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tensorflow/tensor2tensor | tensor2tensor/models/research/adafactor_experiments.py | afx_adafactor | def afx_adafactor():
"""Adafactor with recommended learning rate schedule."""
hparams = afx_adam()
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
hparams.learning_rate_warmup_steps = 10000
return hparams | python | def afx_adafactor():
"""Adafactor with recommended learning rate schedule."""
hparams = afx_adam()
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
hparams.learning_rate_warmup_steps = 10000
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/adafactor_experiments.py | afx_small | def afx_small():
"""Small transformer model with small batch size for fast step times."""
hparams = transformer.transformer_tpu()
hparams.filter_size = 1024
hparams.num_heads = 4
hparams.num_hidden_layers = 3
hparams.batch_size = 512
return hparams | python | def afx_small():
"""Small transformer model with small batch size for fast step times."""
hparams = transformer.transformer_tpu()
hparams.filter_size = 1024
hparams.num_heads = 4
hparams.num_hidden_layers = 3
hparams.batch_size = 512
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/video/emily.py | next_frame_emily | def next_frame_emily():
"""Emily's model hparams."""
hparams = sv2p_params.next_frame_sv2p()
hparams.video_num_input_frames = 2
hparams.video_num_target_frames = 10
hparams.learning_rate_constant = 1e-4
seq_length = hparams.video_num_input_frames + hparams.video_num_target_frames
# The latent_loss_multipl... | python | def next_frame_emily():
"""Emily's model hparams."""
hparams = sv2p_params.next_frame_sv2p()
hparams.video_num_input_frames = 2
hparams.video_num_target_frames = 10
hparams.learning_rate_constant = 1e-4
seq_length = hparams.video_num_input_frames + hparams.video_num_target_frames
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tensorflow/tensor2tensor | tensor2tensor/data_generators/inspect_tfrecord.py | main | def main(_):
"""Convert a file to examples."""
if FLAGS.subword_text_encoder_filename:
encoder = text_encoder.SubwordTextEncoder(
FLAGS.subword_text_encoder_filename)
elif FLAGS.token_text_encoder_filename:
encoder = text_encoder.TokenTextEncoder(FLAGS.token_text_encoder_filename)
elif FLAGS.byt... | python | def main(_):
"""Convert a file to examples."""
if FLAGS.subword_text_encoder_filename:
encoder = text_encoder.SubwordTextEncoder(
FLAGS.subword_text_encoder_filename)
elif FLAGS.token_text_encoder_filename:
encoder = text_encoder.TokenTextEncoder(FLAGS.token_text_encoder_filename)
elif FLAGS.byt... | [
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tensorflow/tensor2tensor | tensor2tensor/envs/rendered_env_problem.py | RenderedEnvProblem.example_reading_spec | def example_reading_spec(self):
"""Return a mix of env and video data fields and decoders."""
video_fields, video_decoders = (
video_utils.VideoProblem.example_reading_spec(self))
env_fields, env_decoders = env_problem.EnvProblem.example_reading_spec(self)
# Remove raw observations field since ... | python | def example_reading_spec(self):
"""Return a mix of env and video data fields and decoders."""
video_fields, video_decoders = (
video_utils.VideoProblem.example_reading_spec(self))
env_fields, env_decoders = env_problem.EnvProblem.example_reading_spec(self)
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tensorflow/tensor2tensor | tensor2tensor/envs/rendered_env_problem.py | RenderedEnvProblem._generate_time_steps | def _generate_time_steps(self, trajectory_list):
"""Transforms time step observations to frames of a video."""
for time_step in env_problem.EnvProblem._generate_time_steps(
self, trajectory_list):
# Convert the rendered observations from numpy to png format.
frame_np = np.array(time_step.pop... | python | def _generate_time_steps(self, trajectory_list):
"""Transforms time step observations to frames of a video."""
for time_step in env_problem.EnvProblem._generate_time_steps(
self, trajectory_list):
# Convert the rendered observations from numpy to png format.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | txt_line_iterator | def txt_line_iterator(txt_path):
"""Iterate through lines of file."""
