partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
test | optimize_updates | General optimization function for Theano.
Parameters:
params - parameters
gradients - gradients
config - training config
Returns:
Theano updates
:type config: deepy.TrainerConfig or dict | deepy/trainers/optimize.py | def optimize_updates(params, gradients, config=None, shapes=None):
"""
General optimization function for Theano.
Parameters:
params - parameters
gradients - gradients
config - training config
Returns:
Theano updates
:type config: deepy.TrainerConfig or dict
"""
... | def optimize_updates(params, gradients, config=None, shapes=None):
"""
General optimization function for Theano.
Parameters:
params - parameters
gradients - gradients
config - training config
Returns:
Theano updates
:type config: deepy.TrainerConfig or dict
"""
... | [
"General",
"optimization",
"function",
"for",
"Theano",
".",
"Parameters",
":",
"params",
"-",
"parameters",
"gradients",
"-",
"gradients",
"config",
"-",
"training",
"config",
"Returns",
":",
"Theano",
"updates",
":",
"type",
"config",
":",
"deepy",
".",
"Tra... | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/optimize.py#L19-L123 | [
"def",
"optimize_updates",
"(",
"params",
",",
"gradients",
",",
"config",
"=",
"None",
",",
"shapes",
"=",
"None",
")",
":",
"if",
"config",
"and",
"isinstance",
"(",
"config",
",",
"dict",
")",
":",
"config",
"=",
"TrainerConfig",
"(",
"config",
")",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | optimize_function | Create a optimizing function receives gradients.
Parameters:
params - parameters
config - training configuration
Returns:
updating function receives gradients | deepy/trainers/optimize.py | def optimize_function(params, config=None):
"""
Create a optimizing function receives gradients.
Parameters:
params - parameters
config - training configuration
Returns:
updating function receives gradients
"""
gs = [dim_to_var(p.ndim) for p in params]
updates, _ = op... | def optimize_function(params, config=None):
"""
Create a optimizing function receives gradients.
Parameters:
params - parameters
config - training configuration
Returns:
updating function receives gradients
"""
gs = [dim_to_var(p.ndim) for p in params]
updates, _ = op... | [
"Create",
"a",
"optimizing",
"function",
"receives",
"gradients",
".",
"Parameters",
":",
"params",
"-",
"parameters",
"config",
"-",
"training",
"configuration",
"Returns",
":",
"updating",
"function",
"receives",
"gradients"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/optimize.py#L125-L136 | [
"def",
"optimize_function",
"(",
"params",
",",
"config",
"=",
"None",
")",
":",
"gs",
"=",
"[",
"dim_to_var",
"(",
"p",
".",
"ndim",
")",
"for",
"p",
"in",
"params",
"]",
"updates",
",",
"_",
"=",
"optimize_updates",
"(",
"params",
",",
"gs",
",",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GeneralNeuralTrainer._learning_updates | Return updates in the training. | deepy/trainers/trainers.py | def _learning_updates(self):
"""
Return updates in the training.
"""
params = self.training_params()
gradients = self.get_gradients(params)
return self.optimization_updates(params, gradients) | def _learning_updates(self):
"""
Return updates in the training.
"""
params = self.training_params()
gradients = self.get_gradients(params)
return self.optimization_updates(params, gradients) | [
"Return",
"updates",
"in",
"the",
"training",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/trainers.py#L40-L46 | [
"def",
"_learning_updates",
"(",
"self",
")",
":",
"params",
"=",
"self",
".",
"training_params",
"(",
")",
"gradients",
"=",
"self",
".",
"get_gradients",
"(",
"params",
")",
"return",
"self",
".",
"optimization_updates",
"(",
"params",
",",
"gradients",
")... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GeneralNeuralTrainer.training_params | Get parameters to be optimized. | deepy/trainers/trainers.py | def training_params(self):
"""
Get parameters to be optimized.
"""
params = self.network.parameters
# Freeze parameters
if self.config.fixed_parameters:
logging.info("fixed parameters: %s" % ", ".join(map(str, self.config.fixed_parameters)))
params... | def training_params(self):
"""
Get parameters to be optimized.
"""
params = self.network.parameters
# Freeze parameters
if self.config.fixed_parameters:
logging.info("fixed parameters: %s" % ", ".join(map(str, self.config.fixed_parameters)))
params... | [
"Get",
"parameters",
"to",
"be",
"optimized",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/trainers.py#L48-L57 | [
"def",
"training_params",
"(",
"self",
")",
":",
"params",
"=",
"self",
".",
"network",
".",
"parameters",
"# Freeze parameters",
"if",
"self",
".",
"config",
".",
"fixed_parameters",
":",
"logging",
".",
"info",
"(",
"\"fixed parameters: %s\"",
"%",
"\", \"",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GeneralNeuralTrainer.optimization_updates | Return updates from optimization. | deepy/trainers/trainers.py | def optimization_updates(self, params, gradients):
"""
Return updates from optimization.
"""
updates, free_parameters = optimize_updates(params, gradients, self.config)
self.network.free_parameters.extend(free_parameters)
logging.info("Added %d free parameters for optimiz... | def optimization_updates(self, params, gradients):
"""
Return updates from optimization.
"""
updates, free_parameters = optimize_updates(params, gradients, self.config)
self.network.free_parameters.extend(free_parameters)
logging.info("Added %d free parameters for optimiz... | [
"Return",
"updates",
"from",
"optimization",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/trainers.py#L65-L72 | [
"def",
"optimization_updates",
"(",
"self",
",",
"params",
",",
"gradients",
")",
":",
"updates",
",",
"free_parameters",
"=",
"optimize_updates",
"(",
"params",
",",
"gradients",
",",
"self",
".",
"config",
")",
"self",
".",
"network",
".",
"free_parameters",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GeneralNeuralTrainer.learning_function | Get the learning function.
:param func:
:return: | deepy/trainers/trainers.py | def learning_function(self):
"""
Get the learning function.
:param func:
:return:
"""
network_updates = list(self.network.updates) + list(self.network.training_updates)
learning_updates = list(self._learning_updates())
update_list = network_updates + learn... | def learning_function(self):
"""
Get the learning function.
:param func:
:return:
"""
network_updates = list(self.network.updates) + list(self.network.training_updates)
learning_updates = list(self._learning_updates())
update_list = network_updates + learn... | [
"Get",
"the",
"learning",
"function",
".",
":",
"param",
"func",
":",
":",
"return",
":"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/trainers.py#L74-L94 | [
"def",
"learning_function",
"(",
"self",
")",
":",
"network_updates",
"=",
"list",
"(",
"self",
".",
"network",
".",
"updates",
")",
"+",
"list",
"(",
"self",
".",
"network",
".",
"training_updates",
")",
"learning_updates",
"=",
"list",
"(",
"self",
".",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AttentionLayer._glimpse_sensor | Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
4x12 matrix | examples/attention_models/baseline_model.py | def _glimpse_sensor(self, x_t, l_p):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
4x12 matrix
"""
# Turn l_p to the left-top point of rectangle
l_p = l_p * 14 + 14 - 2
l_p = T.cast(T.round(l_p), "int32")
... | def _glimpse_sensor(self, x_t, l_p):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
4x12 matrix
"""
# Turn l_p to the left-top point of rectangle
l_p = l_p * 14 + 14 - 2
l_p = T.cast(T.round(l_p), "int32")
... | [
"Parameters",
":",
"x_t",
"-",
"28x28",
"image",
"l_p",
"-",
"2x1",
"focus",
"vector",
"Returns",
":",
"4x12",
"matrix"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/attention_models/baseline_model.py#L37-L62 | [
"def",
"_glimpse_sensor",
"(",
"self",
",",
"x_t",
",",
"l_p",
")",
":",
"# Turn l_p to the left-top point of rectangle",
"l_p",
"=",
"l_p",
"*",
"14",
"+",
"14",
"-",
"2",
"l_p",
"=",
"T",
".",
"cast",
"(",
"T",
".",
"round",
"(",
"l_p",
")",
",",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AttentionLayer._refined_glimpse_sensor | Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
7*14 matrix | examples/attention_models/baseline_model.py | def _refined_glimpse_sensor(self, x_t, l_p):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
7*14 matrix
"""
# Turn l_p to the left-top point of rectangle
l_p = l_p * 14 + 14 - 4
l_p = T.cast(T.round(l_p), "int... | def _refined_glimpse_sensor(self, x_t, l_p):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
7*14 matrix
"""
# Turn l_p to the left-top point of rectangle
l_p = l_p * 14 + 14 - 4
l_p = T.cast(T.round(l_p), "int... | [
"Parameters",
":",
"x_t",
"-",
"28x28",
"image",
"l_p",
"-",
"2x1",
"focus",
"vector",
"Returns",
":",
"7",
"*",
"14",
"matrix"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/attention_models/baseline_model.py#L64-L81 | [
"def",
"_refined_glimpse_sensor",
"(",
"self",
",",
"x_t",
",",
"l_p",
")",
":",
"# Turn l_p to the left-top point of rectangle",
"l_p",
"=",
"l_p",
"*",
"14",
"+",
"14",
"-",
"4",
"l_p",
"=",
"T",
".",
"cast",
"(",
"T",
".",
"round",
"(",
"l_p",
")",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AttentionLayer._glimpse_network | Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
4x12 matrix | examples/attention_models/baseline_model.py | def _glimpse_network(self, x_t, l_p):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
4x12 matrix
"""
sensor_output = self._refined_glimpse_sensor(x_t, l_p)
sensor_output = T.flatten(sensor_output)
h_g = self._... | def _glimpse_network(self, x_t, l_p):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
Returns:
4x12 matrix
"""
sensor_output = self._refined_glimpse_sensor(x_t, l_p)
sensor_output = T.flatten(sensor_output)
h_g = self._... | [
"Parameters",
":",
"x_t",
"-",
"28x28",
"image",
"l_p",
"-",
"2x1",
"focus",
"vector",
"Returns",
":",
"4x12",
"matrix"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/attention_models/baseline_model.py#L88-L101 | [
"def",
"_glimpse_network",
"(",
"self",
",",
"x_t",
",",
"l_p",
")",
":",
"sensor_output",
"=",
"self",
".",
"_refined_glimpse_sensor",
"(",
"x_t",
",",
"l_p",
")",
"sensor_output",
"=",
"T",
".",
"flatten",
"(",
"sensor_output",
")",
"h_g",
"=",
"self",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AttentionLayer._action_network | Parameters:
h_t - 256x1 vector
Returns:
10x1 vector | examples/attention_models/baseline_model.py | def _action_network(self, h_t):
"""
Parameters:
h_t - 256x1 vector
Returns:
10x1 vector
"""
z = self._relu(T.dot(h_t, self.W_a) + self.B_a)
return self._softmax(z) | def _action_network(self, h_t):
"""
Parameters:
h_t - 256x1 vector
Returns:
10x1 vector
"""
z = self._relu(T.dot(h_t, self.W_a) + self.B_a)
return self._softmax(z) | [
"Parameters",
":",
"h_t",
"-",
"256x1",
"vector",
"Returns",
":",
"10x1",
"vector"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/attention_models/baseline_model.py#L112-L120 | [
"def",
"_action_network",
"(",
"self",
",",
"h_t",
")",
":",
"z",
"=",
"self",
".",
"_relu",
"(",
"T",
".",
"dot",
"(",
"h_t",
",",
"self",
".",
"W_a",
")",
"+",
"self",
".",
"B_a",
")",
"return",
"self",
".",
"_softmax",
"(",
"z",
")"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | get_network | Get baseline model.
Parameters:
model - model path
Returns:
network | examples/attention_models/first_glimpse_model.py | def get_network(model=None, std=0.005, disable_reinforce=False, random_glimpse=False):
"""
Get baseline model.
Parameters:
model - model path
Returns:
network
"""
network = NeuralClassifier(input_dim=28 * 28)
network.stack_layer(FirstGlimpseLayer(std=std, disable_reinforce=di... | def get_network(model=None, std=0.005, disable_reinforce=False, random_glimpse=False):
"""
Get baseline model.
Parameters:
model - model path
Returns:
network
"""
network = NeuralClassifier(input_dim=28 * 28)
network.stack_layer(FirstGlimpseLayer(std=std, disable_reinforce=di... | [
"Get",
"baseline",
"model",
".",
"Parameters",
":",
"model",
"-",
"model",
"path",
"Returns",
":",
"network"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/attention_models/first_glimpse_model.py#L192-L204 | [
"def",
"get_network",
"(",
"model",
"=",
"None",
",",
"std",
"=",
"0.005",
",",
"disable_reinforce",
"=",
"False",
",",
"random_glimpse",
"=",
"False",
")",
":",
"network",
"=",
"NeuralClassifier",
"(",
"input_dim",
"=",
"28",
"*",
"28",
")",
"network",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | FirstGlimpseLayer._first_glimpse_sensor | Compute first glimpse position using down-sampled image. | examples/attention_models/first_glimpse_model.py | def _first_glimpse_sensor(self, x_t):
"""
Compute first glimpse position using down-sampled image.
"""
downsampled_img = theano.tensor.signal.downsample.max_pool_2d(x_t, (4,4))
downsampled_img = downsampled_img.flatten()
first_l = T.dot(downsampled_img, self.W_f)
... | def _first_glimpse_sensor(self, x_t):
"""
Compute first glimpse position using down-sampled image.
