def check_dataset(name):
try:
x_dim, data_train, data_valid, data_test = datasets.get_streams(name, batch_size=10, small_batch_size=10)
except IOError as e:
raise SkipTest
for ds in (data_train, data_valid, data_test):
features, = next(ds.get_epoch_iterator())
features = features.reshape([10, -1])
assert features.shape == (10, x_dim)
def main(args):
"""Run experiment. """
lr_tag = float_tag(args.learning_rate)
x_dim, train_stream, valid_stream, test_stream = datasets.get_streams(args.data, args.batch_size)
#------------------------------------------------------------
# Setup model
deterministic_act = Tanh
deterministic_size = 1.
if args.method == 'vae':
sizes_tag = args.layer_spec.replace(",", "-")
layer_sizes = [int(i) for i in args.layer_spec.split(",")]
layer_sizes, z_dim = layer_sizes[:-1], layer_sizes[-1]
name = "%s-%s-%s-lr%s-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.n_samples, sizes_tag)
if args.activation == "tanh":
hidden_act = Tanh()
elif args.activation == "logistic":
hidden_act = Logistic()
elif args.activation == "relu":
hidden_act = Rectifier()
else:
raise "Unknown hidden nonlinearity %s" % args.hidden_act
model = VAE(x_dim=x_dim, hidden_layers=layer_sizes, hidden_act=hidden_act, z_dim=z_dim,
batch_norm=args.batch_normalization)
model.initialize()
elif args.method == 'rws':
sizes_tag = args.layer_spec.replace(",", "-")
name = "%s-%s-%s-lr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(
args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act, deterministic_size)
model = ReweightedWakeSleep(
p_layers,
q_layers,
)
model.initialize()
elif args.method == 'bihm':
sizes_tag = args.layer_spec.replace(",", "-")
name = "%s-%s-%s-lr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(
args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act, deterministic_size)
model = BiHM(
p_layers,
q_layers,
l1reg=args.l1reg,
l2reg=args.l2reg,
)
model.initialize()
elif args.method == 'continue':
import cPickle as pickle
from os.path import basename, splitext
with open(args.model_file, 'rb') as f:
m = pickle.load(f)
if isinstance(m, MainLoop):
m = m.model
model = m.get_top_bricks()[0]
while len(model.parents) > 0:
model = model.parents[0]
assert isinstance(model, (BiHM, ReweightedWakeSleep, VAE))
mname, _, _ = basename(args.model_file).rpartition("_model.pkl")
name = "%s-cont-%s-lr%s-spl%s" % (mname, args.name, lr_tag, args.n_samples)
else:
raise ValueError("Unknown training method '%s'" % args.method)
#------------------------------------------------------------
x = tensor.matrix('features')
#------------------------------------------------------------
# Testset monitoring
train_monitors = []
valid_monitors = []
test_monitors = []
for s in [1, 10, 100, 1000,]:
log_p, log_ph = model.log_likelihood(x, s)
log_p = -log_p.mean()
log_ph = -log_ph.mean()
log_p.name = "log_p_%d" % s
log_ph.name = "log_ph_%d" % s
#valid_monitors += [log_p, log_ph]
test_monitors += [log_p, log_ph]
#------------------------------------------------------------
# Z estimation
#for s in [100000]:
# z2 = tensor.exp(model.estimate_log_z2(s)) / s
# z2.name = "z2_%d" % s
#
# valid_monitors += [z2]
# test_monitors += [z2]
#------------------------------------------------------------
# Gradient and training monitoring
if args.method in ['vae', 'dvae']:
log_p_bound = model.log_likelihood_bound(x, args.n_samples)
gradients = None
log_p_bound = -log_p_bound.mean()
log_p_bound.name = "log_p_bound"
cost = log_p_bound
train_monitors += [log_p_bound, named(model.kl_term.mean(), 'kl_term'), named(model.recons_term.mean(), 'recons_term')]
valid_monitors += [log_p_bound, named(model.kl_term.mean(), 'kl_term'), named(model.recons_term.mean(), 'recons_term')]