with tf.gfile.Open(txt_path) as f:
for line in f:
yield line.strip() | python | def txt_line_iterator(txt_path):
"""Iterate through lines of file."""
with tf.gfile.Open(txt_path) as f:
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | text2text_txt_iterator | def text2text_txt_iterator(source_txt_path, target_txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of files."""
for inputs, targets in zip(
txt_line_iterator(source_txt_path), txt_line_iterator(target_txt_path)):
yield {"inputs": inputs, "targets": targets} | python | def text2text_txt_iterator(source_txt_path, target_txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of files."""
for inputs, targets in zip(
txt_line_iterator(source_txt_path), txt_line_iterator(target_txt_path)):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | text2text_distill_iterator | def text2text_distill_iterator(source_txt_path, target_txt_path,
distill_txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of files."""
for inputs, targets, dist_targets in zip(
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... | python | def text2text_distill_iterator(source_txt_path, target_txt_path,
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | text2class_txt_iterator | def text2class_txt_iterator(source_txt_path, label_txt_path, class_strs=None):
"""Yield dicts for Text2ClassProblem.generate_samples from lines of files.
Args:
source_txt_path: txt file with record per line.
label_txt_path: txt file with label per line, either as int or str. If
string, must provide c... | python | def text2class_txt_iterator(source_txt_path, label_txt_path, class_strs=None):
"""Yield dicts for Text2ClassProblem.generate_samples from lines of files.
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source_txt_path: txt file with record per line.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | text2text_txt_tab_iterator | def text2text_txt_tab_iterator(txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of txt_path.
Args:
txt_path: path to txt file with a record per line, source and target
are tab-separated.
Yields:
{"inputs": inputs, "targets": targets}
"""
for line in txt_line_iterator(... | python | def text2text_txt_tab_iterator(txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of txt_path.
Args:
txt_path: path to txt file with a record per line, source and target
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Yields:
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | text2text_generate_encoded | def text2text_generate_encoded(sample_generator,
vocab,
targets_vocab=None,
has_inputs=True,
inputs_prefix="",
targets_prefix=""):
"""Encode Text2Text samples from... | python | def text2text_generate_encoded(sample_generator,
vocab,
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has_inputs=True,
inputs_prefix="",
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | Text2TextProblem._pack_fn | def _pack_fn(self):
"""For packed datasets, returns a function to pack examples.
Returns:
None or a function from list of TFRecords to list of TFRecords
"""
if not self.packed_length:
return None
def my_fn(records):
"""Function from list of TFRecords to list of TFRecords."""
... | python | def _pack_fn(self):
"""For packed datasets, returns a function to pack examples.
Returns:
None or a function from list of TFRecords to list of TFRecords
"""
if not self.packed_length:
return None
def my_fn(records):
"""Function from list of TFRecords to list of TFRecords."""
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | Text2TextProblem._maybe_pack_examples | def _maybe_pack_examples(self, generator):
"""Wraps generator with packer if self.packed_length."""
if not self.packed_length:
return generator
return generator_utils.pack_examples(
generator,
self.has_inputs,
self.packed_length,
spacing=self.packed_spacing,
cho... | python | def _maybe_pack_examples(self, generator):
"""Wraps generator with packer if self.packed_length."""
if not self.packed_length:
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return generator_utils.pack_examples(
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | ChoppedTextProblem.text_filepaths_for_task | def text_filepaths_for_task(self, tmp_dir, task_id):
"""List of input filepaths for a particular training or dev shard.
Args:
tmp_dir: a string
task_id: an integer less than self.num_shards
Returns:
a list of tuples (filepath, start_pos, num_bytes)
"""
assert task_id >= 0
asse... | python | def text_filepaths_for_task(self, tmp_dir, task_id):
"""List of input filepaths for a particular training or dev shard.
Args:
tmp_dir: a string
task_id: an integer less than self.num_shards
Returns:
a list of tuples (filepath, start_pos, num_bytes)
"""
assert task_id >= 0
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | ChoppedTextProblem.filepath_to_unicode_strings | def filepath_to_unicode_strings(self, filepath):
"""Read text out of an input file.