"""
downsampled_img = theano.tensor.signal.downsample.max_pool_2d(x_t, (4,4))
downsampled_img = downsampled_img.flatten()
first_l = T.dot(downsampled_img, self.W_f)
... | [
"Compute",
"first",
"glimpse",
"position",
"using",
"down",
"-",
"sampled",
"image",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/attention_models/first_glimpse_model.py#L38-L54 | [
"def",
"_first_glimpse_sensor",
"(",
"self",
",",
"x_t",
")",
":",
"downsampled_img",
"=",
"theano",
".",
"tensor",
".",
"signal",
".",
"downsample",
".",
"max_pool_2d",
"(",
"x_t",
",",
"(",
"4",
",",
"4",
")",
")",
"downsampled_img",
"=",
"downsampled_im... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | FirstGlimpseLayer._core_network | Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
h_p - 256x1 vector
Returns:
h_t, 256x1 vector | examples/attention_models/first_glimpse_model.py | def _core_network(self, l_p, h_p, x_t):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
h_p - 256x1 vector
Returns:
h_t, 256x1 vector
"""
g_t = self._glimpse_network(x_t, l_p)
h_t = self._tanh(T.dot(g_t, self.W_h_g)... | def _core_network(self, l_p, h_p, x_t):
"""
Parameters:
x_t - 28x28 image
l_p - 2x1 focus vector
h_p - 256x1 vector
Returns:
h_t, 256x1 vector
"""
g_t = self._glimpse_network(x_t, l_p)
h_t = self._tanh(T.dot(g_t, self.W_h_g)... | [
"Parameters",
":",
"x_t",
"-",
"28x28",
"image",
"l_p",
"-",
"2x1",
"focus",
"vector",
"h_p",
"-",
"256x1",
"vector",
"Returns",
":",
"h_t",
"256x1",
"vector"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/attention_models/first_glimpse_model.py#L107-L133 | [
"def",
"_core_network",
"(",
"self",
",",
"l_p",
",",
"h_p",
",",
"x_t",
")",
":",
"g_t",
"=",
"self",
".",
"_glimpse_network",
"(",
"x_t",
",",
"l_p",
")",
"h_t",
"=",
"self",
".",
"_tanh",
"(",
"T",
".",
"dot",
"(",
"g_t",
",",
"self",
".",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | MyJointTrainingModel.prepare | All codes that create parameters should be put into 'setup' function. | examples/tutorials/tutorial2.py | def prepare(self):
"""
All codes that create parameters should be put into 'setup' function.
"""
self.output_dim = 10
self.encoder = Chain(self.input_dim).stack(Dense(self.internal_layer_size, 'tanh'))
self.decoder = Chain(self.internal_layer_size).stack(Dense(self.input_... | def prepare(self):
"""
All codes that create parameters should be put into 'setup' function.
"""
self.output_dim = 10
self.encoder = Chain(self.input_dim).stack(Dense(self.internal_layer_size, 'tanh'))
self.decoder = Chain(self.internal_layer_size).stack(Dense(self.input_... | [
"All",
"codes",
"that",
"create",
"parameters",
"should",
"be",
"put",
"into",
"setup",
"function",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/tutorials/tutorial2.py#L27-L41 | [
"def",
"prepare",
"(",
"self",
")",
":",
"self",
".",
"output_dim",
"=",
"10",
"self",
".",
"encoder",
"=",
"Chain",
"(",
"self",
".",
"input_dim",
")",
".",
"stack",
"(",
"Dense",
"(",
"self",
".",
"internal_layer_size",
",",
"'tanh'",
")",
")",
"se... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | MyJointTrainingModel.compute_tensor | Build the computation graph here. | examples/tutorials/tutorial2.py | def compute_tensor(self, x):
"""
Build the computation graph here.
"""
internal_variable = self.encoder.compute_tensor(x)
decoding_output = self.decoder.compute_tensor(internal_variable)
classification_output = self.classifier.compute_tensor(internal_variable)
... | def compute_tensor(self, x):
"""
Build the computation graph here.
"""
internal_variable = self.encoder.compute_tensor(x)
decoding_output = self.decoder.compute_tensor(internal_variable)
classification_output = self.classifier.compute_tensor(internal_variable)
... | [
"Build",
"the",
"computation",
"graph",
"here",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/tutorials/tutorial2.py#L43-L65 | [
"def",
"compute_tensor",
"(",
"self",
",",
"x",
")",
":",
"internal_variable",
"=",
"self",
".",
"encoder",
".",
"compute_tensor",
"(",
"x",
")",
"decoding_output",
"=",
"self",
".",
"decoder",
".",
"compute_tensor",
"(",
"internal_variable",
")",
"classificat... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | BasicDataset.map | Process all data with given function.
The scheme of function should be x,y -> x,y. | deepy/dataset/basic.py | def map(self, func):
"""
Process all data with given function.
The scheme of function should be x,y -> x,y.
"""
if self._train_set:
self._train_set = map(func, self._train_set)
if self._valid_set:
self._valid_set = map(func, self._valid_set)
... | def map(self, func):
"""
Process all data with given function.
The scheme of function should be x,y -> x,y.
"""
if self._train_set:
self._train_set = map(func, self._train_set)
if self._valid_set:
self._valid_set = map(func, self._valid_set)
... | [
"Process",
"all",
"data",
"with",
"given",
"function",
".",
"The",
"scheme",
"of",
"function",
"should",
"be",
"x",
"y",
"-",
">",
"x",
"y",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/basic.py#L30-L40 | [
"def",
"map",
"(",
"self",
",",
"func",
")",
":",
"if",
"self",
".",
"_train_set",
":",
"self",
".",
"_train_set",
"=",
"map",
"(",
"func",
",",
"self",
".",
"_train_set",
")",
"if",
"self",
".",
"_valid_set",
":",
"self",
".",
"_valid_set",
"=",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | BasicDataset.vectorize_target | Make targets be one-hot vectors. | deepy/dataset/basic.py | def vectorize_target(self, size):
"""
Make targets be one-hot vectors.
"""
if self._train_set:
self._train_set = self._vectorize_set(self._train_set, size)
if self._valid_set:
self._valid_set = self._vectorize_set(self._valid_set, size)
if self._te... | def vectorize_target(self, size):
"""
Make targets be one-hot vectors.
"""
if self._train_set:
self._train_set = self._vectorize_set(self._train_set, size)
if self._valid_set:
self._valid_set = self._vectorize_set(self._valid_set, size)
if self._te... | [
"Make",
"targets",
"be",
"one",
"-",
"hot",
"vectors",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/basic.py#L51-L60 | [
"def",
"vectorize_target",
"(",
"self",
",",
"size",
")",
":",
"if",
"self",
".",
"_train_set",
":",
"self",
".",
"_train_set",
"=",
"self",
".",
"_vectorize_set",
"(",
"self",
".",
"_train_set",
",",
"size",
")",
"if",
"self",
".",
"_valid_set",
":",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | BasicDataset.report | Print dataset statistics. | deepy/dataset/basic.py | def report(self):
"""
Print dataset statistics.
"""
logging.info("%s train=%d valid=%d test=%d" % (self.__class__.__name__,
len(list(self._train_set)) if self._train_set else 0,
... | def report(self):
"""
Print dataset statistics.
"""
logging.info("%s train=%d valid=%d test=%d" % (self.__class__.__name__,
len(list(self._train_set)) if self._train_set else 0,
... | [
"Print",
"dataset",
"statistics",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/basic.py#L62-L69 | [
"def",
"report",
"(",
"self",
")",
":",
"logging",
".",
"info",
"(",
"\"%s train=%d valid=%d test=%d\"",
"%",
"(",
"self",
".",
"__class__",
".",
"__name__",
",",
"len",
"(",
"list",
"(",
"self",
".",
"_train_set",
")",
")",
"if",
"self",
".",
"_train_se... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | CustomizeTrainer.train | We train over mini-batches and evaluate periodically. | deepy/trainers/customize_trainer.py | def train(self, train_set, valid_set=None, test_set=None, train_size=None):
'''We train over mini-batches and evaluate periodically.'''
iteration = 0
while True:
if not iteration % self.config.test_frequency and test_set:
try:
self.test(iteration, ... | def train(self, train_set, valid_set=None, test_set=None, train_size=None):
'''We train over mini-batches and evaluate periodically.'''
iteration = 0
while True:
if not iteration % self.config.test_frequency and test_set:
try:
self.test(iteration, ... | [
"We",
"train",
"over",
"mini",
"-",
"batches",
"and",
"evaluate",
"periodically",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/customize_trainer.py#L28-L66 | [
"def",
"train",
"(",
"self",
",",
"train_set",
",",
"valid_set",
"=",
"None",
",",
"test_set",
"=",
"None",
",",
"train_size",
"=",
"None",
")",
":",
"iteration",
"=",
"0",
"while",
"True",
":",
"if",
"not",
"iteration",
"%",
"self",
".",
"config",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLM.sample | Sample outputs from LM. | examples/lm/lm.py | def sample(self, input, steps):
"""
Sample outputs from LM.
"""
inputs = [[onehot(self.input_dim, x) for x in input]]
for _ in range(steps):
target = self.compute(inputs)[0,-1].argmax()
input.append(target)
inputs[0].append(onehot(self.input_di... | def sample(self, input, steps):
"""
Sample outputs from LM.
"""
inputs = [[onehot(self.input_dim, x) for x in input]]
for _ in range(steps):
target = self.compute(inputs)[0,-1].argmax()
input.append(target)
inputs[0].append(onehot(self.input_di... | [
"Sample",
"outputs",
"from",
"LM",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/lm/lm.py#L60-L69 | [
"def",
"sample",
"(",
"self",
",",
"input",
",",
"steps",
")",
":",
"inputs",
"=",
"[",
"[",
"onehot",
"(",
"self",
".",
"input_dim",
",",
"x",
")",
"for",
"x",
"in",
"input",
"]",
"]",
"for",
"_",
"in",
"range",
"(",
"steps",
")",
":",
"target... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | ClassOutputLayer.compute_tensor | :param x: (batch, time, vec) | examples/lm/layers.py | def compute_tensor(self, x):
"""
:param x: (batch, time, vec)
"""
# Target class
class_matrix = self.target_tensor // self.output_size
class_vector = class_matrix.reshape((-1,))
# Target index
target_matrix = self.target_tensor % self.output_size
t... | def compute_tensor(self, x):
"""
:param x: (batch, time, vec)
"""
# Target class
class_matrix = self.target_tensor // self.output_size
class_vector = class_matrix.reshape((-1,))
# Target index
target_matrix = self.target_tensor % self.output_size
t... | [
":",
"param",
"x",
":",
"(",
"batch",
"time",
"vec",
")"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/lm/layers.py#L49-L75 | [
"def",
"compute_tensor",
"(",
"self",
",",
"x",
")",
":",
"# Target class",
"class_matrix",
"=",
"self",
".",
"target_tensor",
"//",
"self",
".",
"output_size",
"class_vector",
"=",
"class_matrix",
".",
"reshape",
"(",
"(",
"-",
"1",
",",
")",
")",
"# Targ... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Attention.compute_alignments | Compute the alignment weights based on the previous state. | deepy/layers/attention.py | def compute_alignments(self, prev_state, precomputed_values, mask=None):
"""
Compute the alignment weights based on the previous state.
"""
WaSp = T.dot(prev_state, self.Wa)
UaH = precomputed_values
# For test time the UaH will be (time, output_dim)
if UaH.ndim =... | def compute_alignments(self, prev_state, precomputed_values, mask=None):
"""
Compute the alignment weights based on the previous state.
"""
WaSp = T.dot(prev_state, self.Wa)
UaH = precomputed_values
# For test time the UaH will be (time, output_dim)
if UaH.ndim =... | [
"Compute",
"the",
"alignment",
"weights",
"based",
"on",
"the",
"previous",
"state",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/attention.py#L29-L50 | [
"def",
"compute_alignments",
"(",
"self",
",",
"prev_state",
",",
"precomputed_values",
",",
"mask",
"=",
"None",
")",
":",
"WaSp",
"=",
"T",
".",
"dot",
"(",
"prev_state",
",",
"self",
".",
"Wa",
")",
"UaH",
"=",
"precomputed_values",
"# For test time the U... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Attention.compute_context_vector | Compute the context vector with soft attention. | deepy/layers/attention.py | def compute_context_vector(self, prev_state, inputs, precomputed_values=None, mask=None):
"""
Compute the context vector with soft attention.
"""
precomputed_values = precomputed_values if precomputed_values else self.precompute(inputs)
align_weights = self.compute_alignments(pre... | def compute_context_vector(self, prev_state, inputs, precomputed_values=None, mask=None):
"""
Compute the context vector with soft attention.
"""
precomputed_values = precomputed_values if precomputed_values else self.precompute(inputs)
align_weights = self.compute_alignments(pre... | [
"Compute",
"the",
"context",
"vector",
"with",
"soft",
"attention",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/attention.py#L52-L59 | [
"def",
"compute_context_vector",
"(",
"self",
",",
"prev_state",
",",
"inputs",
",",
"precomputed_values",
"=",
"None",
",",
"mask",
"=",
"None",
")",
":",
"precomputed_values",
"=",
"precomputed_values",
"if",
"precomputed_values",
"else",
"self",
".",
"precomput... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | MultiGPUTrainer.train | Train the model in multi-GPU environment. | deepy/multigpu/worker.py | def train(self, train_set, valid_set=None, test_set=None, train_size=None):
"""
Train the model in multi-GPU environment.
"""
from platoon.channel import Worker
from platoon.param_sync import EASGD, ASGD
server_port = self._port
param_map = self.create_param_map()... | def train(self, train_set, valid_set=None, test_set=None, train_size=None):
"""
Train the model in multi-GPU environment.
"""
from platoon.channel import Worker
from platoon.param_sync import EASGD, ASGD
server_port = self._port
param_map = self.create_param_map()... | [
"Train",
"the",
"model",
"in",
"multi",
"-",
"GPU",
"environment",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/multigpu/worker.py#L59-L159 | [
"def",
"train",
"(",
"self",
",",
"train_set",
",",
"valid_set",
"=",
"None",
",",
"test_set",
"=",
"None",
",",
"train_size",
"=",
"None",
")",
":",
"from",
"platoon",
".",
"channel",
"import",
"Worker",
"from",
"platoon",
".",
"param_sync",
"import",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | concatenate | A utility function of concatenate. | deepy/tensor/functions.py | def concatenate(vars, axis=-1):
"""
A utility function of concatenate.
"""
from deepy.core.neural_var import NeuralVariable
if isinstance(vars[0], NeuralVariable):
concat_var = Concatenate(axis=axis).compute(*vars)
if axis == -1 or axis == vars[0].tensor.ndim - 1:
concat_... | def concatenate(vars, axis=-1):
"""
A utility function of concatenate.