test_monitors += [log_p_bound, named(model.kl_term.mean(), 'kl_term'), named(model.recons_term.mean(), 'recons_term')]
else:
log_p, log_ph, gradients = model.get_gradients(x, args.n_samples)
log_p_bound = named( -model.log_p_bound.mean(), "log_p_bound")
log_p = named( -log_p.mean(), "log_p")
log_ph = named( -log_ph.mean(), "log_ph")
cost = log_p
train_monitors += [log_p_bound, log_p, log_ph]
valid_monitors += [log_p_bound, log_p, log_ph]
#------------------------------------------------------------
cg = ComputationGraph([cost])
if args.step_rule == "momentum":
step_rule = Momentum(args.learning_rate, 0.95)
elif args.step_rule == "rmsprop":
step_rule = RMSProp(args.learning_rate)
elif args.step_rule == "adam":
step_rule = Adam(args.learning_rate)
else:
raise "Unknown step_rule %s" % args.step_rule
parameters = cg.parameters
algorithm = GradientDescent(
cost=cost,
parameters=parameters,
gradients=gradients,
step_rule=CompositeRule([
step_rule,
])
)
#------------------------------------------------------------
train_monitors += [aggregation.mean(algorithm.total_gradient_norm),
aggregation.mean(algorithm.total_step_norm)]
#------------------------------------------------------------
# Live plotting?
plotting_extensions = []
if args.live_plotting:
plotting_extensions = [
PlotManager(
name,
[Plotter(channels=[
["valid_%s" % cost.name, "valid_log_p"],
["train_total_gradient_norm", "train_total_step_norm"]],
titles=[
"validation cost",
"norm of training gradient and step"
]),
DisplayImage([
WeightDisplay(
model.p_layers[0].mlp.linear_transformations[0].W,
n_weights=100, image_shape=(28, 28))]
#ImageDataStreamDisplay(test_stream, image_shape=(28,28))]
)]
)
]
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[Timing(),
ProgressBar(),
TrainingDataMonitoring(
train_monitors,
prefix="train",
after_epoch=False,
after_batch=True),
DataStreamMonitoring(
valid_monitors,
data_stream=valid_stream,
prefix="valid"),
DataStreamMonitoring(
test_monitors,
data_stream=test_stream,
prefix="test",
after_epoch=False,
after_training=True,
every_n_epochs=10),
TrackTheBest('valid_%s' % cost.name),
Checkpoint(name+".pkl", save_separately=['log', 'model']),
FinishIfNoImprovementAfter('valid_%s_best_so_far' % cost.name, epochs=args.patience),
FinishAfter(after_n_epochs=args.max_epochs),
Printing()] + plotting_extensions)
main_loop.run()
layer_kl = [
tensor.sum(lq - lp, axis=1) / n_samples
for lp, lq in zip(log_p[:], log_q[:])
]
do_kl = theano.function([x, n_samples], [log_px, total_kl] + layer_kl,
name="do_kl",
allow_input_downcast=True)
#----------------------------------------------------------------------
logger.info("Loading dataset...")
n_samples = args.nsamples
batch_size = max(1, 10000 // args.nsamples)
x_dim, stream_train, stream_valid, stream_test = datasets.get_streams(
args.data, batch_size)
stream = stream_test
log_px = np.array([])
total_kl = np.array([])
layer_kl = [np.array([]) for _ in xrange(n_layers)]
for batch in stream.get_epoch_iterator():
features = batch[0]
ret = do_kl(features, n_samples)
log_px_batch, total_kl_batch = ret[:2]
layer_kl_batch = ret[2:]
log_px = np.concatenate([log_px, log_px_batch])
total_kl = np.concatenate([total_kl, total_kl_batch])
for l, kl_batch in enumerate(layer_kl_batch):
layer_kl[l] = np.concatenate([layer_kl[l], kl_batch])
def main(args):
"""Run experiment. """
lr_tag = float_tag(args.learning_rate)
x_dim, train_stream, valid_stream, test_stream = datasets.get_streams(
args.data, args.batch_size)
#------------------------------------------------------------
# Setup model
deterministic_act = Tanh
deterministic_size = 1.