The default just reads the text, converts to unicode and yields one
unicode string.
Subclasses can override this function in order to preprocess, and can
yield any number of strings.
Args:
filepath: a st... | python | def filepath_to_unicode_strings(self, filepath):
"""Read text out of an input file.
The default just reads the text, converts to unicode and yields one
unicode string.
Subclasses can override this function in order to preprocess, and can
yield any number of strings.
Args:
filepath: a st... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | ChoppedTextProblem.file_generator | def file_generator(self,
filepaths,
max_chars_per_file=None,
max_chars_total=None):
"""Read complete text of input files and yield unicode strings.
By default, one unicode string is produced per file, but this is
not guaranteed, since subclasse... | python | def file_generator(self,
filepaths,
max_chars_per_file=None,
max_chars_total=None):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | ChoppedTextProblem.example_generator | def example_generator(self, encoder, tmp_dir, task_id):
"""Generator for examples.
Args:
encoder: a TextEncoder
tmp_dir: a string
task_id: an integer
Yields:
feature dictionaries
"""
filepaths = self.text_filepaths_for_task(tmp_dir, task_id)
if task_id >= self.num_train_... | python | def example_generator(self, encoder, tmp_dir, task_id):
"""Generator for examples.
Args:
encoder: a TextEncoder
tmp_dir: a string
task_id: an integer
Yields:
feature dictionaries
"""
filepaths = self.text_filepaths_for_task(tmp_dir, task_id)
if task_id >= self.num_train_... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | ChoppedTextProblem.prepare_to_generate | def prepare_to_generate(self, data_dir, tmp_dir):
"""Make sure that the data is prepared and the vocab is generated."""
self.get_or_create_vocab(data_dir, tmp_dir)
self.train_text_filepaths(tmp_dir)
self.dev_text_filepaths(tmp_dir) | python | def prepare_to_generate(self, data_dir, tmp_dir):
"""Make sure that the data is prepared and the vocab is generated."""
self.get_or_create_vocab(data_dir, tmp_dir)
self.train_text_filepaths(tmp_dir)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/text_problems.py | ChoppedTextProblem.generate_data | def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""Generates training/dev data.
Args:
data_dir: a string
tmp_dir: a string
task_id: an optional integer
Returns:
shard or shards for which data was generated.
"""
tf.logging.info("generate_data task_id=%s" % task_id)
... | python | def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""Generates training/dev data.
Args:
data_dir: a string
tmp_dir: a string
task_id: an optional integer
Returns:
shard or shards for which data was generated.
"""
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tensorflow/tensor2tensor | tensor2tensor/trax/models/resnet.py | ConvBlock | def ConvBlock(kernel_size, filters, strides):
"""ResNet convolutional striding block."""
ks = kernel_size
filters1, filters2, filters3 = filters
main = layers.Serial(
layers.Conv(filters1, (1, 1), strides),
layers.BatchNorm(),
layers.Relu(),
layers.Conv(filters2, (ks, ks), padding='SAME'... | python | def ConvBlock(kernel_size, filters, strides):
"""ResNet convolutional striding block."""
ks = kernel_size
filters1, filters2, filters3 = filters
main = layers.Serial(
layers.Conv(filters1, (1, 1), strides),
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tensorflow/tensor2tensor | tensor2tensor/trax/models/resnet.py | IdentityBlock | def IdentityBlock(kernel_size, filters):
"""ResNet identical size block."""
ks = kernel_size
filters1, filters2, filters3 = filters
main = layers.Serial(
layers.Conv(filters1, (1, 1)),
layers.BatchNorm(),
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layers.BatchN... | python | def IdentityBlock(kernel_size, filters):
"""ResNet identical size block."""
ks = kernel_size
filters1, filters2, filters3 = filters
main = layers.Serial(
layers.Conv(filters1, (1, 1)),
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tensorflow/tensor2tensor | tensor2tensor/trax/models/resnet.py | Resnet50 | def Resnet50(hidden_size=64, num_output_classes=1001, mode='train'):
"""ResNet.