"""
from deepy.core.neural_var import NeuralVariable
if isinstance(vars[0], NeuralVariable):
concat_var = Concatenate(axis=axis).compute(*vars)
if axis == -1 or axis == vars[0].tensor.ndim - 1:
concat_... | [
"A",
"utility",
"function",
"of",
"concatenate",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/tensor/functions.py#L38-L49 | [
"def",
"concatenate",
"(",
"vars",
",",
"axis",
"=",
"-",
"1",
")",
":",
"from",
"deepy",
".",
"core",
".",
"neural_var",
"import",
"NeuralVariable",
"if",
"isinstance",
"(",
"vars",
"[",
"0",
"]",
",",
"NeuralVariable",
")",
":",
"concat_var",
"=",
"C... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | var | Wrap a Theano tensor into the variable for defining neural network.
:param last_dim: last dimension of tensor, 0 indicates that the last dimension is flexible
:rtype: deepy.core.neural_var.NeuralVariable | deepy/tensor/functions.py | def var(tensor_type, last_dim=0, test_shape=None):
"""
Wrap a Theano tensor into the variable for defining neural network.
:param last_dim: last dimension of tensor, 0 indicates that the last dimension is flexible
:rtype: deepy.core.neural_var.NeuralVariable
"""
# Create tensor
from deepy.co... | def var(tensor_type, last_dim=0, test_shape=None):
"""
Wrap a Theano tensor into the variable for defining neural network.
:param last_dim: last dimension of tensor, 0 indicates that the last dimension is flexible
:rtype: deepy.core.neural_var.NeuralVariable
"""
# Create tensor
from deepy.co... | [
"Wrap",
"a",
"Theano",
"tensor",
"into",
"the",
"variable",
"for",
"defining",
"neural",
"network",
".",
":",
"param",
"last_dim",
":",
"last",
"dimension",
"of",
"tensor",
"0",
"indicates",
"that",
"the",
"last",
"dimension",
"is",
"flexible",
":",
"rtype",... | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/tensor/functions.py#L72-L116 | [
"def",
"var",
"(",
"tensor_type",
",",
"last_dim",
"=",
"0",
",",
"test_shape",
"=",
"None",
")",
":",
"# Create tensor",
"from",
"deepy",
".",
"core",
".",
"neural_var",
"import",
"NeuralVariable",
"from",
"deepy",
".",
"core",
".",
"env",
"import",
"env"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | SequentialDataset._pad | Pad sequences to given length in the left or right side. | deepy/dataset/sequence.py | def _pad(self, side, length):
"""
Pad sequences to given length in the left or right side.
"""
if self._train_set:
self._train_set = pad_dataset(self._train_set, side, length)
if self._valid_set:
self._valid_set = pad_dataset(self._valid_set, side, length)... | def _pad(self, side, length):
"""
Pad sequences to given length in the left or right side.
"""
if self._train_set:
self._train_set = pad_dataset(self._train_set, side, length)
if self._valid_set:
self._valid_set = pad_dataset(self._valid_set, side, length)... | [
"Pad",
"sequences",
"to",
"given",
"length",
"in",
"the",
"left",
"or",
"right",
"side",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/sequence.py#L15-L24 | [
"def",
"_pad",
"(",
"self",
",",
"side",
",",
"length",
")",
":",
"if",
"self",
".",
"_train_set",
":",
"self",
".",
"_train_set",
"=",
"pad_dataset",
"(",
"self",
".",
"_train_set",
",",
"side",
",",
"length",
")",
"if",
"self",
".",
"_valid_set",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | rmsprop_core | RMSPROP optimization core. | deepy/trainers/cores/rmsprop.py | def rmsprop_core(params, gradients, momentum=0.9, learning_rate=0.01):
"""
RMSPROP optimization core.
"""
for param, grad in zip(params, gradients):
rms_ = theano.shared(np.zeros_like(param.get_value()), name=param.name + '_rms')
rms = momentum * rms_ + (1 - momentum) * grad * gr... | def rmsprop_core(params, gradients, momentum=0.9, learning_rate=0.01):
"""
RMSPROP optimization core.
"""
for param, grad in zip(params, gradients):
rms_ = theano.shared(np.zeros_like(param.get_value()), name=param.name + '_rms')
rms = momentum * rms_ + (1 - momentum) * grad * gr... | [
"RMSPROP",
"optimization",
"core",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/cores/rmsprop.py#L8-L16 | [
"def",
"rmsprop_core",
"(",
"params",
",",
"gradients",
",",
"momentum",
"=",
"0.9",
",",
"learning_rate",
"=",
"0.01",
")",
":",
"for",
"param",
",",
"grad",
"in",
"zip",
"(",
"params",
",",
"gradients",
")",
":",
"rms_",
"=",
"theano",
".",
"shared",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | pad_dataset | Pad data set to specified length.
Parameters:
length - max length, a just to the max length in the batch if length is -1 | deepy/dataset/padding.py | def pad_dataset(subset, side="right", length=-1):
"""
Pad data set to specified length.
Parameters:
length - max length, a just to the max length in the batch if length is -1
"""
assert length == -1 or length > 0
if type(subset[0][0][0]) in [float, int, np.int64, np.int32, np.float32]:
... | def pad_dataset(subset, side="right", length=-1):
"""
Pad data set to specified length.
Parameters:
length - max length, a just to the max length in the batch if length is -1
"""
assert length == -1 or length > 0
if type(subset[0][0][0]) in [float, int, np.int64, np.int32, np.float32]:
... | [
"Pad",
"data",
"set",
"to",
"specified",
"length",
".",
"Parameters",
":",
"length",
"-",
"max",
"length",
"a",
"just",
"to",
"the",
"max",
"length",
"in",
"the",
"batch",
"if",
"length",
"is",
"-",
"1"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/padding.py#L7-L17 | [
"def",
"pad_dataset",
"(",
"subset",
",",
"side",
"=",
"\"right\"",
",",
"length",
"=",
"-",
"1",
")",
":",
"assert",
"length",
"==",
"-",
"1",
"or",
"length",
">",
"0",
"if",
"type",
"(",
"subset",
"[",
"0",
"]",
"[",
"0",
"]",
"[",
"0",
"]",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | ScheduledTrainingServer.prepare_epoch | Prepare for one epoch.
Returns:
bool: False if to stop the training. | deepy/multigpu/server.py | def prepare_epoch(self):
"""
Prepare for one epoch.
Returns:
bool: False if to stop the training.
"""
self.epoch += 1
if self.epoch >= self.epoch_start_halving and ((self.epoch - self.epoch_start_halving) % self._halving_freq == 0):
self._lr *= 0.5... | def prepare_epoch(self):
"""
Prepare for one epoch.
Returns:
bool: False if to stop the training.
"""
self.epoch += 1
if self.epoch >= self.epoch_start_halving and ((self.epoch - self.epoch_start_halving) % self._halving_freq == 0):
self._lr *= 0.5... | [
"Prepare",
"for",
"one",
"epoch",
".",
"Returns",
":",
"bool",
":",
"False",
"if",
"to",
"stop",
"the",
"training",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/multigpu/server.py#L71-L91 | [
"def",
"prepare_epoch",
"(",
"self",
")",
":",
"self",
".",
"epoch",
"+=",
"1",
"if",
"self",
".",
"epoch",
">=",
"self",
".",
"epoch_start_halving",
"and",
"(",
"(",
"self",
".",
"epoch",
"-",
"self",
".",
"epoch_start_halving",
")",
"%",
"self",
".",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | ScheduledTrainingServer.handle_control | Handles a control_request received from a worker.
Returns:
string or dict: response
'stop' - the worker should quit
'wait' - wait for 1 second
'eval' - evaluate on valid and test set to start a new epoch
'sync_hyperparams' - set learning rate
... | deepy/multigpu/server.py | def handle_control(self, req, worker_id, req_info):
"""
Handles a control_request received from a worker.
Returns:
string or dict: response
'stop' - the worker should quit
'wait' - wait for 1 second
'eval' - evaluate on valid and test set to start... | def handle_control(self, req, worker_id, req_info):
"""
Handles a control_request received from a worker.
Returns:
string or dict: response
'stop' - the worker should quit
'wait' - wait for 1 second
'eval' - evaluate on valid and test set to start... | [
"Handles",
"a",
"control_request",
"received",
"from",
"a",
"worker",
".",
"Returns",
":",
"string",
"or",
"dict",
":",
"response"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/multigpu/server.py#L116-L262 | [
"def",
"handle_control",
"(",
"self",
",",
"req",
",",
"worker_id",
",",
"req_info",
")",
":",
"if",
"self",
".",
"start_time",
"is",
"None",
":",
"self",
".",
"start_time",
"=",
"time",
".",
"time",
"(",
")",
"response",
"=",
"\"\"",
"if",
"req",
"=... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Timer.report | Report elapsed time. | deepy/utils/timer.py | def report(self):
"""
Report elapsed time.
"""
if not self.end_time:
self.end()
print ("Time: {} mins".format((self.end_time - self.start_time )/ 60)) | def report(self):
"""
Report elapsed time.
"""
if not self.end_time:
self.end()
print ("Time: {} mins".format((self.end_time - self.start_time )/ 60)) | [
"Report",
"elapsed",
"time",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/utils/timer.py#L21-L27 | [
"def",
"report",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"end_time",
":",
"self",
".",
"end",
"(",
")",
"print",
"(",
"\"Time: {} mins\"",
".",
"format",
"(",
"(",
"self",
".",
"end_time",
"-",
"self",
".",
"start_time",
")",
"/",
"60",
")... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | TrainingValidator.compare | Compare to previous records and return whether the given cost is a new best.
:return: True if the given cost is a new best | deepy/trainers/controllers.py | def compare(self, cost_map):
"""
Compare to previous records and return whether the given cost is a new best.
:return: True if the given cost is a new best
"""
cri_val = cost_map[self._criteria]
if self._best_criteria is None:
self._best_criteria = cri_val
... | def compare(self, cost_map):
"""
Compare to previous records and return whether the given cost is a new best.
:return: True if the given cost is a new best
"""
cri_val = cost_map[self._criteria]
if self._best_criteria is None:
self._best_criteria = cri_val
... | [
"Compare",
"to",
"previous",
"records",
"and",
"return",
"whether",
"the",
"given",
"cost",
"is",
"a",
"new",
"best",
".",
":",
"return",
":",
"True",
"if",
"the",
"given",
"cost",
"is",
"a",
"new",
"best"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/controllers.py#L42-L59 | [
"def",
"compare",
"(",
"self",
",",
"cost_map",
")",
":",
"cri_val",
"=",
"cost_map",
"[",
"self",
".",
"_criteria",
"]",
"if",
"self",
".",
"_best_criteria",
"is",
"None",
":",
"self",
".",
"_best_criteria",
"=",
"cri_val",
"return",
"True",
"else",
":"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | TrainingValidator.run | Run the model with validation data and return costs. | deepy/trainers/controllers.py | def run(self, data_x):
"""
Run the model with validation data and return costs.
"""
output_vars = self.compute(*data_x)
return self._extract_costs(output_vars) | def run(self, data_x):
"""
Run the model with validation data and return costs.
"""
output_vars = self.compute(*data_x)
return self._extract_costs(output_vars) | [
"Run",
"the",
"model",
"with",
"validation",
"data",
"and",
"return",
"costs",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/controllers.py#L79-L84 | [
"def",
"run",
"(",
"self",
",",
"data_x",
")",
":",
"output_vars",
"=",
"self",
".",
"compute",
"(",
"*",
"data_x",
")",
"return",
"self",
".",
"_extract_costs",
"(",
"output_vars",
")"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | TrainingValidator.invoke | This function will be called after each iteration. | deepy/trainers/controllers.py | def invoke(self):
"""
This function will be called after each iteration.
"""
self._counter += 1
if self._counter % self._freq == 0:
cnt = 0.
sum_map = defaultdict(float)
for x in self._trainer.get_data(self._data_split):
val_map... | def invoke(self):
"""
This function will be called after each iteration.
"""
self._counter += 1
if self._counter % self._freq == 0:
cnt = 0.
sum_map = defaultdict(float)
for x in self._trainer.get_data(self._data_split):
val_map... | [
"This",
"function",
"will",
"be",
"called",
"after",
"each",
"iteration",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/controllers.py#L86-L106 | [
"def",
"invoke",
"(",
"self",
")",
":",
"self",
".",
"_counter",
"+=",
"1",
"if",
"self",
".",
"_counter",
"%",
"self",
".",
"_freq",
"==",
"0",
":",
"cnt",
"=",
"0.",
"sum_map",
"=",
"defaultdict",
"(",
"float",
")",
"for",
"x",
"in",
"self",
".... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Loop._build_loop_vars | Create inner loop variables. | deepy/core/loop.py | def _build_loop_vars(self):
"""
Create inner loop variables.
"""
from theano.tensor.var import TensorVariable
from deepy.core.neural_var import NeuralVariable
if not self._loop_vars:
self._ordered_out_keys = self._outputs.keys()
seq_keys = self._se... | def _build_loop_vars(self):
"""
Create inner loop variables.
"""
from theano.tensor.var import TensorVariable
from deepy.core.neural_var import NeuralVariable
if not self._loop_vars:
self._ordered_out_keys = self._outputs.keys()
seq_keys = self._se... | [
"Create",
"inner",
"loop",
"variables",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/loop.py#L32-L56 | [
"def",
"_build_loop_vars",
"(",
"self",
")",
":",
"from",
"theano",
".",
"tensor",
".",
"var",
"import",
"TensorVariable",
"from",
"deepy",
".",
"core",
".",
"neural_var",
"import",
"NeuralVariable",
"if",
"not",
"self",
".",
"_loop_vars",
":",
"self",
".",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Loop._scan_step | Internal scan with dummy input variables. | deepy/core/loop.py | def _scan_step(self, vars):
"""
Internal scan with dummy input variables.
"""
from neural_var import NeuralVariable
if not self._loop_vars:
raise Exception("The loop is not initialized. To initialize the loop, use `with loop as vars`")
replace_map = {}
... | def _scan_step(self, vars):
"""
Internal scan with dummy input variables.
"""
from neural_var import NeuralVariable
if not self._loop_vars:
raise Exception("The loop is not initialized. To initialize the loop, use `with loop as vars`")
replace_map = {}
... | [
"Internal",
"scan",
"with",
"dummy",
"input",
"variables",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/loop.py#L86-L103 | [
"def",
"_scan_step",
"(",
"self",
",",
"vars",
")",
":",
"from",
"neural_var",
"import",
"NeuralVariable",
"if",
"not",
"self",
".",
"_loop_vars",
":",
"raise",
"Exception",
"(",
"\"The loop is not initialized. To initialize the loop, use `with loop as vars`\"",
")",
"r... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Loop.get_outputs | Get the outputs of the loop.