if args.method == 'vae':
sizes_tag = args.layer_spec.replace(",", "-")
layer_sizes = [int(i) for i in args.layer_spec.split(",")]
layer_sizes, z_dim = layer_sizes[:-1], layer_sizes[-1]
name = "%s-%s-%s-lr%s-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.n_samples, sizes_tag)
if args.activation == "tanh":
hidden_act = Tanh()
elif args.activation == "logistic":
hidden_act = Logistic()
elif args.activation == "relu":
hidden_act = Rectifier()
else:
raise "Unknown hidden nonlinearity %s" % args.hidden_act
model = VAE(x_dim=x_dim,
hidden_layers=layer_sizes,
hidden_act=hidden_act,
z_dim=z_dim,
batch_norm=args.batch_normalization)
model.initialize()
elif args.method == 'dvae':
sizes_tag = args.layer_spec.replace(",", "-")
layer_sizes = [int(i) for i in args.layer_spec.split(",")]
layer_sizes, z_dim = layer_sizes[:-1], layer_sizes[-1]
name = "%s-%s-%s-lr%s-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.n_samples, sizes_tag)
if args.activation == "tanh":
hidden_act = Tanh()
elif args.activation == "logistic":
hidden_act = Logistic()
elif args.activation == "relu":
hidden_act = Rectifier()
else:
raise "Unknown hidden nonlinearity %s" % args.hidden_act
model = DVAE(x_dim=x_dim,
hidden_layers=layer_sizes,
hidden_act=hidden_act,
z_dim=z_dim,
batch_norm=args.batch_normalization)
model.initialize()
elif args.method == 'rws':
sizes_tag = args.layer_spec.replace(",", "-")
qbase = "" if not args.no_qbaseline else "noqb-"
name = "%s-%s-%s-%slr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, qbase, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act,
deterministic_size)
model = ReweightedWakeSleep(
p_layers,
q_layers,
qbaseline=(not args.no_qbaseline),
)
model.initialize()
elif args.method == 'bihm-rws':
sizes_tag = args.layer_spec.replace(",", "-")
name = "%s-%s-%s-lr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act,
deterministic_size)
model = BiHM(
p_layers,
q_layers,
l1reg=args.l1reg,
l2reg=args.l2reg,
)
model.initialize()
elif args.method == 'continue':
import cPickle as pickle
from os.path import basename, splitext
with open(args.model_file, 'rb') as f:
m = pickle.load(f)
if isinstance(m, MainLoop):
m = m.model
model = m.get_top_bricks()[0]
while len(model.parents) > 0:
model = model.parents[0]
assert isinstance(model, (BiHM, ReweightedWakeSleep, VAE))
mname, _, _ = basename(args.model_file).rpartition("_model.pkl")
name = "%s-cont-%s-lr%s-spl%s" % (mname, args.name, lr_tag,
args.n_samples)
else:
raise ValueError("Unknown training method '%s'" % args.method)
#------------------------------------------------------------
x = tensor.matrix('features')
#------------------------------------------------------------
# Testset monitoring
train_monitors = []
valid_monitors = []
test_monitors = []
for s in [1, 10, 100, 1000]:
log_p, log_ph = model.log_likelihood(x, s)
log_p = -log_p.mean()
log_ph = -log_ph.mean()
log_p.name = "log_p_%d" % s
log_ph.name = "log_ph_%d" % s
#train_monitors += [log_p, log_ph]
#valid_monitors += [log_p, log_ph]
test_monitors += [log_p, log_ph]
#------------------------------------------------------------
# Z estimation
#for s in [100000]:
# z2 = tensor.exp(model.estimate_log_z2(s)) / s
# z2.name = "z2_%d" % s
#
# valid_monitors += [z2]
# test_monitors += [z2]
#------------------------------------------------------------
# Gradient and training monitoring
if args.method in ['vae', 'dvae']:
log_p_bound, gradients = model.get_gradients(x, args.n_samples)
log_p_bound = -log_p_bound.mean()
log_p_bound.name = "log_p_bound"
cost = log_p_bound
train_monitors += [
log_p_bound,
named(model.kl_term.mean(), 'kl_term'),
named(model.recons_term.mean(), 'recons_term')
]
valid_monitors += [
log_p_bound,
named(model.kl_term.mean(), 'kl_term'),
named(model.recons_term.mean(), 'recons_term')
]