Args:
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: how many classes to distinguish.
mode: whether we are training or evaluating or doing inference.
Returns:
The ResNet model... | python | def Resnet50(hidden_size=64, num_output_classes=1001, mode='train'):
"""ResNet.
Args:
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: how many classes to distinguish.
mode: whether we are training or evaluating or doing inference.
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tensorflow/tensor2tensor | tensor2tensor/trax/models/resnet.py | WideResnetBlock | def WideResnetBlock(channels, strides=(1, 1), channel_mismatch=False):
"""WideResnet convolutational block."""
main = layers.Serial(layers.BatchNorm(), layers.Relu(),
layers.Conv(channels, (3, 3), strides, padding='SAME'),
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"""WideResnet convolutational block."""
main = layers.Serial(layers.BatchNorm(), layers.Relu(),
layers.Conv(channels, (3, 3), strides, padding='SAME'),
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tensorflow/tensor2tensor | tensor2tensor/trax/models/resnet.py | WideResnet | def WideResnet(num_blocks=3, hidden_size=64, num_output_classes=10,
mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: int, num... | python | def WideResnet(num_blocks=3, hidden_size=64, num_output_classes=10,
mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/rnn.py | GRUCell | def GRUCell(units):
"""Builds a traditional GRU cell with dense internal transformations.
Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
Args:
units: Number of hidden units.
Returns:
A Stax model representing a traditional GRU RNN cell.
"""
return GeneralGRUCell(
candidate_tra... | python | def GRUCell(units):
"""Builds a traditional GRU cell with dense internal transformations.
Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
Args:
units: Number of hidden units.
Returns:
A Stax model representing a traditional GRU RNN cell.
"""
return GeneralGRUCell(
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/rnn.py | ConvGRUCell | def ConvGRUCell(units, kernel_size=(3, 3)):
"""Builds a convolutional GRU.
Paper: https://arxiv.org/abs/1511.06432.
Args:
units: Number of hidden units
kernel_size: Kernel size for convolution
Returns:
A Stax model representing a GRU cell with convolution transforms.
"""
def BuildConv():
... | python | def ConvGRUCell(units, kernel_size=(3, 3)):
"""Builds a convolutional GRU.
Paper: https://arxiv.org/abs/1511.06432.
Args:
units: Number of hidden units
kernel_size: Kernel size for convolution
Returns:
A Stax model representing a GRU cell with convolution transforms.
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/rnn.py | GeneralGRUCell | def GeneralGRUCell(candidate_transform,
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gate_nonlinearity=core.Sigmoid,
candidate_nonlinearity=core.Tanh,
dropout_rate_c=0.1,
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r"""Parametrized Gated Recurrent Unit (... | python | def GeneralGRUCell(candidate_transform,
memory_transform=combinators.Identity,
gate_nonlinearity=core.Sigmoid,
candidate_nonlinearity=core.Tanh,
dropout_rate_c=0.1,
sigmoid_bias=0.5):
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | MakeTargetMask | def MakeTargetMask(target, pad=0):
"""Create an attention mask to hide padding and future words."""
target_mask = (target != pad)[ :, np.newaxis, :]
target_dtype = target_mask.dtype
causal_mask = onp.tril(onp.ones((1, target.shape[-1], target.shape[-1]),
dtype=target_dtype), k=... | python | def MakeTargetMask(target, pad=0):
"""Create an attention mask to hide padding and future words."""
target_mask = (target != pad)[ :, np.newaxis, :]
target_dtype = target_mask.dtype
causal_mask = onp.tril(onp.ones((1, target.shape[-1], target.shape[-1]),
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | PreparePairedSequenceBatch | def PreparePairedSequenceBatch(source, target_in, pad=0):
"""Build masks for this batch.
Args:
source: (batch, source_len) array of integer-coded symbols for inputs
target_in: (batch, batch_len) array of integer-coded symbols for targets
pad: int: the padding symbol used to pad the above
Returns:
... | python | def PreparePairedSequenceBatch(source, target_in, pad=0):
"""Build masks for this batch.