Return specific variables by passing the keys to the arguments.
:rtype: MapDict | deepy/core/loop.py | def get_outputs(self, *args):
"""
Get the outputs of the loop.
Return specific variables by passing the keys to the arguments.
:rtype: MapDict
"""
if args:
output_vars = map(self._scan_outputs.get, args)
if len(output_vars) == 1:
re... | def get_outputs(self, *args):
"""
Get the outputs of the loop.
Return specific variables by passing the keys to the arguments.
:rtype: MapDict
"""
if args:
output_vars = map(self._scan_outputs.get, args)
if len(output_vars) == 1:
re... | [
"Get",
"the",
"outputs",
"of",
"the",
"loop",
".",
"Return",
"specific",
"variables",
"by",
"passing",
"the",
"keys",
"to",
"the",
"arguments",
".",
":",
"rtype",
":",
"MapDict"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/loop.py#L116-L129 | [
"def",
"get_outputs",
"(",
"self",
",",
"*",
"args",
")",
":",
"if",
"args",
":",
"output_vars",
"=",
"map",
"(",
"self",
".",
"_scan_outputs",
".",
"get",
",",
"args",
")",
"if",
"len",
"(",
"output_vars",
")",
"==",
"1",
":",
"return",
"output_vars... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | momentum_core | Momentum SGD optimization core. | deepy/trainers/cores/momentum.py | def momentum_core(params, gradients, momentum=0.9, learning_rate=0.01):
"""
Momentum SGD optimization core.
"""
free_parameters = []
updates = []
for param, grad in zip(params, gradients):
delta = learning_rate * grad
velocity = theano.shared(np.zeros_like(param.get_value... | def momentum_core(params, gradients, momentum=0.9, learning_rate=0.01):
"""
Momentum SGD optimization core.
"""
free_parameters = []
updates = []
for param, grad in zip(params, gradients):
delta = learning_rate * grad
velocity = theano.shared(np.zeros_like(param.get_value... | [
"Momentum",
"SGD",
"optimization",
"core",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/cores/momentum.py#L7-L19 | [
"def",
"momentum_core",
"(",
"params",
",",
"gradients",
",",
"momentum",
"=",
"0.9",
",",
"learning_rate",
"=",
"0.01",
")",
":",
"free_parameters",
"=",
"[",
"]",
"updates",
"=",
"[",
"]",
"for",
"param",
",",
"grad",
"in",
"zip",
"(",
"params",
",",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Runtime.iftrain | Execute `then_branch` when training. | deepy/core/runtime.py | def iftrain(self, then_branch, else_branch):
"""
Execute `then_branch` when training.
"""
return ifelse(self._training_flag, then_branch, else_branch, name="iftrain") | def iftrain(self, then_branch, else_branch):
"""
Execute `then_branch` when training.
"""
return ifelse(self._training_flag, then_branch, else_branch, name="iftrain") | [
"Execute",
"then_branch",
"when",
"training",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/runtime.py#L20-L24 | [
"def",
"iftrain",
"(",
"self",
",",
"then_branch",
",",
"else_branch",
")",
":",
"return",
"ifelse",
"(",
"self",
".",
"_training_flag",
",",
"then_branch",
",",
"else_branch",
",",
"name",
"=",
"\"iftrain\"",
")"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Runtime.switch_training | Switch training mode.
:param flag: switch on training mode when flag is True. | deepy/core/runtime.py | def switch_training(self, flag):
"""
Switch training mode.
:param flag: switch on training mode when flag is True.
"""
if self._is_training == flag: return
self._is_training = flag
if flag:
self._training_flag.set_value(1)
else:
sel... | def switch_training(self, flag):
"""
Switch training mode.
:param flag: switch on training mode when flag is True.
"""
if self._is_training == flag: return
self._is_training = flag
if flag:
self._training_flag.set_value(1)
else:
sel... | [
"Switch",
"training",
"mode",
".",
":",
"param",
"flag",
":",
"switch",
"on",
"training",
"mode",
"when",
"flag",
"is",
"True",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/runtime.py#L26-L36 | [
"def",
"switch_training",
"(",
"self",
",",
"flag",
")",
":",
"if",
"self",
".",
"_is_training",
"==",
"flag",
":",
"return",
"self",
".",
"_is_training",
"=",
"flag",
"if",
"flag",
":",
"self",
".",
"_training_flag",
".",
"set_value",
"(",
"1",
")",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | nag_core | Nesterov's Accelerated Gradient (NAG).
See http://www.cs.toronto.edu/~fritz/absps/momentum.pdf .
Still unfinished | deepy/trainers/cores/nag.py | def nag_core(params, J, momentum=0.9, learning_rate=0.01):
"""
Nesterov's Accelerated Gradient (NAG).
See http://www.cs.toronto.edu/~fritz/absps/momentum.pdf .
Still unfinished
"""
# TODO: this requires some refractorings.
for param in params:
step = theano.shared(np.zeros_like(param... | def nag_core(params, J, momentum=0.9, learning_rate=0.01):
"""
Nesterov's Accelerated Gradient (NAG).
See http://www.cs.toronto.edu/~fritz/absps/momentum.pdf .
Still unfinished
"""
# TODO: this requires some refractorings.
for param in params:
step = theano.shared(np.zeros_like(param... | [
"Nesterov",
"s",
"Accelerated",
"Gradient",
"(",
"NAG",
")",
".",
"See",
"http",
":",
"//",
"www",
".",
"cs",
".",
"toronto",
".",
"edu",
"/",
"~fritz",
"/",
"absps",
"/",
"momentum",
".",
"pdf",
".",
"Still",
"unfinished"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/cores/nag.py#L8-L21 | [
"def",
"nag_core",
"(",
"params",
",",
"J",
",",
"momentum",
"=",
"0.9",
",",
"learning_rate",
"=",
"0.01",
")",
":",
"# TODO: this requires some refractorings.",
"for",
"param",
"in",
"params",
":",
"step",
"=",
"theano",
".",
"shared",
"(",
"np",
".",
"z... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.skip | Skip N batches in the training. | deepy/trainers/base.py | def skip(self, n_batches, n_epochs=0):
"""
Skip N batches in the training.
"""
logging.info("skip %d epochs and %d batches" % (n_epochs, n_batches))
self._skip_batches = n_batches
self._skip_epochs = n_epochs | def skip(self, n_batches, n_epochs=0):
"""
Skip N batches in the training.
"""
logging.info("skip %d epochs and %d batches" % (n_epochs, n_batches))
self._skip_batches = n_batches
self._skip_epochs = n_epochs | [
"Skip",
"N",
"batches",
"in",
"the",
"training",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L90-L96 | [
"def",
"skip",
"(",
"self",
",",
"n_batches",
",",
"n_epochs",
"=",
"0",
")",
":",
"logging",
".",
"info",
"(",
"\"skip %d epochs and %d batches\"",
"%",
"(",
"n_epochs",
",",
"n_batches",
")",
")",
"self",
".",
"_skip_batches",
"=",
"n_batches",
"self",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.load_params | Load parameters for the training.
This method can load free parameters and resume the training progress. | deepy/trainers/base.py | def load_params(self, path, exclude_free_params=False):
"""
Load parameters for the training.
This method can load free parameters and resume the training progress.
"""
self.network.load_params(path, exclude_free_params=exclude_free_params)
self.best_params = self.copy_pa... | def load_params(self, path, exclude_free_params=False):
"""
Load parameters for the training.
This method can load free parameters and resume the training progress.
"""
self.network.load_params(path, exclude_free_params=exclude_free_params)
self.best_params = self.copy_pa... | [
"Load",
"parameters",
"for",
"the",
"training",
".",
"This",
"method",
"can",
"load",
"free",
"parameters",
"and",
"resume",
"the",
"training",
"progress",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L144-L153 | [
"def",
"load_params",
"(",
"self",
",",
"path",
",",
"exclude_free_params",
"=",
"False",
")",
":",
"self",
".",
"network",
".",
"load_params",
"(",
"path",
",",
"exclude_free_params",
"=",
"exclude_free_params",
")",
"self",
".",
"best_params",
"=",
"self",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.add_iter_controllers | Add iteration callbacks function (receives an argument of the trainer).
:param controllers: can be a `TrainingController` or a function.
:type funcs: list of TrainingContoller | deepy/trainers/base.py | def add_iter_controllers(self, *controllers):
"""
Add iteration callbacks function (receives an argument of the trainer).
:param controllers: can be a `TrainingController` or a function.
:type funcs: list of TrainingContoller
"""
for controller in controllers:
... | def add_iter_controllers(self, *controllers):
"""
Add iteration callbacks function (receives an argument of the trainer).
:param controllers: can be a `TrainingController` or a function.
:type funcs: list of TrainingContoller
"""
for controller in controllers:
... | [
"Add",
"iteration",
"callbacks",
"function",
"(",
"receives",
"an",
"argument",
"of",
"the",
"trainer",
")",
".",
":",
"param",
"controllers",
":",
"can",
"be",
"a",
"TrainingController",
"or",
"a",
"function",
".",
":",
"type",
"funcs",
":",
"list",
"of",... | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L166-L175 | [
"def",
"add_iter_controllers",
"(",
"self",
",",
"*",
"controllers",
")",
":",
"for",
"controller",
"in",
"controllers",
":",
"if",
"isinstance",
"(",
"controller",
",",
"TrainingController",
")",
":",
"controller",
".",
"bind",
"(",
"self",
")",
"self",
"."... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.add_epoch_controllers | Add epoch callbacks function.
:param controllers: can be a `TrainingController` or a function. | deepy/trainers/base.py | def add_epoch_controllers(self, *controllers):
"""
Add epoch callbacks function.
:param controllers: can be a `TrainingController` or a function.
"""
for controller in controllers:
if isinstance(controller, TrainingController):
controller.bind(self)
... | def add_epoch_controllers(self, *controllers):
"""
Add epoch callbacks function.
:param controllers: can be a `TrainingController` or a function.
"""
for controller in controllers:
if isinstance(controller, TrainingController):
controller.bind(self)
... | [
"Add",
"epoch",
"callbacks",
"function",
".",
":",
"param",
"controllers",
":",
"can",
"be",
"a",
"TrainingController",
"or",
"a",
"function",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L177-L185 | [
"def",
"add_epoch_controllers",
"(",
"self",
",",
"*",
"controllers",
")",
":",
"for",
"controller",
"in",
"controllers",
":",
"if",
"isinstance",
"(",
"controller",
",",
"TrainingController",
")",
":",
"controller",
".",
"bind",
"(",
"self",
")",
"self",
".... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.train | Train the model and return costs. | deepy/trainers/base.py | def train(self, train_set, valid_set=None, test_set=None, train_size=None):
"""
Train the model and return costs.
"""
self._epoch = 0
while True:
if self._skip_epochs > 0:
logging.info("skipping one epoch ...")
self._skip_epochs -= 1
... | def train(self, train_set, valid_set=None, test_set=None, train_size=None):
"""
Train the model and return costs.
"""
self._epoch = 0
while True:
if self._skip_epochs > 0:
logging.info("skipping one epoch ...")
self._skip_epochs -= 1
... | [
"Train",
"the",
"model",
"and",
"return",
"costs",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L187-L236 | [
"def",
"train",
"(",
"self",
",",
"train_set",
",",
"valid_set",
"=",
"None",
",",
"test_set",
"=",
"None",
",",
"train_size",
"=",
"None",
")",
":",
"self",
".",
"_epoch",
"=",
"0",
"while",
"True",
":",
"if",
"self",
".",
"_skip_epochs",
">",
"0",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer._run_train | Run one training iteration. | deepy/trainers/base.py | def _run_train(self, epoch, train_set, train_size=None):
"""
Run one training iteration.
"""
self.network.train_logger.record_epoch(epoch + 1)
costs = self.train_step(train_set, train_size)
if not epoch % self.config.monitor_frequency:
self.report(dict(costs),... | def _run_train(self, epoch, train_set, train_size=None):
"""
Run one training iteration.
"""
self.network.train_logger.record_epoch(epoch + 1)
costs = self.train_step(train_set, train_size)
if not epoch % self.config.monitor_frequency:
self.report(dict(costs),... | [
"Run",
"one",
"training",
"iteration",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L254-L263 | [
"def",
"_run_train",
"(",
"self",
",",
"epoch",
",",
"train_set",
",",
"train_size",
"=",
"None",
")",
":",
"self",
".",
"network",
".",
"train_logger",
".",
"record_epoch",
"(",
"epoch",
"+",
"1",
")",
"costs",
"=",
"self",
".",
"train_step",
"(",
"tr... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer._run_valid | Run one valid iteration, return true if to continue training. | deepy/trainers/base.py | def _run_valid(self, epoch, valid_set, dry_run=False, save_path=None):
"""
Run one valid iteration, return true if to continue training.
"""
costs = self.valid_step(valid_set)
# this is the same as: (J_i - J_f) / J_i > min improvement
_, J = costs[0]
new_best = Fa... | def _run_valid(self, epoch, valid_set, dry_run=False, save_path=None):
"""
Run one valid iteration, return true if to continue training.
"""
costs = self.valid_step(valid_set)
# this is the same as: (J_i - J_f) / J_i > min improvement
_, J = costs[0]
new_best = Fa... | [
"Run",
"one",
"valid",
"iteration",
"return",
"true",
"if",
"to",
"continue",
"training",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L265-L284 | [
"def",
"_run_valid",
"(",
"self",
",",
"epoch",
",",
"valid_set",
",",
"dry_run",
"=",
"False",
",",
"save_path",
"=",
"None",
")",
":",
"costs",
"=",
"self",
".",
"valid_step",
"(",
"valid_set",
")",
"# this is the same as: (J_i - J_f) / J_i > min improvement",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.report | Report the scores and record them in the log. | deepy/trainers/base.py | def report(self, score_map, type="valid", epoch=-1, new_best=False):
"""
Report the scores and record them in the log.