test_monitors += [
log_p_bound,
named(model.kl_term.mean(), 'kl_term'),
named(model.recons_term.mean(), 'recons_term')
]
else:
log_p, log_ph, gradients = model.get_gradients(x, args.n_samples)
log_p = -log_p.mean()
log_ph = -log_ph.mean()
log_p.name = "log_p"
log_ph.name = "log_ph"
cost = log_ph
train_monitors += [log_p, log_ph]
valid_monitors += [log_p, log_ph]
#------------------------------------------------------------
# Detailed monitoring
"""
n_layers = len(p_layers)
log_px, w, log_p, log_q, samples = model.log_likelihood(x, n_samples)
exp_samples = []
for l in xrange(n_layers):
e = (w.dimshuffle(0, 1, 'x')*samples[l]).sum(axis=1)
e.name = "inference_h%d" % l
e.tag.aggregation_scheme = aggregation.TakeLast(e)
exp_samples.append(e)
s1 = samples[1]
sh1 = s1.shape
s1_ = s1.reshape([sh1[0]*sh1[1], sh1[2]])
s0, _ = model.p_layers[0].sample_expected(s1_)
s0 = s0.reshape([sh1[0], sh1[1], s0.shape[1]])
s0 = (w.dimshuffle(0, 1, 'x')*s0).sum(axis=1)
s0.name = "inference_h0^"
s0.tag.aggregation_scheme = aggregation.TakeLast(s0)
exp_samples.append(s0)
# Draw P-samples
p_samples, _, _ = model.sample_p(100)
#weights = model.importance_weights(samples)
#weights = weights / weights.sum()
for i, s in enumerate(p_samples):
s.name = "psamples_h%d" % i
s.tag.aggregation_scheme = aggregation.TakeLast(s)
#
samples = model.sample(100, oversample=100)
for i, s in enumerate(samples):
s.name = "samples_h%d" % i
s.tag.aggregation_scheme = aggregation.TakeLast(s)
"""
cg = ComputationGraph([cost])
#------------------------------------------------------------
if args.step_rule == "momentum":
step_rule = Momentum(args.learning_rate, 0.95)
elif args.step_rule == "rmsprop":
step_rule = RMSProp(args.learning_rate)
elif args.step_rule == "adam":
step_rule = Adam(args.learning_rate)
else:
raise "Unknown step_rule %s" % args.step_rule
#parameters = cg.parameters[:4] + cg.parameters[5:]
parameters = cg.parameters
algorithm = GradientDescent(
cost=cost,
parameters=parameters,
gradients=gradients,
step_rule=CompositeRule([
#StepClipping(25),
step_rule,
#RemoveNotFinite(1.0),
]))
#------------------------------------------------------------
train_monitors += [
aggregation.mean(algorithm.total_gradient_norm),
aggregation.mean(algorithm.total_step_norm)
]
#------------------------------------------------------------
# Live plotting?
plotting_extensions = []
if args.live_plotting:
plotting_extensions = [
PlotManager(
name,
[
Plotter(channels=[[
"valid_%s" % cost.name, "valid_log_p"
], ["train_total_gradient_norm", "train_total_step_norm"]],
titles=[
"validation cost",
"norm of training gradient and step"
]),
DisplayImage(
[
WeightDisplay(model.p_layers[0].mlp.
linear_transformations[0].W,
n_weights=100,
image_shape=(28, 28))
]
#ImageDataStreamDisplay(test_stream, image_shape=(28,28))]
)
])
]
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
Timing(),
ProgressBar(),
TrainingDataMonitoring(
train_monitors, prefix="train", after_epoch=True),
DataStreamMonitoring(
valid_monitors, data_stream=valid_stream, prefix="valid"),
DataStreamMonitoring(test_monitors,
data_stream=test_stream,
prefix="test",
after_epoch=False,
after_training=True,
every_n_epochs=10),
#SharedVariableModifier(
# algorithm.step_rule.components[0].learning_rate,
# half_lr_func,
# before_training=False,
# after_epoch=False,
# after_batch=False,
# every_n_epochs=half_lr),
TrackTheBest('valid_%s' % cost.name),
Checkpoint(name + ".pkl", save_separately=['log', 'model']),
FinishIfNoImprovementAfter('valid_%s_best_so_far' % cost.name,
epochs=args.patience),
FinishAfter(after_n_epochs=args.max_epochs),
Printing()
] + plotting_extensions)
main_loop.run()
allow_input_downcast=True)
#----------------------------------------------------------------------
logger.info("Loading dataset...")