Args:
source: (batch, source_len) array of integer-coded symbols for inputs
target_in: (batch, batch_len) array of integer-coded symbols for targets
pad: int: the padding symbol used to pad the above
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | _layer_norm_new_params | def _layer_norm_new_params(input_shape, rng, epsilon=1e-6): # pylint: disable=invalid-name
"""Helper: create layer norm parameters."""
del rng, epsilon
features = input_shape[-1]
scale = np.ones(features)
bias = np.zeros(features)
return (scale, bias) | python | def _layer_norm_new_params(input_shape, rng, epsilon=1e-6): # pylint: disable=invalid-name
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del rng, epsilon
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scale = np.ones(features)
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | _positional_encoding_new_params | def _positional_encoding_new_params(input_shape, rng, max_len=2048): # pylint: disable=invalid-name
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | PositionalEncoding | def PositionalEncoding(x, params, **unused_kwargs):
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"""Implements bare positional encoding."""
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query: array of representations
key: array of representations
value: array of representations
mask: attention-mask, gates attention
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | PureDotProductAttention | def PureDotProductAttention(dropout=0.0, mode='train'):
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dropout: float: dropout rate
mode: str: 'train' or 'eval'
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"""Pure single-headed self-attention.
Args:
dropout: float: dropout rate
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Args:
x: inputs ((q, k, v), mask)
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | MultiHeadedAttentionQKV | def MultiHeadedAttentionQKV(
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"""Transformer-style multi-headed attention.
Accepts inputs of the form (q, k, v), mask.
Args:
feature_depth: int: depth of embedding
num_heads: int: number of attention heads
dropout: float: dropout rate
m... | python | def MultiHeadedAttentionQKV(
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | _chunked_selector_output_shape | def _chunked_selector_output_shape( # pylint: disable=invalid-name
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"""Helper: calculate output shape for chunked key selector (see below)."""
# Read the main function below first, the shape logic just follows the ops.
selector = selector or (lambda x: [] if x < ... | python | def _chunked_selector_output_shape( # pylint: disable=invalid-name
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | ChunkedAttentionSelector | def ChunkedAttentionSelector(x, params, selector=None, **kwargs):
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Args:
x: inputs, a list of elements of the form (q, k, v), mask for each chunk.
params: parameters (unused).
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | ChunkedCausalMultiHeadedAttention | def ChunkedCausalMultiHeadedAttention(
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tensorflow/tensor2tensor | tensor2tensor/trax/layers/attention.py | ShiftRight | def ShiftRight(x, **unused_kwargs):
"""Layer to shift the tensor to the right by padding on axis 1."""
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pad_widths = [(0, 0), (1, 0)]
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return padded[:, :-1]
# Handling chunked inputs. Recall that t... | python | def ShiftRight(x, **unused_kwargs):
"""Layer to shift the tensor to the right by padding on axis 1."""
if not isinstance(x, (list, tuple)): # non-chunked inputs
pad_widths = [(0, 0), (1, 0)]
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic.py | zipf_distribution | def zipf_distribution(nbr_symbols, alpha):
"""Helper function: Create a Zipf distribution.
Args:
nbr_symbols: number of symbols to use in the distribution.
alpha: float, Zipf's Law Distribution parameter. Default = 1.5.
Usually for modelling natural text distribution is in
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... | python | def zipf_distribution(nbr_symbols, alpha):
"""Helper function: Create a Zipf distribution.
Args:
nbr_symbols: number of symbols to use in the distribution.
alpha: float, Zipf's Law Distribution parameter. Default = 1.5.
Usually for modelling natural text distribution is in
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic.py | zipf_random_sample | def zipf_random_sample(distr_map, sample_len):
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distr_map: list of float, Zipf's distribution over nbr_symbols.
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distr_map: list of float, Zipf's distribution over nbr_symbols.
sample_len: integer, length of sequence to generate.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic.py | reverse_generator_nlplike | def reverse_generator_nlplike(nbr_symbols,
max_length,
nbr_cases,
scale_std_dev=100,
alpha=1.5):
"""Generator for the reversing nlp-like task on sequences of symbols.