"""
type_str = type
if len(type_str) < 5:
type_str += " " * (5 - len(type_str))
info = " ".join("%s=%.2f" % el for el in score_map.items())
... | def report(self, score_map, type="valid", epoch=-1, new_best=False):
"""
Report the scores and record them in the log.
"""
type_str = type
if len(type_str) < 5:
type_str += " " * (5 - len(type_str))
info = " ".join("%s=%.2f" % el for el in score_map.items())
... | [
"Report",
"the",
"scores",
"and",
"record",
"them",
"in",
"the",
"log",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L293-L310 | [
"def",
"report",
"(",
"self",
",",
"score_map",
",",
"type",
"=",
"\"valid\"",
",",
"epoch",
"=",
"-",
"1",
",",
"new_best",
"=",
"False",
")",
":",
"type_str",
"=",
"type",
"if",
"len",
"(",
"type_str",
")",
"<",
"5",
":",
"type_str",
"+=",
"\" \"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.get_data | Get specified split of data. | deepy/trainers/base.py | def get_data(self, data_split="train"):
"""
Get specified split of data.
"""
if data_split == 'train':
return self._current_train_set
elif data_split == 'valid':
return self._current_valid_set
elif data_split == 'test':
return self._cur... | def get_data(self, data_split="train"):
"""
Get specified split of data.
"""
if data_split == 'train':
return self._current_train_set
elif data_split == 'valid':
return self._current_valid_set
elif data_split == 'test':
return self._cur... | [
"Get",
"specified",
"split",
"of",
"data",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L390-L401 | [
"def",
"get_data",
"(",
"self",
",",
"data_split",
"=",
"\"train\"",
")",
":",
"if",
"data_split",
"==",
"'train'",
":",
"return",
"self",
".",
"_current_train_set",
"elif",
"data_split",
"==",
"'valid'",
":",
"return",
"self",
".",
"_current_valid_set",
"elif... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralTrainer.run | Run until the end.
:param epoch_controllers: deprecated | deepy/trainers/base.py | def run(self, train_set, valid_set=None, test_set=None, train_size=None, epoch_controllers=None):
"""
Run until the end.
:param epoch_controllers: deprecated
"""
epoch_controllers = epoch_controllers if epoch_controllers else []
epoch_controllers += self._epoch_controller... | def run(self, train_set, valid_set=None, test_set=None, train_size=None, epoch_controllers=None):
"""
Run until the end.
:param epoch_controllers: deprecated
"""
epoch_controllers = epoch_controllers if epoch_controllers else []
epoch_controllers += self._epoch_controller... | [
"Run",
"until",
"the",
"end",
".",
":",
"param",
"epoch_controllers",
":",
"deprecated"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/base.py#L403-L430 | [
"def",
"run",
"(",
"self",
",",
"train_set",
",",
"valid_set",
"=",
"None",
",",
"test_set",
"=",
"None",
",",
"train_size",
"=",
"None",
",",
"epoch_controllers",
"=",
"None",
")",
":",
"epoch_controllers",
"=",
"epoch_controllers",
"if",
"epoch_controllers",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | BunchSequences._cut_to_pieces | :type bunch_stack: list of list of int | deepy/dataset/bunch_seq.py | def _cut_to_pieces(self, bunch_stack):
"""
:type bunch_stack: list of list of int
"""
stack_len = len(bunch_stack[0])
for i in xrange(0, stack_len, self.fragment_length):
yield np.array(map(lambda stack: stack[i: i + self.fragment_length], bunch_stack)) | def _cut_to_pieces(self, bunch_stack):
"""
:type bunch_stack: list of list of int
"""
stack_len = len(bunch_stack[0])
for i in xrange(0, stack_len, self.fragment_length):
yield np.array(map(lambda stack: stack[i: i + self.fragment_length], bunch_stack)) | [
":",
"type",
"bunch_stack",
":",
"list",
"of",
"list",
"of",
"int"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/bunch_seq.py#L58-L64 | [
"def",
"_cut_to_pieces",
"(",
"self",
",",
"bunch_stack",
")",
":",
"stack_len",
"=",
"len",
"(",
"bunch_stack",
"[",
"0",
"]",
")",
"for",
"i",
"in",
"xrange",
"(",
"0",
",",
"stack_len",
",",
"self",
".",
"fragment_length",
")",
":",
"yield",
"np",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | BunchSequences._pad_zeros | :type bunch_stack: list of list | deepy/dataset/bunch_seq.py | def _pad_zeros(self, bunch_stack):
"""
:type bunch_stack: list of list
"""
min_len = min(map(len, bunch_stack))
for i in range(len(bunch_stack)):
bunch_stack[i] = bunch_stack[i][:min_len] | def _pad_zeros(self, bunch_stack):
"""
:type bunch_stack: list of list
"""
min_len = min(map(len, bunch_stack))
for i in range(len(bunch_stack)):
bunch_stack[i] = bunch_stack[i][:min_len] | [
":",
"type",
"bunch_stack",
":",
"list",
"of",
"list"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/bunch_seq.py#L66-L72 | [
"def",
"_pad_zeros",
"(",
"self",
",",
"bunch_stack",
")",
":",
"min_len",
"=",
"min",
"(",
"map",
"(",
"len",
",",
"bunch_stack",
")",
")",
"for",
"i",
"in",
"range",
"(",
"len",
"(",
"bunch_stack",
")",
")",
":",
"bunch_stack",
"[",
"i",
"]",
"="... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralVariable.apply | Apply a function to tensors. | deepy/core/neural_var.py | def apply(self, func, dim=None):
"""
Apply a function to tensors.
"""
output_dim = dim if dim else self.output_dim
return NeuralVariable(func(self.tensor), output_dim) | def apply(self, func, dim=None):
"""
Apply a function to tensors.
"""
output_dim = dim if dim else self.output_dim
return NeuralVariable(func(self.tensor), output_dim) | [
"Apply",
"a",
"function",
"to",
"tensors",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/neural_var.py#L29-L34 | [
"def",
"apply",
"(",
"self",
",",
"func",
",",
"dim",
"=",
"None",
")",
":",
"output_dim",
"=",
"dim",
"if",
"dim",
"else",
"self",
".",
"output_dim",
"return",
"NeuralVariable",
"(",
"func",
"(",
"self",
".",
"tensor",
")",
",",
"output_dim",
")"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | rprop_core | Rprop optimizer.
See http://sci2s.ugr.es/keel/pdf/algorithm/articulo/2003-Neuro-Igel-IRprop+.pdf. | deepy/trainers/cores/rprop.py | def rprop_core(params, gradients, rprop_increase=1.01, rprop_decrease=0.99, rprop_min_step=0, rprop_max_step=100,
learning_rate=0.01):
"""
Rprop optimizer.
See http://sci2s.ugr.es/keel/pdf/algorithm/articulo/2003-Neuro-Igel-IRprop+.pdf.
"""
for param, grad in zip(params, gradients):
... | def rprop_core(params, gradients, rprop_increase=1.01, rprop_decrease=0.99, rprop_min_step=0, rprop_max_step=100,
learning_rate=0.01):
"""
Rprop optimizer.
See http://sci2s.ugr.es/keel/pdf/algorithm/articulo/2003-Neuro-Igel-IRprop+.pdf.
"""
for param, grad in zip(params, gradients):
... | [
"Rprop",
"optimizer",
".",
"See",
"http",
":",
"//",
"sci2s",
".",
"ugr",
".",
"es",
"/",
"keel",
"/",
"pdf",
"/",
"algorithm",
"/",
"articulo",
"/",
"2003",
"-",
"Neuro",
"-",
"Igel",
"-",
"IRprop",
"+",
".",
"pdf",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/cores/rprop.py#L8-L28 | [
"def",
"rprop_core",
"(",
"params",
",",
"gradients",
",",
"rprop_increase",
"=",
"1.01",
",",
"rprop_decrease",
"=",
"0.99",
",",
"rprop_min_step",
"=",
"0",
",",
"rprop_max_step",
"=",
"100",
",",
"learning_rate",
"=",
"0.01",
")",
":",
"for",
"param",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GeneralConfig.report | Report usage of training parameters. | deepy/conf/config.py | def report(self):
"""
Report usage of training parameters.
"""
if self.logger:
self.logger.info("accessed parameters:")
for key in self.used_parameters:
self.logger.info(" - %s %s" % (key, "(undefined)" if key in self.undefined_parameters else "")) | def report(self):
"""
Report usage of training parameters.
"""
if self.logger:
self.logger.info("accessed parameters:")
for key in self.used_parameters:
self.logger.info(" - %s %s" % (key, "(undefined)" if key in self.undefined_parameters else "")) | [
"Report",
"usage",
"of",
"training",
"parameters",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/conf/config.py#L39-L46 | [
"def",
"report",
"(",
"self",
")",
":",
"if",
"self",
".",
"logger",
":",
"self",
".",
"logger",
".",
"info",
"(",
"\"accessed parameters:\"",
")",
"for",
"key",
"in",
"self",
".",
"used_parameters",
":",
"self",
".",
"logger",
".",
"info",
"(",
"\" - ... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.new_block | Create a parameters block.
:param layers: register some layers in the block
:param name: specify the name of this block | deepy/core/graph.py | def new_block(self, *layers, **kwargs):
"""
Create a parameters block.
:param layers: register some layers in the block
:param name: specify the name of this block
"""
from deepy.layers.block import Block
block = Block(*layers, **kwargs)
return block | def new_block(self, *layers, **kwargs):
"""
Create a parameters block.
:param layers: register some layers in the block
:param name: specify the name of this block
"""
from deepy.layers.block import Block
block = Block(*layers, **kwargs)
return block | [
"Create",
"a",
"parameters",
"block",
".",
":",
"param",
"layers",
":",
"register",
"some",
"layers",
"in",
"the",
"block",
":",
"param",
"name",
":",
"specify",
"the",
"name",
"of",
"this",
"block"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L39-L47 | [
"def",
"new_block",
"(",
"self",
",",
"*",
"layers",
",",
"*",
"*",
"kwargs",
")",
":",
"from",
"deepy",
".",
"layers",
".",
"block",
"import",
"Block",
"block",
"=",
"Block",
"(",
"*",
"layers",
",",
"*",
"*",
"kwargs",
")",
"return",
"block"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.var | An alias of deepy.tensor.var. | deepy/core/graph.py | def var(self, tensor_type, last_dim=0, test_shape=None):
"""
An alias of deepy.tensor.var.
"""
from deepy.tensor import var
return var(tensor_type, last_dim=last_dim, test_shape=test_shape) | def var(self, tensor_type, last_dim=0, test_shape=None):
"""
An alias of deepy.tensor.var.
"""
from deepy.tensor import var
return var(tensor_type, last_dim=last_dim, test_shape=test_shape) | [
"An",
"alias",
"of",
"deepy",
".",
"tensor",
".",
"var",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L49-L54 | [
"def",
"var",
"(",
"self",
",",
"tensor_type",
",",
"last_dim",
"=",
"0",
",",
"test_shape",
"=",
"None",
")",
":",
"from",
"deepy",
".",
"tensor",
"import",
"var",
"return",
"var",
"(",
"tensor_type",
",",
"last_dim",
"=",
"last_dim",
",",
"test_shape",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.create_vars_from_data | Create vars given a dataset and set test values.
Useful when dataset is already defined. | deepy/core/graph.py | def create_vars_from_data(self, dataset, split="train"):
"""
Create vars given a dataset and set test values.
Useful when dataset is already defined.
"""
from deepy.core.neural_var import NeuralVariable
vars = []
if split == "valid":
data_split = datas... | def create_vars_from_data(self, dataset, split="train"):
"""
Create vars given a dataset and set test values.
Useful when dataset is already defined.
"""
from deepy.core.neural_var import NeuralVariable
vars = []
if split == "valid":
data_split = datas... | [
"Create",
"vars",
"given",
"a",
"dataset",
"and",
"set",
"test",
"values",
".",
"Useful",
"when",
"dataset",
"is",
"already",
"defined",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L56-L91 | [
"def",
"create_vars_from_data",
"(",
"self",
",",
"dataset",
",",
"split",
"=",
"\"train\"",
")",
":",
"from",
"deepy",
".",
"core",
".",
"neural_var",
"import",
"NeuralVariable",
"vars",
"=",
"[",
"]",
"if",
"split",
"==",
"\"valid\"",
":",
"data_split",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.scan | A loop function, the usage is identical with the theano one.
:type block: deepy.layers.Block | deepy/core/graph.py | def scan(self, func, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
A loop function, the usage is identical with the theano one.
:type block: deepy.layers.Block
"""
results, updates = Scanner(func, sequences, outputs, non_sequences, neural_computatio... | def scan(self, func, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
A loop function, the usage is identical with the theano one.
:type block: deepy.layers.Block
"""
results, updates = Scanner(func, sequences, outputs, non_sequences, neural_computatio... | [
"A",
"loop",
"function",
"the",
"usage",
"is",
"identical",
"with",
"the",
"theano",
"one",
".",
":",
"type",
"block",
":",
"deepy",
".",
"layers",
".",
"Block"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L94-L104 | [
"def",
"scan",
"(",
"self",
",",
"func",
",",
"sequences",
"=",
"None",
",",
"outputs",
"=",
"None",
",",
"non_sequences",
"=",
"None",
",",
"block",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"results",
",",
"updates",
"=",
"Scanner",
"(",
"f... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.loop | Start a loop.
Usage:
```
with deepy.graph.loop(sequences={"x": x}, outputs={"o": None}) as vars:
vars.o = vars.x + 1
loop_outputs = deepy.graph.loop_outputs()
result = loop_outputs.o
``` | deepy/core/graph.py | def loop(self, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
Start a loop.
Usage:
```
with deepy.graph.loop(sequences={"x": x}, outputs={"o": None}) as vars:
vars.o = vars.x + 1
loop_outputs = deepy.graph.loop_outputs()
r... | def loop(self, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
Start a loop.