x_dim, _, _, data_test = datasets.get_data(args.data)
num_examples = data_test.num_examples
n_samples = (int(s) for s in args.nsamples.split(","))
dict_p = {}
dict_ps = {}
for K in n_samples:
batch_size = max(args.max_batch // K, 1)
x_dim, _, _, stream = datasets.get_streams(args.data, batch_size)
log_p = np.asarray([])
log_ps = np.asarray([])
for batch in stream.get_epoch_iterator(as_dict=True):
log_p_, log_ps_ = do_nll(batch['features'], K)
log_p = np.concatenate((log_p, log_p_))
log_ps = np.concatenate((log_ps, log_ps_))
log_p_ = stats.sem(log_p)
log_p = np.mean(log_p)
log_ps_ = stats.sem(log_ps)
log_ps = np.mean(log_ps)
dict_p[K] = log_p
def main(args):
"""Run experiment. """
lr_tag = float_tag(args.learning_rate)
x_dim, train_stream, valid_stream, test_stream = datasets.get_streams(
args.data, args.batch_size)
#------------------------------------------------------------
# Setup model
deterministic_act = Tanh
deterministic_size = 1.
if args.method == 'vae':
sizes_tag = args.layer_spec.replace(",", "-")
layer_sizes = [int(i) for i in args.layer_spec.split(",")]
layer_sizes, z_dim = layer_sizes[:-1], layer_sizes[-1]
name = "%s-%s-%s-lr%s-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.n_samples, sizes_tag)
if args.activation == "tanh":
hidden_act = Tanh()
elif args.activation == "logistic":
hidden_act = Logistic()
elif args.activation == "relu":
hidden_act = Rectifier()
else:
raise "Unknown hidden nonlinearity %s" % args.hidden_act
model = VAE(x_dim=x_dim,
hidden_layers=layer_sizes,
hidden_act=hidden_act,
z_dim=z_dim,
batch_norm=args.batch_normalization)
model.initialize()
elif args.method == 'rws':
sizes_tag = args.layer_spec.replace(",", "-")
name = "%s-%s-%s-lr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act,
deterministic_size)
model = ReweightedWakeSleep(
p_layers,
q_layers,
)
model.initialize()
elif args.method == 'bihm':
sizes_tag = args.layer_spec.replace(",", "-")
name = "%s-%s-%s-lr%s-dl%d-spl%d-%s" % \
(args.data, args.method, args.name, lr_tag, args.deterministic_layers, args.n_samples, sizes_tag)
p_layers, q_layers = create_layers(args.layer_spec, x_dim,
args.deterministic_layers,
deterministic_act,
deterministic_size)
model = BiHM(
p_layers,
q_layers,
l1reg=args.l1reg,
l2reg=args.l2reg,
)
model.initialize()
elif args.method == 'continue':
import cPickle as pickle
from os.path import basename, splitext
with open(args.model_file, 'rb') as f:
m = pickle.load(f)
if isinstance(m, MainLoop):
m = m.model
model = m.get_top_bricks()[0]
while len(model.parents) > 0:
model = model.parents[0]
assert isinstance(model, (BiHM, ReweightedWakeSleep, VAE))
mname, _, _ = basename(args.model_file).rpartition("_model.pkl")
name = "%s-cont-%s-lr%s-spl%s" % (mname, args.name, lr_tag,
args.n_samples)
else:
raise ValueError("Unknown training method '%s'" % args.method)
#------------------------------------------------------------
x = tensor.matrix('features')
#------------------------------------------------------------
# Testset monitoring
train_monitors = []
valid_monitors = []
test_monitors = []
for s in [
1,
10,
100,
1000,
]:
log_p, log_ph = model.log_likelihood(x, s)
log_p = -log_p.mean()
log_ph = -log_ph.mean()
log_p.name = "log_p_%d" % s
log_ph.name = "log_ph_%d" % s
