist()
# Extract the center patch (4x4 pixels) of each image.
indices_to_keep = [
348, 349, 350, 351, 376, 377, 378, 379, 404, 405, 406, 407, 432,
433, 434, 435
]
trainset = Dataset(trainset.inputs.get_value()[:, indices_to_keep],
trainset.inputs.get_value()[:, indices_to_keep],
name="trainset")
validset = Dataset(validset.inputs.get_value()[:, indices_to_keep],
validset.inputs.get_value()[:, indices_to_keep],
name="validset")
testset = Dataset(testset.inputs.get_value()[:, indices_to_keep],
testset.inputs.get_value()[:, indices_to_keep],
name="testset")
image_shape = (4, 4)
nb_channels = 1
# Nested function to build a trainer.
def _build_trainer(nb_epochs):
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(nb_epochs))
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
use_mask_as_input=use_mask_as_input)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize(initer.UniformInitializer(random_seed=1234))
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size)
trainer = Trainer(optimizer, batch_scheduler)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
nll = views.LossView(
loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(
validset, batch_size=len(validset), keep_mask=True))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}",
nll.mean, nll.stderror))
trainer.append_task(stopping_criteria.MaxEpochStopping(nb_epochs))
return trainer, nll, logger
trainer1, nll1, logger1 = _build_trainer(nb_epochs=10)
with Timer("Compiling training graph"):
trainer1.build_theano_graph()
with Timer("Training"):
trainer1.train()
trainer2a, nll2a, logger2a = _build_trainer(5)
with Timer("Compiling training graph"):
trainer2a.build_theano_graph()
with Timer("Training"):
trainer2a.train()
# Save model halfway during training and resume it.
with tempfile.TemporaryDirectory() as experiment_dir:
with Timer("Saving"):
# Save current state of the model (i.e. after 5 epochs).
trainer2a.save(experiment_dir)
with Timer("Loading"):
# Load previous state from which training will resume.
trainer2b, nll2b, logger2b = _build_trainer(10)
trainer2b.load(experiment_dir)
# Check we correctly reloaded the model.
assert_equal(len(trainer2a._optimizer.loss.model.parameters),
len(trainer2b._optimizer.loss.model.parameters))
for param1, param2 in zip(
trainer2a._optimizer.loss.model.parameters,
trainer2b._optimizer.loss.model.parameters):
assert_array_equal(param1.get_value(),
param2.get_value(),
err_msg=param1.name)
with Timer("Compiling training graph"):
trainer2b.build_theano_graph()
with Timer("Training"):
trainer2b.train()
# Check we correctly resumed training.
assert_equal(len(trainer1._optimizer.loss.model.parameters),
len(trainer2b._optimizer.loss.model.parameters))
for param1, param2 in zip(trainer1._optimizer.loss.model.parameters,
trainer2b._optimizer.loss.model.parameters):
# I tested it, they are exactly equal when using float64.
assert_array_almost_equal(param1.get_value(),
param2.get_value(),
err_msg=param1.name)
# I tested it, they are exactly equal when using float64.
assert_array_almost_equal(nll1.mean.view(trainer1.status),
nll2b.mean.view(trainer2b.status))
assert_array_almost_equal(nll1.stderror.view(trainer1.status),
nll2b.stderror.view(trainer2b.status))
# I tested it, they are exactly equal when using float64.
assert_array_almost_equal(
logger1.get_variable_history(0),
logger2a.get_variable_history(0) + logger2b.get_variable_history(0))
assert_array_almost_equal(
logger1.get_variable_history(1),
logger2a.get_variable_history(1) + logger2b.get_variable_history(1))
def test_simple_convnade():
nb_kernels = 8
kernel_shape = (2, 2)
hidden_activation = "sigmoid"
use_mask_as_input = True
batch_size = 1024
ordering_seed = 1234
max_epoch = 3
nb_orderings = 1
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(max_epoch))
with Timer("Loading/processing binarized MNIST"):
trainset, validset, testset = load_binarized_mnist()
# Extract the center patch (4x4 pixels) of each image.
indices_to_keep = [
348, 349, 350, 351, 376, 377, 378, 379, 404, 405, 406, 407, 432,
433, 434, 435
]
trainset = Dataset(trainset.inputs.get_value()[:, indices_to_keep],
trainset.inputs.get_value()[:, indices_to_keep],
name="trainset")
validset = Dataset(validset.inputs.get_value()[:, indices_to_keep],
validset.inputs.get_value()[:, indices_to_keep],
name="validset")
testset = Dataset(testset.inputs.get_value()[:, indices_to_keep],
testset.inputs.get_value()[:, indices_to_keep],
name="testset")
image_shape = (4, 4)
nb_channels = 1
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
use_mask_as_input=use_mask_as_input)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize() # By default, uniform initialization.
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size)
trainer = Trainer(optimizer, batch_scheduler)
trainer.append_task(stopping_criteria.MaxEpochStopping(max_epoch))
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
nll = views.LossView(
loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(
validset, batch_size=len(validset)))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}",
nll.mean, nll.stderror))
trainer.build_theano_graph()
with Timer("Training"):
trainer.train()
with Timer("Checking the probs for all possible inputs sum to 1"):
rng = np.random.RandomState(ordering_seed)
D = np.prod(image_shape)
inputs = cartesian([[0, 1]] * int(D), dtype=np.float32)
ordering = np.arange(D, dtype=np.int32)
rng.shuffle(ordering)
symb_input = T.vector("input")
symb_input.tag.test_value = inputs[-len(inputs) // 4]
symb_ordering = T.ivector("ordering")
symb_ordering.tag.test_value = ordering
nll_of_x_given_o = theano.function([symb_input, symb_ordering],
model.nll_of_x_given_o(
symb_input, symb_ordering),
name="nll_of_x_given_o")
#theano.printing.pydotprint(nll_of_x_given_o, '{0}_nll_of_x_given_o_{1}'.format(model.__class__.__name__, theano.config.device), with_ids=True)
for i in range(nb_orderings):
print("Ordering:", ordering)
ordering = np.arange(D, dtype=np.int32)
rng.shuffle(ordering)
nlls = []
for no, input in enumerate(inputs):
print("{}/{}".format(no, len(inputs)), end='\r')
nlls.append(nll_of_x_given_o(input, ordering))
print("{}/{} Done".format(len(inputs), len(inputs)))
p_x = np.exp(np.logaddexp.reduce(-np.array(nlls)))
print("Sum of p(x) for all x:", p_x)
assert_almost_equal(p_x, 1., decimal=5)