Usage:
```
with deepy.graph.loop(sequences={"x": x}, outputs={"o": None}) as vars:
vars.o = vars.x + 1
loop_outputs = deepy.graph.loop_outputs()
r... | [
"Start",
"a",
"loop",
".",
"Usage",
":",
"with",
"deepy",
".",
"graph",
".",
"loop",
"(",
"sequences",
"=",
"{",
"x",
":",
"x",
"}",
"outputs",
"=",
"{",
"o",
":",
"None",
"}",
")",
"as",
"vars",
":",
"vars",
".",
"o",
"=",
"vars",
".",
"x",
... | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L106-L118 | [
"def",
"loop",
"(",
"self",
",",
"sequences",
"=",
"None",
",",
"outputs",
"=",
"None",
",",
"non_sequences",
"=",
"None",
",",
"block",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"from",
"loop",
"import",
"Loop",
"return",
"Loop",
"(",
"sequenc... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.get_trainer | Get a trainer to optimize given model.
:rtype: deepy.trainers.GeneralNeuralTrainer | deepy/core/graph.py | def get_trainer(self, model, method='sgd', config=None, annealer=None, validator=None):
"""
Get a trainer to optimize given model.
:rtype: deepy.trainers.GeneralNeuralTrainer
"""
from deepy.trainers import GeneralNeuralTrainer
return GeneralNeuralTrainer(model, method=me... | def get_trainer(self, model, method='sgd', config=None, annealer=None, validator=None):
"""
Get a trainer to optimize given model.
:rtype: deepy.trainers.GeneralNeuralTrainer
"""
from deepy.trainers import GeneralNeuralTrainer
return GeneralNeuralTrainer(model, method=me... | [
"Get",
"a",
"trainer",
"to",
"optimize",
"given",
"model",
".",
":",
"rtype",
":",
"deepy",
".",
"trainers",
".",
"GeneralNeuralTrainer"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L120-L126 | [
"def",
"get_trainer",
"(",
"self",
",",
"model",
",",
"method",
"=",
"'sgd'",
",",
"config",
"=",
"None",
",",
"annealer",
"=",
"None",
",",
"validator",
"=",
"None",
")",
":",
"from",
"deepy",
".",
"trainers",
"import",
"GeneralNeuralTrainer",
"return",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.shared | Create a shared theano scalar value. | deepy/core/graph.py | def shared(self, value, name=None):
"""
Create a shared theano scalar value.
"""
if type(value) == int:
final_value = np.array(value, dtype="int32")
elif type(value) == float:
final_value = np.array(value, dtype=env.FLOATX)
else:
final_... | def shared(self, value, name=None):
"""
Create a shared theano scalar value.
"""
if type(value) == int:
final_value = np.array(value, dtype="int32")
elif type(value) == float:
final_value = np.array(value, dtype=env.FLOATX)
else:
final_... | [
"Create",
"a",
"shared",
"theano",
"scalar",
"value",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L129-L140 | [
"def",
"shared",
"(",
"self",
",",
"value",
",",
"name",
"=",
"None",
")",
":",
"if",
"type",
"(",
"value",
")",
"==",
"int",
":",
"final_value",
"=",
"np",
".",
"array",
"(",
"value",
",",
"dtype",
"=",
"\"int32\"",
")",
"elif",
"type",
"(",
"va... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | GraphBuilder.fill_parameters | Load parameters from file to fill all blocks sequentially.
:type blocks: list of deepy.layers.Block | deepy/core/graph.py | def fill_parameters(self, path, blocks, exclude_free_params=False, check_parameters=False):
"""
Load parameters from file to fill all blocks sequentially.
:type blocks: list of deepy.layers.Block
"""
if not os.path.exists(path):
raise Exception("model {} does not exis... | def fill_parameters(self, path, blocks, exclude_free_params=False, check_parameters=False):
"""
Load parameters from file to fill all blocks sequentially.
:type blocks: list of deepy.layers.Block
"""
if not os.path.exists(path):
raise Exception("model {} does not exis... | [
"Load",
"parameters",
"from",
"file",
"to",
"fill",
"all",
"blocks",
"sequentially",
".",
":",
"type",
"blocks",
":",
"list",
"of",
"deepy",
".",
"layers",
".",
"Block"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/core/graph.py#L158-L194 | [
"def",
"fill_parameters",
"(",
"self",
",",
"path",
",",
"blocks",
",",
"exclude_free_params",
"=",
"False",
",",
"check_parameters",
"=",
"False",
")",
":",
"if",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"path",
")",
":",
"raise",
"Exception",
"(",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Dataset.train_size | Return size of training data. (optional)
:rtype: number | deepy/dataset/dataset.py | def train_size(self):
"""
Return size of training data. (optional)
:rtype: number
"""
train_set = self.train_set()
if isinstance(train_set, collections.Iterable):
return len(list(train_set))
else:
return None | def train_size(self):
"""
Return size of training data. (optional)
:rtype: number
"""
train_set = self.train_set()
if isinstance(train_set, collections.Iterable):
return len(list(train_set))
else:
return None | [
"Return",
"size",
"of",
"training",
"data",
".",
"(",
"optional",
")",
":",
"rtype",
":",
"number"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/dataset/dataset.py#L29-L38 | [
"def",
"train_size",
"(",
"self",
")",
":",
"train_set",
"=",
"self",
".",
"train_set",
"(",
")",
"if",
"isinstance",
"(",
"train_set",
",",
"collections",
".",
"Iterable",
")",
":",
"return",
"len",
"(",
"list",
"(",
"train_set",
")",
")",
"else",
":"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | LearningRateAnnealer.invoke | Run it, return whether to end training. | deepy/trainers/annealers.py | def invoke(self):
"""
Run it, return whether to end training.
"""
self._iter += 1
if self._iter - max(self._trainer.best_iter, self._annealed_iter) >= self._patience:
if self._annealed_times >= self._anneal_times:
logging.info("ending")
... | def invoke(self):
"""
Run it, return whether to end training.
"""
self._iter += 1
if self._iter - max(self._trainer.best_iter, self._annealed_iter) >= self._patience:
if self._annealed_times >= self._anneal_times:
logging.info("ending")
... | [
"Run",
"it",
"return",
"whether",
"to",
"end",
"training",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/annealers.py#L37-L51 | [
"def",
"invoke",
"(",
"self",
")",
":",
"self",
".",
"_iter",
"+=",
"1",
"if",
"self",
".",
"_iter",
"-",
"max",
"(",
"self",
".",
"_trainer",
".",
"best_iter",
",",
"self",
".",
"_annealed_iter",
")",
">=",
"self",
".",
"_patience",
":",
"if",
"se... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | SimpleScheduler.invoke | Run it, return whether to end training. | deepy/trainers/annealers.py | def invoke(self):
"""
Run it, return whether to end training.
"""
self._iter += 1
logging.info("{} epochs left to run".format(self._patience - self._iter))
if self._iter >= self._patience:
self._trainer.exit() | def invoke(self):
"""
Run it, return whether to end training.
"""
self._iter += 1
logging.info("{} epochs left to run".format(self._patience - self._iter))
if self._iter >= self._patience:
self._trainer.exit() | [
"Run",
"it",
"return",
"whether",
"to",
"end",
"training",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/annealers.py#L131-L138 | [
"def",
"invoke",
"(",
"self",
")",
":",
"self",
".",
"_iter",
"+=",
"1",
"logging",
".",
"info",
"(",
"\"{} epochs left to run\"",
".",
"format",
"(",
"self",
".",
"_patience",
"-",
"self",
".",
"_iter",
")",
")",
"if",
"self",
".",
"_iter",
">=",
"s... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | VariationalAutoEncoder.stack_reparameterization_layer | Perform reparameterization trick for latent variables.
:param layer_size: the size of latent variable | examples/variational_autoencoder/variational_autoencoder.py | def stack_reparameterization_layer(self, layer_size):
"""
Perform reparameterization trick for latent variables.
:param layer_size: the size of latent variable
"""
self.rep_layer = ReparameterizationLayer(layer_size, sample=self.sample)
self.stack_encoders(self.rep_layer) | def stack_reparameterization_layer(self, layer_size):
"""
Perform reparameterization trick for latent variables.
:param layer_size: the size of latent variable
"""
self.rep_layer = ReparameterizationLayer(layer_size, sample=self.sample)
self.stack_encoders(self.rep_layer) | [
"Perform",
"reparameterization",
"trick",
"for",
"latent",
"variables",
".",
":",
"param",
"layer_size",
":",
"the",
"size",
"of",
"latent",
"variable"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/examples/variational_autoencoder/variational_autoencoder.py#L69-L75 | [
"def",
"stack_reparameterization_layer",
"(",
"self",
",",
"layer_size",
")",
":",
"self",
".",
"rep_layer",
"=",
"ReparameterizationLayer",
"(",
"layer_size",
",",
"sample",
"=",
"self",
".",
"sample",
")",
"self",
".",
"stack_encoders",
"(",
"self",
".",
"re... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AutoEncoder.stack_encoders | Stack encoding layers, this must be done before stacking decoding layers. | deepy/networks/auto_encoder.py | def stack_encoders(self, *layers):
"""
Stack encoding layers, this must be done before stacking decoding layers.
"""
self.stack(*layers)
self.encoding_layes.extend(layers) | def stack_encoders(self, *layers):
"""
Stack encoding layers, this must be done before stacking decoding layers.
"""
self.stack(*layers)
self.encoding_layes.extend(layers) | [
"Stack",
"encoding",
"layers",
"this",
"must",
"be",
"done",
"before",
"stacking",
"decoding",
"layers",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/auto_encoder.py#L41-L46 | [
"def",
"stack_encoders",
"(",
"self",
",",
"*",
"layers",
")",
":",
"self",
".",
"stack",
"(",
"*",
"layers",
")",
"self",
".",
"encoding_layes",
".",
"extend",
"(",
"layers",
")"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AutoEncoder.stack_decoders | Stack decoding layers. | deepy/networks/auto_encoder.py | def stack_decoders(self, *layers):
"""
Stack decoding layers.
"""
self.stack(*layers)
self.decoding_layers.extend(layers) | def stack_decoders(self, *layers):
"""
Stack decoding layers.
"""
self.stack(*layers)
self.decoding_layers.extend(layers) | [
"Stack",
"decoding",
"layers",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/auto_encoder.py#L48-L53 | [
"def",
"stack_decoders",
"(",
"self",
",",
"*",
"layers",
")",
":",
"self",
".",
"stack",
"(",
"*",
"layers",
")",
"self",
".",
"decoding_layers",
".",
"extend",
"(",
"layers",
")"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AutoEncoder.encode | Encode given input. | deepy/networks/auto_encoder.py | def encode(self, x):
"""
Encode given input.
"""
if not self.encoding_network:
self.encoding_network = NeuralNetwork(self.input_dim, self.input_tensor)
self.encoding_network.input_variables = self.input_variables
for layer in self.encoding_layes:
... | def encode(self, x):
"""
Encode given input.
"""
if not self.encoding_network:
self.encoding_network = NeuralNetwork(self.input_dim, self.input_tensor)
self.encoding_network.input_variables = self.input_variables
for layer in self.encoding_layes:
... | [
"Encode",
"given",
"input",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/auto_encoder.py#L55-L64 | [
"def",
"encode",
"(",
"self",
",",
"x",
")",
":",
"if",
"not",
"self",
".",
"encoding_network",
":",
"self",
".",
"encoding_network",
"=",
"NeuralNetwork",
"(",
"self",
".",
"input_dim",
",",
"self",
".",
"input_tensor",
")",
"self",
".",
"encoding_network... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | AutoEncoder.decode | Decode given representation. | deepy/networks/auto_encoder.py | def decode(self, x):
"""
Decode given representation.
"""
if not self.rep_dim:
raise Exception("rep_dim must be set to decode.")
if not self.decoding_network:
self.decoding_network = NeuralNetwork(self.rep_dim)
for layer in self.decoding_layers... | def decode(self, x):
"""
Decode given representation.
"""
if not self.rep_dim:
raise Exception("rep_dim must be set to decode.")
if not self.decoding_network:
self.decoding_network = NeuralNetwork(self.rep_dim)
for layer in self.decoding_layers... | [
"Decode",
"given",
"representation",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/auto_encoder.py#L66-L76 | [
"def",
"decode",
"(",
"self",
",",
"x",
")",
":",
"if",
"not",
"self",
".",
"rep_dim",
":",
"raise",
"Exception",
"(",
"\"rep_dim must be set to decode.\"",
")",
"if",
"not",
"self",
".",
"decoding_network",
":",
"self",
".",
"decoding_network",
"=",
"Neural... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | create_2d_gaussian | This function creates a 2d gaussian kernel with the standard deviation
denoted by sigma
:param dim: integer denoting a side (1-d) of gaussian kernel
:param sigma: floating point indicating the standard deviation
:returns: a numpy 2d array | deepy/preprocessing/elastic_distortion.py | def create_2d_gaussian(dim, sigma):
"""
This function creates a 2d gaussian kernel with the standard deviation
denoted by sigma
:param dim: integer denoting a side (1-d) of gaussian kernel
:param sigma: floating point indicating the standard deviation
:returns: a numpy 2d array
"""
# ... | def create_2d_gaussian(dim, sigma):
"""
This function creates a 2d gaussian kernel with the standard deviation
denoted by sigma
:param dim: integer denoting a side (1-d) of gaussian kernel
:param sigma: floating point indicating the standard deviation
:returns: a numpy 2d array
"""
# ... | [
"This",
"function",
"creates",
"a",
"2d",
"gaussian",
"kernel",
"with",
"the",
"standard",
"deviation",
"denoted",
"by",
"sigma"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/preprocessing/elastic_distortion.py#L17-L54 | [
"def",
"create_2d_gaussian",
"(",
"dim",
",",
"sigma",
")",
":",
"# check if the dimension is odd",
"if",
"dim",
"%",
"2",
"==",
"0",
":",
"raise",
"ValueError",
"(",
"\"Kernel dimension should be odd\"",
")",
"# initialize the kernel",
"kernel",
"=",
"np",
".",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | elastic_distortion | This method performs elastic transformations on an image by convolving
with a gaussian kernel.
:param image: a numpy nd array
:kernel_dim: dimension(1-D) of the gaussian kernel
:param sigma: standard deviation of the kernel
:param alpha: a multiplicative factor for image after convolution
:param... | deepy/preprocessing/elastic_distortion.py | def elastic_distortion(image, kernel_dim=21, sigma=6, alpha=30, negated=True):
"""
This method performs elastic transformations on an image by convolving
with a gaussian kernel.