#valid_monitors += [log_p, log_ph]
test_monitors += [log_p, log_ph]
#------------------------------------------------------------
# Z estimation
#for s in [100000]:
# z2 = tensor.exp(model.estimate_log_z2(s)) / s
# z2.name = "z2_%d" % s
#
# valid_monitors += [z2]
# test_monitors += [z2]
#------------------------------------------------------------
# Gradient and training monitoring
if args.method in ['vae', 'dvae']:
log_p_bound = model.log_likelihood_bound(x, args.n_samples)
gradients = None
log_p_bound = -log_p_bound.mean()
log_p_bound.name = "log_p_bound"
cost = log_p_bound
train_monitors += [
log_p_bound,
named(model.kl_term.mean(), 'kl_term'),
named(model.recons_term.mean(), 'recons_term')
]
valid_monitors += [
log_p_bound,
named(model.kl_term.mean(), 'kl_term'),
named(model.recons_term.mean(), 'recons_term')
]
test_monitors += [
log_p_bound,
named(model.kl_term.mean(), 'kl_term'),
named(model.recons_term.mean(), 'recons_term')
]
else:
log_p, log_ph, gradients = model.get_gradients(x, args.n_samples)
log_p_bound = named(-model.log_p_bound.mean(), "log_p_bound")
log_p = named(-log_p.mean(), "log_p")
log_ph = named(-log_ph.mean(), "log_ph")
cost = log_p
train_monitors += [log_p_bound, log_p, log_ph]
valid_monitors += [log_p_bound, log_p, log_ph]
#------------------------------------------------------------
cg = ComputationGraph([cost])
if args.step_rule == "momentum":
step_rule = Momentum(args.learning_rate, 0.95)
elif args.step_rule == "rmsprop":
step_rule = RMSProp(args.learning_rate)
elif args.step_rule == "adam":
step_rule = Adam(args.learning_rate)
else:
raise "Unknown step_rule %s" % args.step_rule
parameters = cg.parameters
algorithm = GradientDescent(cost=cost,
parameters=parameters,
gradients=gradients,
step_rule=CompositeRule([
step_rule,
]))
#------------------------------------------------------------
train_monitors += [
aggregation.mean(algorithm.total_gradient_norm),
aggregation.mean(algorithm.total_step_norm)
]
#------------------------------------------------------------
# Live plotting?
plotting_extensions = []
if args.live_plotting:
plotting_extensions = [
PlotManager(
name,
[
Plotter(channels=[[
"valid_%s" % cost.name, "valid_log_p"
], ["train_total_gradient_norm", "train_total_step_norm"]],
titles=[
"validation cost",
"norm of training gradient and step"
]),
DisplayImage(
[
WeightDisplay(model.p_layers[0].mlp.
linear_transformations[0].W,
n_weights=100,
image_shape=(28, 28))
]
#ImageDataStreamDisplay(test_stream, image_shape=(28,28))]
)
])
]
main_loop = MainLoop(
model=Model(cost),
data_stream=train_stream,
algorithm=algorithm,
extensions=[
Timing(),
ProgressBar(),
TrainingDataMonitoring(train_monitors,
prefix="train",
after_epoch=False,
after_batch=True),
DataStreamMonitoring(
valid_monitors, data_stream=valid_stream, prefix="valid"),
DataStreamMonitoring(test_monitors,
data_stream=test_stream,
prefix="test",
after_epoch=False,
after_training=True,
every_n_epochs=10),
TrackTheBest('valid_%s' % cost.name),
Checkpoint(name + ".pkl", save_separately=['log', 'model']),
FinishIfNoImprovementAfter('valid_%s_best_so_far' % cost.name,
epochs=args.patience),
FinishAfter(after_n_epochs=args.max_epochs),
Printing()
] + plotting_extensions)
main_loop.run()