The length of the sequence i... | python | def reverse_generator_nlplike(nbr_symbols,
max_length,
nbr_cases,
scale_std_dev=100,
alpha=1.5):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic.py | lower_endian_to_number | def lower_endian_to_number(l, base):
"""Helper function: convert a list of digits in the given base to a number."""
return sum([d * (base**i) for i, d in enumerate(l)]) | python | def lower_endian_to_number(l, base):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic.py | number_to_lower_endian | def number_to_lower_endian(n, base):
"""Helper function: convert a number to a list of digits in the given base."""
if n < base:
return [n]
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/parallel_launch.py | remote_run | def remote_run(cmd, instance_name, detach=False, retries=1):
"""Run command on GCS instance, optionally detached."""
if detach:
cmd = SCREEN.format(command=cmd)
args = SSH.format(instance_name=instance_name).split()
args.append(cmd)
for i in range(retries + 1):
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"""Run command on GCS instance, optionally detached."""
if detach:
cmd = SCREEN.format(command=cmd)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/parallel_launch.py | wait_for_ssh | def wait_for_ssh(ip):
"""Wait for SSH to be available at given IP address."""
for _ in range(12):
with safe_socket() as s:
try:
s.connect((ip, 22))
return True
except socket.timeout:
pass
time.sleep(10)
return False | python | def wait_for_ssh(ip):
"""Wait for SSH to be available at given IP address."""
for _ in range(12):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/parallel_launch.py | launch_instance | def launch_instance(instance_name,
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existing_ip=None,
cpu=1,
mem=4,
code_dir=None,
setup_command=None):
"""Launch a GCE instance."""
# Create instance
ip = existing_ip or create_instance... | python | def launch_instance(instance_name,
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mem=4,
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tensorflow/tensor2tensor | tensor2tensor/models/evolved_transformer.py | evolved_transformer_encoder | def evolved_transformer_encoder(encoder_input,
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save_weights_to=None,
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tensorflow/tensor2tensor | tensor2tensor/models/evolved_transformer.py | _add_attend_to_encoder_cache | def _add_attend_to_encoder_cache(cache, attention_name, hparams, num_layers,
key_channels, value_channels,
vars_3d_num_heads, scope_prefix,
encoder_output):
"""Add attend-to-encoder layers to cache."""
for layer in ra... | python | def _add_attend_to_encoder_cache(cache, attention_name, hparams, num_layers,
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tensorflow/tensor2tensor | tensor2tensor/models/evolved_transformer.py | _init_evolved_transformer_cache | def _init_evolved_transformer_cache(cache, hparams, batch_size,
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tensorflow/tensor2tensor | tensor2tensor/models/evolved_transformer.py | add_evolved_transformer_hparams | def add_evolved_transformer_hparams(hparams):
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Note: These are for the Adam optimizer, not the Adafactor optimizer used in
the paper.
Args:
hparams: Current hparams.
Returns:
hparams updated with Evolved Transformer values.
"""
# Evolved Transformer "layers" a... | python | def add_evolved_transformer_hparams(hparams):
"""Add Evolved Transformer hparams.
Note: These are for the Adam optimizer, not the Adafactor optimizer used in
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Args:
hparams: Current hparams.
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hparams updated with Evolved Transformer values.
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tensorflow/tensor2tensor | tensor2tensor/models/evolved_transformer.py | evolved_transformer_base_tpu | def evolved_transformer_base_tpu():
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hparams.learning_rate_constant = 1 / hparams.learning_rate_warmup_steps ** 0.5
hparams.learning_rate_schedule = (
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"""Base parameters for Evolved Transformer model on TPU."""
hparams = add_evolved_transformer_hparams(transformer.transformer_tpu())
hparams.learning_rate_constant = 1 / hparams.learning_rate_warmup_steps ** 0.5
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tensorflow/tensor2tensor | tensor2tensor/models/evolved_transformer.py | evolved_transformer_big_tpu | def evolved_transformer_big_tpu():
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"""Big parameters for Evolved Transformer model on TPU."""