:param image: a numpy nd array
:kernel_dim: dimension(1-D) of the gaussian kernel
:param sigma: standard deviation of ... | def elastic_distortion(image, kernel_dim=21, sigma=6, alpha=30, negated=True):
"""
This method performs elastic transformations on an image by convolving
with a gaussian kernel.
:param image: a numpy nd array
:kernel_dim: dimension(1-D) of the gaussian kernel
:param sigma: standard deviation of ... | [
"This",
"method",
"performs",
"elastic",
"transformations",
"on",
"an",
"image",
"by",
"convolving",
"with",
"a",
"gaussian",
"kernel",
".",
":",
"param",
"image",
":",
"a",
"numpy",
"nd",
"array",
":",
"kernel_dim",
":",
"dimension",
"(",
"1",
"-",
"D",
... | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/preprocessing/elastic_distortion.py#L57-L112 | [
"def",
"elastic_distortion",
"(",
"image",
",",
"kernel_dim",
"=",
"21",
",",
"sigma",
"=",
"6",
",",
"alpha",
"=",
"30",
",",
"negated",
"=",
"True",
")",
":",
"# check if the image is a negated one",
"if",
"not",
"negated",
":",
"image",
"=",
"255",
"-",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.stack_layer | Stack a neural layer.
:type layer: NeuralLayer
:param no_setup: whether the layer is already initialized | deepy/networks/network.py | def stack_layer(self, layer, no_setup=False):
"""
Stack a neural layer.
:type layer: NeuralLayer
:param no_setup: whether the layer is already initialized
"""
if layer.name:
layer.name += "%d" % (len(self.layers) + 1)
if not self.layers:
la... | def stack_layer(self, layer, no_setup=False):
"""
Stack a neural layer.
:type layer: NeuralLayer
:param no_setup: whether the layer is already initialized
"""
if layer.name:
layer.name += "%d" % (len(self.layers) + 1)
if not self.layers:
la... | [
"Stack",
"a",
"neural",
"layer",
".",
":",
"type",
"layer",
":",
"NeuralLayer",
":",
"param",
"no_setup",
":",
"whether",
"the",
"layer",
"is",
"already",
"initialized"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L79-L95 | [
"def",
"stack_layer",
"(",
"self",
",",
"layer",
",",
"no_setup",
"=",
"False",
")",
":",
"if",
"layer",
".",
"name",
":",
"layer",
".",
"name",
"+=",
"\"%d\"",
"%",
"(",
"len",
"(",
"self",
".",
"layers",
")",
"+",
"1",
")",
"if",
"not",
"self",... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.register_layer | Register the layer so that it's param will be trained.
But the output of the layer will not be stacked. | deepy/networks/network.py | def register_layer(self, layer):
"""
Register the layer so that it's param will be trained.
But the output of the layer will not be stacked.
"""
if type(layer) == Block:
layer.fix()
self.parameter_count += layer.parameter_count
self.parameters.extend(l... | def register_layer(self, layer):
"""
Register the layer so that it's param will be trained.
But the output of the layer will not be stacked.
"""
if type(layer) == Block:
layer.fix()
self.parameter_count += layer.parameter_count
self.parameters.extend(l... | [
"Register",
"the",
"layer",
"so",
"that",
"it",
"s",
"param",
"will",
"be",
"trained",
".",
"But",
"the",
"output",
"of",
"the",
"layer",
"will",
"not",
"be",
"stacked",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L106-L125 | [
"def",
"register_layer",
"(",
"self",
",",
"layer",
")",
":",
"if",
"type",
"(",
"layer",
")",
"==",
"Block",
":",
"layer",
".",
"fix",
"(",
")",
"self",
".",
"parameter_count",
"+=",
"layer",
".",
"parameter_count",
"self",
".",
"parameters",
".",
"ex... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.monitor_layer_outputs | Monitoring the outputs of each layer.
Useful for troubleshooting convergence problems. | deepy/networks/network.py | def monitor_layer_outputs(self):
"""
Monitoring the outputs of each layer.
Useful for troubleshooting convergence problems.
"""
for layer, hidden in zip(self.layers, self._hidden_outputs):
self.training_monitors.append(('mean(%s)' % (layer.name), abs(hidden).mean())) | def monitor_layer_outputs(self):
"""
Monitoring the outputs of each layer.
Useful for troubleshooting convergence problems.
"""
for layer, hidden in zip(self.layers, self._hidden_outputs):
self.training_monitors.append(('mean(%s)' % (layer.name), abs(hidden).mean())) | [
"Monitoring",
"the",
"outputs",
"of",
"each",
"layer",
".",
"Useful",
"for",
"troubleshooting",
"convergence",
"problems",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L147-L153 | [
"def",
"monitor_layer_outputs",
"(",
"self",
")",
":",
"for",
"layer",
",",
"hidden",
"in",
"zip",
"(",
"self",
".",
"layers",
",",
"self",
".",
"_hidden_outputs",
")",
":",
"self",
".",
"training_monitors",
".",
"append",
"(",
"(",
"'mean(%s)'",
"%",
"(... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.all_parameters | Return all parameters. | deepy/networks/network.py | def all_parameters(self):
"""
Return all parameters.
"""
params = []
params.extend(self.parameters)
params.extend(self.free_parameters)
return params | def all_parameters(self):
"""
Return all parameters.
"""
params = []
params.extend(self.parameters)
params.extend(self.free_parameters)
return params | [
"Return",
"all",
"parameters",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L156-L164 | [
"def",
"all_parameters",
"(",
"self",
")",
":",
"params",
"=",
"[",
"]",
"params",
".",
"extend",
"(",
"self",
".",
"parameters",
")",
"params",
".",
"extend",
"(",
"self",
".",
"free_parameters",
")",
"return",
"params"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.setup_variables | Set up variables. | deepy/networks/network.py | def setup_variables(self):
"""
Set up variables.
"""
if self.input_tensor:
if type(self.input_tensor) == int:
x = dim_to_var(self.input_tensor, name="x")
else:
x = self.input_tensor
else:
x = T.matrix('x')
... | def setup_variables(self):
"""
Set up variables.
"""
if self.input_tensor:
if type(self.input_tensor) == int:
x = dim_to_var(self.input_tensor, name="x")
else:
x = self.input_tensor
else:
x = T.matrix('x')
... | [
"Set",
"up",
"variables",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L166-L179 | [
"def",
"setup_variables",
"(",
"self",
")",
":",
"if",
"self",
".",
"input_tensor",
":",
"if",
"type",
"(",
"self",
".",
"input_tensor",
")",
"==",
"int",
":",
"x",
"=",
"dim_to_var",
"(",
"self",
".",
"input_tensor",
",",
"name",
"=",
"\"x\"",
")",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.compute | Return network output. | deepy/networks/network.py | def compute(self, *x):
"""
Return network output.
"""
self._compile()
outs = self._compute(*x)
if self._output_keys:
return MapDict(dict(zip(self._output_keys, outs)))
else:
return outs | def compute(self, *x):
"""
Return network output.
"""
self._compile()
outs = self._compute(*x)
if self._output_keys:
return MapDict(dict(zip(self._output_keys, outs)))
else:
return outs | [
"Return",
"network",
"output",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L198-L207 | [
"def",
"compute",
"(",
"self",
",",
"*",
"x",
")",
":",
"self",
".",
"_compile",
"(",
")",
"outs",
"=",
"self",
".",
"_compute",
"(",
"*",
"x",
")",
"if",
"self",
".",
"_output_keys",
":",
"return",
"MapDict",
"(",
"dict",
"(",
"zip",
"(",
"self"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.save_params | Save parameters to file. | deepy/networks/network.py | def save_params(self, path, new_thread=False):
"""
Save parameters to file.
"""
save_logger.info(path)
param_variables = self.all_parameters
params = [p.get_value().copy() for p in param_variables]
if new_thread:
thread = Thread(target=save_network_par... | def save_params(self, path, new_thread=False):
"""
Save parameters to file.
"""
save_logger.info(path)
param_variables = self.all_parameters
params = [p.get_value().copy() for p in param_variables]
if new_thread:
thread = Thread(target=save_network_par... | [
"Save",
"parameters",
"to",
"file",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L237-L249 | [
"def",
"save_params",
"(",
"self",
",",
"path",
",",
"new_thread",
"=",
"False",
")",
":",
"save_logger",
".",
"info",
"(",
"path",
")",
"param_variables",
"=",
"self",
".",
"all_parameters",
"params",
"=",
"[",
"p",
".",
"get_value",
"(",
")",
".",
"c... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.load_params | Load parameters from file. | deepy/networks/network.py | def load_params(self, path, exclude_free_params=False):
"""
Load parameters from file.
"""
if not os.path.exists(path): return;
logging.info("loading parameters from %s" % path)
# Decide which parameters to load
if exclude_free_params:
params_to_load =... | def load_params(self, path, exclude_free_params=False):
"""
Load parameters from file.
"""
if not os.path.exists(path): return;
logging.info("loading parameters from %s" % path)
# Decide which parameters to load
if exclude_free_params:
params_to_load =... | [
"Load",
"parameters",
"from",
"file",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L251-L282 | [
"def",
"load_params",
"(",
"self",
",",
"path",
",",
"exclude_free_params",
"=",
"False",
")",
":",
"if",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"path",
")",
":",
"return",
"logging",
".",
"info",
"(",
"\"loading parameters from %s\"",
"%",
"path",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralNetwork.report | Print network statistics. | deepy/networks/network.py | def report(self):
"""
Print network statistics.
"""
logging.info("network inputs: %s", " ".join(map(str, self.input_variables)))
logging.info("network targets: %s", " ".join(map(str, self.target_variables)))
logging.info("network parameters: %s", " ".join(map(str, self.al... | def report(self):
"""
Print network statistics.
"""
logging.info("network inputs: %s", " ".join(map(str, self.input_variables)))
logging.info("network targets: %s", " ".join(map(str, self.target_variables)))
logging.info("network parameters: %s", " ".join(map(str, self.al... | [
"Print",
"network",
"statistics",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/networks/network.py#L284-L291 | [
"def",
"report",
"(",
"self",
")",
":",
"logging",
".",
"info",
"(",
"\"network inputs: %s\"",
",",
"\" \"",
".",
"join",
"(",
"map",
"(",
"str",
",",
"self",
".",
"input_variables",
")",
")",
")",
"logging",
".",
"info",
"(",
"\"network targets: %s\"",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLayer.init | Initialize the layer.
:param no_prepare: avoid calling preparation function | deepy/layers/layer.py | def init(self, input_dim=0, input_dims=None, no_prepare=False):
"""
Initialize the layer.
:param no_prepare: avoid calling preparation function
"""
if self.initialized:
return
# configure input dimensions
if input_dims:
self.input_dims = in... | def init(self, input_dim=0, input_dims=None, no_prepare=False):
"""
Initialize the layer.
:param no_prepare: avoid calling preparation function
"""
if self.initialized:
return
# configure input dimensions
if input_dims:
self.input_dims = in... | [
"Initialize",
"the",
"layer",
".",
":",
"param",
"no_prepare",
":",
"avoid",
"calling",
"preparation",
"function"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/layer.py#L47-L68 | [
"def",
"init",
"(",
"self",
",",
"input_dim",
"=",
"0",
",",
"input_dims",
"=",
"None",
",",
"no_prepare",
"=",
"False",
")",
":",
"if",
"self",
".",
"initialized",
":",
"return",
"# configure input dimensions",
"if",
"input_dims",
":",
"self",
".",
"input... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLayer.compute | Compute based on NeuralVariable.
:type inputs: list of NeuralVariable
:return: NeuralVariable | deepy/layers/layer.py | def compute(self, *inputs, **kwargs):
"""
Compute based on NeuralVariable.
:type inputs: list of NeuralVariable
:return: NeuralVariable
"""
from deepy.core.neural_var import NeuralVariable
from deepy.core.graph import graph
if type(inputs[0]) != NeuralVar... | def compute(self, *inputs, **kwargs):
"""
Compute based on NeuralVariable.
:type inputs: list of NeuralVariable
:return: NeuralVariable
"""
from deepy.core.neural_var import NeuralVariable
from deepy.core.graph import graph
if type(inputs[0]) != NeuralVar... | [
"Compute",
"based",
"on",
"NeuralVariable",
".",
":",
"type",
"inputs",
":",
"list",
"of",
"NeuralVariable",
":",
"return",
":",
"NeuralVariable"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/layer.py#L70-L97 | [
"def",
"compute",
"(",
"self",
",",
"*",
"inputs",
",",
"*",
"*",
"kwargs",
")",
":",
"from",
"deepy",
".",
"core",
".",
"neural_var",
"import",
"NeuralVariable",
"from",
"deepy",
".",
"core",
".",
"graph",
"import",
"graph",
"if",
"type",
"(",
"inputs... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLayer.belongs_to | Let the given block or network manage the parameters of this layer.
:param block: Block or NeuralNetwork
:return: NeuralLayer | deepy/layers/layer.py | def belongs_to(self, block):
"""
Let the given block or network manage the parameters of this layer.
:param block: Block or NeuralNetwork
:return: NeuralLayer
"""
if self._linked_block:
raise SystemError("The layer {} has already blonged to {}".format(self.nam... | def belongs_to(self, block):
"""
Let the given block or network manage the parameters of this layer.
:param block: Block or NeuralNetwork
:return: NeuralLayer
"""
if self._linked_block:
raise SystemError("The layer {} has already blonged to {}".format(self.nam... | [
"Let",
"the",
"given",
"block",
"or",
"network",
"manage",
"the",
"parameters",
"of",
"this",
"layer",
".",
":",
"param",
"block",
":",
"Block",
"or",
"NeuralNetwork",
":",
"return",
":",
"NeuralLayer"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/layer.py#L111-L121 | [
"def",
"belongs_to",
"(",
"self",
",",
"block",
")",
":",
"if",
"self",
".",
"_linked_block",
":",
"raise",
"SystemError",
"(",
"\"The layer {} has already blonged to {}\"",
".",
"format",
"(",
"self",
".",
"name",
",",
"self",
".",
"_linked_block",
".",
"name... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLayer.register_parameters | Register parameters. | deepy/layers/layer.py | def register_parameters(self, *parameters):
"""
Register parameters.
"""
for param in parameters:
self.parameter_count += np.prod(param.get_value().shape)
self.parameters.extend(parameters) | def register_parameters(self, *parameters):
"""
Register parameters.
"""
for param in parameters:
self.parameter_count += np.prod(param.get_value().shape)
self.parameters.extend(parameters) | [
"Register",
"parameters",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/layer.py#L137-L143 | [
"def",
"register_parameters",
"(",
"self",
",",
"*",
"parameters",
")",
":",
"for",
"param",
"in",
"parameters",
":",
"self",
".",
"parameter_count",
"+=",
"np",
".",
"prod",
"(",
"param",
".",
"get_value",
"(",
")",
".",
"shape",
")",
"self",
".",
"pa... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLayer.register_updates | Register updates that will be executed in each iteration. | deepy/layers/layer.py | def register_updates(self, *updates):
"""
Register updates that will be executed in each iteration.
"""
for key, node in updates:
if key not in self._registered_updates:
self.updates.append((key, node))
self._registered_updates.add(key) | def register_updates(self, *updates):
"""
Register updates that will be executed in each iteration.
"""
for key, node in updates:
if key not in self._registered_updates:
self.updates.append((key, node))
self._registered_updates.add(key) | [
"Register",
"updates",
"that",
"will",
"be",
"executed",
"in",
"each",
"iteration",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/layer.py#L151-L158 | [
"def",
"register_updates",
"(",
"self",
",",
"*",
"updates",
")",
":",
"for",
"key",
",",
"node",
"in",
"updates",
":",
"if",
"key",
"not",
"in",
"self",
".",
"_registered_updates",
":",
"self",
".",
"updates",
".",
"append",
"(",
"(",
"key",
",",
"n... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLayer.register_training_updates | Register updates that will only be executed in training phase. | deepy/layers/layer.py | def register_training_updates(self, *updates):
"""
Register updates that will only be executed in training phase.
"""
for key, node in updates:
if key not in self._registered_training_updates:
self.training_updates.append((key, node))
self._reg... | def register_training_updates(self, *updates):
"""
Register updates that will only be executed in training phase.
"""
for key, node in updates:
if key not in self._registered_training_updates:
self.training_updates.append((key, node))
self._reg... | [
"Register",
"updates",
"that",
"will",
"only",
"be",
"executed",
"in",
"training",
"phase",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/layer.py#L160-L167 | [
"def",
"register_training_updates",
"(",
"self",
",",
"*",
"updates",
")",
":",
"for",
"key",
",",
"node",
"in",
"updates",
":",
"if",
"key",
"not",
"in",
"self",
".",
"_registered_training_updates",
":",
"self",
".",
"training_updates",
".",
"append",
"(",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | NeuralLayer.register_monitors | Register monitors they should be tuple of name and Theano variable. | deepy/layers/layer.py | def register_monitors(self, *monitors):
"""
Register monitors they should be tuple of name and Theano variable.
"""
for key, node in monitors:
if key not in self._registered_monitors:
node *= 1.0 # Avoid CudaNdarray
self.training_monitors.appen... | def register_monitors(self, *monitors):
"""
Register monitors they should be tuple of name and Theano variable.
"""
for key, node in monitors:
if key not in self._registered_monitors:
node *= 1.0 # Avoid CudaNdarray
self.training_monitors.appen... | [
"Register",
"monitors",
"they",
"should",
"be",
"tuple",
"of",
"name",
"and",
"Theano",
"variable",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/layer.py#L169-L178 | [
"def",
"register_monitors",
"(",
"self",
",",
"*",
"monitors",
")",
":",
"for",
"key",
",",
"node",
"in",
"monitors",
":",
"if",
"key",
"not",
"in",
"self",
".",
"_registered_monitors",
":",
"node",
"*=",
"1.0",
"# Avoid CudaNdarray",
"self",
".",
"trainin... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | multiple_l2_norm | Get the L2 norm of multiple tensors.
This function is taken from blocks. | deepy/trainers/util.py | def multiple_l2_norm(tensors):
"""
Get the L2 norm of multiple tensors.
This function is taken from blocks.
"""
# Another way for doing this, I don't know which one is fast
# return T.sqrt(sum(T.sum(t ** 2) for t in tensors))
flattened = [T.as_tensor_variable(t).flatten() for t in tensors]
... | def multiple_l2_norm(tensors):
"""
Get the L2 norm of multiple tensors.
This function is taken from blocks.
"""
# Another way for doing this, I don't know which one is fast
# return T.sqrt(sum(T.sum(t ** 2) for t in tensors))
flattened = [T.as_tensor_variable(t).flatten() for t in tensors]
... | [
"Get",
"the",
"L2",
"norm",
"of",
"multiple",
"tensors",
".",
"This",
"function",
"is",
"taken",
"from",
"blocks",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/trainers/util.py#L19-L30 | [
"def",
"multiple_l2_norm",
"(",
"tensors",
")",
":",
"# Another way for doing this, I don't know which one is fast",
"# return T.sqrt(sum(T.sum(t ** 2) for t in tensors))",
"flattened",
"=",
"[",
"T",
".",
"as_tensor_variable",
"(",
"t",
")",
".",
"flatten",
"(",
")",
"for"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | StreamPickler.dump_one | dumps one element to file_obj, a file opened in write mode | deepy/utils/stream_pickler.py | def dump_one(elt_to_pickle, file_obj):
"""
dumps one element to file_obj, a file opened in write mode
"""
pickled_elt_str = dumps(elt_to_pickle)
file_obj.write(pickled_elt_str)
# record separator is a blank line
# (since pickled_elt_str might contain its own newli... | def dump_one(elt_to_pickle, file_obj):
"""
dumps one element to file_obj, a file opened in write mode
"""
pickled_elt_str = dumps(elt_to_pickle)
file_obj.write(pickled_elt_str)
# record separator is a blank line
# (since pickled_elt_str might contain its own newli... | [
"dumps",
"one",
"element",
"to",
"file_obj",
"a",
"file",
"opened",
"in",
"write",
"mode"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/utils/stream_pickler.py#L25-L33 | [
"def",
"dump_one",
"(",
"elt_to_pickle",
",",
"file_obj",
")",
":",
"pickled_elt_str",
"=",
"dumps",
"(",
"elt_to_pickle",
")",
"file_obj",
".",
"write",
"(",
"pickled_elt_str",
")",
"# record separator is a blank line",
"# (since pickled_elt_str might contain its own newli... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | StreamPickler.load | load contents from file_obj, returning a generator that yields one
element at a time | deepy/utils/stream_pickler.py | def load(file_obj):
"""
load contents from file_obj, returning a generator that yields one
element at a time
"""
cur_elt = []
for line in file_obj:
cur_elt.append(line)
if line == '\n':
pickled_elt_str = ''.join(cur_elt)
cur_el... | def load(file_obj):
"""
load contents from file_obj, returning a generator that yields one
element at a time
"""
cur_elt = []
for line in file_obj:
cur_elt.append(line)
if line == '\n':
pickled_elt_str = ''.join(cur_elt)
cur_el... | [
"load",
"contents",
"from",
"file_obj",
"returning",
"a",
"generator",
"that",
"yields",
"one",
"element",
"at",
"a",
"time"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/utils/stream_pickler.py#L36-L53 | [
"def",
"load",
"(",
"file_obj",
")",
":",
"cur_elt",
"=",
"[",
"]",
"for",
"line",
"in",
"file_obj",
":",
"cur_elt",
".",
"append",
"(",
"line",
")",
"if",
"line",
"==",
"'\\n'",
":",
"pickled_elt_str",
"=",
"''",
".",
"join",
"(",
"cur_elt",
")",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Block.fix | Fix the block, register all the parameters of sub layers.
:return: | deepy/layers/block.py | def fix(self):
"""
Fix the block, register all the parameters of sub layers.
:return:
"""
if not self.fixed:
for layer in self.layers:
if not layer.initialized:
raise Exception("All sub layers in a block must be initialized when fix... | def fix(self):
"""
Fix the block, register all the parameters of sub layers.
:return:
"""
if not self.fixed:
for layer in self.layers:
if not layer.initialized:
raise Exception("All sub layers in a block must be initialized when fix... | [
"Fix",
"the",
"block",
"register",
"all",
"the",
"parameters",
"of",
"sub",
"layers",
".",
":",
"return",
":"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/block.py#L24-L34 | [
"def",
"fix",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"fixed",
":",
"for",
"layer",
"in",
"self",
".",
"layers",
":",
"if",
"not",
"layer",
".",
"initialized",
":",
"raise",
"Exception",
"(",
"\"All sub layers in a block must be initialized when fixin... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Block.register_layer | Register one connected layer.
:type layer: NeuralLayer | deepy/layers/block.py | def register_layer(self, layer):
"""
Register one connected layer.
:type layer: NeuralLayer
"""
if self.fixed:
raise Exception("After a block is fixed, no more layers can be registered.")
self.layers.append(layer) | def register_layer(self, layer):
"""
Register one connected layer.
:type layer: NeuralLayer
"""
if self.fixed:
raise Exception("After a block is fixed, no more layers can be registered.")
self.layers.append(layer) | [
"Register",
"one",
"connected",
"layer",
".",
":",
"type",
"layer",
":",
"NeuralLayer"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/block.py#L51-L58 | [
"def",
"register_layer",
"(",
"self",
",",
"layer",
")",
":",
"if",
"self",
".",
"fixed",
":",
"raise",
"Exception",
"(",
"\"After a block is fixed, no more layers can be registered.\"",
")",
"self",
".",
"layers",
".",
"append",
"(",
"layer",
")"
] | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | Block.load_params | Load parameters to the block. | deepy/layers/block.py | def load_params(self, path, exclude_free_params=False):
from deepy.core import graph
"""
Load parameters to the block.
"""
from deepy.core.comp_graph import ComputationalGraph
model = graph.compile(blocks=[self])
model.load_params(path, exclude_free_params=exclude... | def load_params(self, path, exclude_free_params=False):
from deepy.core import graph
"""
Load parameters to the block.
"""
from deepy.core.comp_graph import ComputationalGraph
model = graph.compile(blocks=[self])
model.load_params(path, exclude_free_params=exclude... | [
"Load",
"parameters",
"to",
"the",
"block",
"."
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/block.py#L64-L71 | [
"def",
"load_params",
"(",
"self",
",",
"path",
",",
"exclude_free_params",
"=",
"False",
")",
":",
"from",
"deepy",
".",
"core",
"import",
"graph",
"from",
"deepy",
".",
"core",
".",
"comp_graph",
"import",
"ComputationalGraph",
"model",
"=",
"graph",
".",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | RecurrentLayer.compute_step | Compute one step in the RNN.
:return: one variable for RNN and GRU, multiple variables for LSTM | deepy/layers/recurrent.py | def compute_step(self, state, lstm_cell=None, input=None, additional_inputs=None):
"""
Compute one step in the RNN.
:return: one variable for RNN and GRU, multiple variables for LSTM
"""
if not self.initialized:
input_dim = None
if input and hasattr(input.... | def compute_step(self, state, lstm_cell=None, input=None, additional_inputs=None):
"""
Compute one step in the RNN.
:return: one variable for RNN and GRU, multiple variables for LSTM
"""
if not self.initialized:
input_dim = None
if input and hasattr(input.... | [
"Compute",
"one",
"step",
"in",
"the",
"RNN",
".",
":",
"return",
":",
"one",
"variable",
"for",
"RNN",
"and",
"GRU",
"multiple",
"variables",
"for",
"LSTM"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/recurrent.py#L85-L106 | [
"def",
"compute_step",
"(",
"self",
",",
"state",
",",
"lstm_cell",
"=",
"None",
",",
"input",
"=",
"None",
",",
"additional_inputs",
"=",
"None",
")",
":",
"if",
"not",
"self",
".",
"initialized",
":",
"input_dim",
"=",
"None",
"if",
"input",
"and",
"... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | RecurrentLayer.get_initial_states | :type input_var: T.var
:rtype: dict | deepy/layers/recurrent.py | def get_initial_states(self, input_var, init_state=None):
"""
:type input_var: T.var
:rtype: dict
"""
initial_states = {}
for state in self.state_names:
if state != "state" or not init_state:
if self._input_type == 'sequence' and input_var.ndim... | def get_initial_states(self, input_var, init_state=None):
"""
:type input_var: T.var
:rtype: dict
"""
initial_states = {}
for state in self.state_names:
if state != "state" or not init_state:
if self._input_type == 'sequence' and input_var.ndim... | [
":",
"type",
"input_var",
":",
"T",
".",
"var",
":",
"rtype",
":",
"dict"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/recurrent.py#L109-L122 | [
"def",
"get_initial_states",
"(",
"self",
",",
"input_var",
",",
"init_state",
"=",
"None",
")",
":",
"initial_states",
"=",
"{",
"}",
"for",
"state",
"in",
"self",
".",
"state_names",
":",
"if",
"state",
"!=",
"\"state\"",
"or",
"not",
"init_state",
":",
... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
test | RecurrentLayer.get_step_inputs | :type input_var: T.var
:rtype: dict | deepy/layers/recurrent.py | def get_step_inputs(self, input_var, states=None, mask=None, additional_inputs=None):
"""
:type input_var: T.var
:rtype: dict
"""
step_inputs = {}
if self._input_type == "sequence":
if not additional_inputs:
additional_inputs = []
i... | def get_step_inputs(self, input_var, states=None, mask=None, additional_inputs=None):
"""
:type input_var: T.var
:rtype: dict
"""
step_inputs = {}
if self._input_type == "sequence":
if not additional_inputs:
additional_inputs = []
i... | [
":",
"type",
"input_var",
":",
"T",
".",
"var",
":",
"rtype",
":",
"dict"
] | zomux/deepy | python | https://github.com/zomux/deepy/blob/090fbad22a08a809b12951cd0d4984f5bd432698/deepy/layers/recurrent.py#L125-L145 | [
"def",
"get_step_inputs",
"(",
"self",
",",
"input_var",
",",
"states",
"=",
"None",
",",
"mask",
"=",
"None",
",",
"additional_inputs",
"=",
"None",
")",
":",
"step_inputs",
"=",
"{",
"}",
"if",
"self",
".",
"_input_type",
"==",
"\"sequence\"",
":",
"if... | 090fbad22a08a809b12951cd0d4984f5bd432698 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.