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hparams.learning_rate_constant = 1 / hparams.learning_rate_warmup_steps ** 0.5
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tensorflow/tensor2tensor | tensor2tensor/models/research/moe.py | transformer_moe_layer_v1 | def transformer_moe_layer_v1(inputs, output_dim, hparams, train,
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slice_dtype=tf.float32):
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tensorflow/tensor2tensor | tensor2tensor/models/research/moe.py | transformer_moe_layer_v2 | def transformer_moe_layer_v2(inputs, output_dim, hparams, train,
master_dtype=tf.bfloat16, slice_dtype=tf.float32):
"""2-level mixture of experts.
Adapted from the paper https://arxiv.org/abs/1701.06538
Note: until the algorithm and inferface solidify, we pass in a hyperparameters
... | python | def transformer_moe_layer_v2(inputs, output_dim, hparams, train,
master_dtype=tf.bfloat16, slice_dtype=tf.float32):
"""2-level mixture of experts.
Adapted from the paper https://arxiv.org/abs/1701.06538
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tensorflow/tensor2tensor | tensor2tensor/models/research/moe.py | _top_2_gating | def _top_2_gating(
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tensorflow/tensor2tensor | tensor2tensor/models/research/moe.py | set_default_moe_hparams | def set_default_moe_hparams(hparams):
"""Add necessary hyperparameters for mixture-of-experts."""
hparams.moe_num_experts = 16
hparams.moe_loss_coef = 1e-2
hparams.add_hparam("moe_gating", "top_2")
# Experts have fixed capacity per batch. We need some extra capacity
# in case gating is not perfectly balanc... | python | def set_default_moe_hparams(hparams):
"""Add necessary hyperparameters for mixture-of-experts."""
hparams.moe_num_experts = 16
hparams.moe_loss_coef = 1e-2
hparams.add_hparam("moe_gating", "top_2")
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tensorflow/tensor2tensor | tensor2tensor/models/research/moe.py | _split_into_groups | def _split_into_groups(n, max_group_size, mesh_dim_size):
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We have a dimension with size n and we want to split it into
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tensorflow/tensor2tensor | tensor2tensor/rl/envs/in_graph_batch_env.py | InGraphBatchEnv.reset | def reset(self, indices=None):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset.
Returns:
Batch tensor of the new observations.
"""
return tf.cond(
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"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset.
Returns:
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tensorflow/tensor2tensor | tensor2tensor/utils/adafactor.py | adafactor_decay_rate_adam | def adafactor_decay_rate_adam(beta2):
"""Second-moment decay rate like Adam, subsuming the correction factor.
Args:
beta2: a float between 0 and 1
Returns:
a scalar
"""
t = tf.to_float(tf.train.get_or_create_global_step()) + 1.0
decay = beta2 * (1.0 - tf.pow(beta2, t - 1.0)) / (1.0 - tf.pow(beta2, ... | python | def adafactor_decay_rate_adam(beta2):
"""Second-moment decay rate like Adam, subsuming the correction factor.
Args:
beta2: a float between 0 and 1
Returns:
a scalar
"""
t = tf.to_float(tf.train.get_or_create_global_step()) + 1.0
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tensorflow/tensor2tensor | tensor2tensor/utils/adafactor.py | adafactor_optimizer_from_hparams | def adafactor_optimizer_from_hparams(hparams, lr):
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Args:
hparams: model hyperparameters
lr: learning rate scalar.
Returns:
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Raises:
ValueError: on illegal values
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Args:
hparams: model hyperparameters
lr: learning rate scalar.
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | _nargs_validator | def _nargs_validator(nargs, message):
"""Makes validator for function to ensure it takes nargs args."""
if message is None:
message = "Registered function must take exactly %d arguments" % nargs
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del key
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"""Makes validator for function to ensure it takes nargs args."""
if message is None:
message = "Registered function must take exactly %d arguments" % nargs
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | parse_problem_name | def parse_problem_name(name):
"""Determines if problem_name specifies a copy and/or reversal.
Args:
name: str, problem name, possibly with suffixes.
Returns:
ProblemSpec: namedtuple with ["base_name", "was_reversed", "was_copy"]
Raises:
ValueError if name contains multiple suffixes of the same ty... | python | def parse_problem_name(name):
"""Determines if problem_name specifies a copy and/or reversal.
Args:
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | get_problem_name | def get_problem_name(base_name, was_reversed=False, was_copy=False):
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Inverse of `parse_problem_name`.
Args:
base_name: base problem name. Should not end in "_rev" or "_copy"
was_reversed: if the problem is to be reversed
was_copy: if t... | python | def get_problem_name(base_name, was_reversed=False, was_copy=False):
"""Construct a problem name from base and reversed/copy options.
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base_name: base problem name. Should not end in "_rev" or "_copy"
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | optimizer | def optimizer(name):
"""Get pre-registered optimizer keyed by name.
`name` should be snake case, though SGD -> sgd, RMSProp -> rms_prop and
UpperCamelCase -> snake_case conversions included for legacy support.
Args:
name: name of optimizer used in registration. This should be a snake case
identifier... | python | def optimizer(name):
"""Get pre-registered optimizer keyed by name.
`name` should be snake case, though SGD -> sgd, RMSProp -> rms_prop and
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | problem | def problem(problem_name, **kwargs):
"""Get possibly copied/reversed problem in `base_registry` or `env_registry`.
Args:
problem_name: string problem name. See `parse_problem_name`.
**kwargs: forwarded to env problem's initialize method.
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"""Get possibly copied/reversed problem in `base_registry` or `env_registry`.
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | env_problem | def env_problem(env_problem_name, **kwargs):
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"""Get and initialize the `EnvProblem` with the given name and batch size.
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env_problem_name: string name of the registered env problem.
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | display_list_by_prefix | def display_list_by_prefix(names_list, starting_spaces=0):
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cur_prefix, result_lines = None, []
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resul... | python | def display_list_by_prefix(names_list, starting_spaces=0):
"""Creates a help string for names_list grouped by prefix."""
cur_prefix, result_lines = None, []
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | help_string | def help_string():
"""Generate help string with contents of registry."""
help_str = """
Registry contents:
------------------
Models:
%s
HParams:
%s
RangedHParams:
%s
Problems:
%s
Optimizers:
%s
Attacks:
%s
Attack HParams:
%s
Pruning HParams:
%s
Pruning Strategies:
%s
Env Problems:
%s
... | python | def help_string():
"""Generate help string with contents of registry."""
help_str = """
Registry contents:
------------------
Models:
%s
HParams:
%s
RangedHParams:
%s
Problems:
%s
Optimizers:
%s
Attacks:
%s
Attack HParams:
%s
Pruning HParams:
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Pruning Strategies:
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | Registry.validate | def validate(self, key, value):
"""Validation function run before setting. Uses function from __init__."""
if self._validator is not None:
self._validator(key, value) | python | def validate(self, key, value):
"""Validation function run before setting. Uses function from __init__."""
if self._validator is not None:
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self._on_set(key, value) | python | def on_set(self, key, value):
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tensorflow/tensor2tensor | tensor2tensor/utils/registry.py | Registry.register | def register(self, key_or_value=None):
"""Decorator to register a function, or registration itself.
This is primarily intended for use as a decorator, either with or without
a key/parentheses.
```python
@my_registry.register('key1')
def value_fn(x, y, z):
pass
@my_registry.register()... | python | def register(self, key_or_value=None):
"""Decorator to register a function, or registration itself.
This is primarily intended for use as a decorator, either with or without
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```python
@my_registry.register('key1')
def value_fn(x, y, z):
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Microsoft/LightGBM | helpers/check_dynamic_dependencies.py | check_dependicies | def check_dependicies(objdump_string):
"""Check the dynamic symbol versions.
Parameters
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objdump_string : string
The dynamic symbol table entries of the file (result of `objdump -T` command).
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"""Check the dynamic symbol versions.
Parameters
----------
objdump_string : string
The dynamic symbol table entries of the file (result of `objdump -T` command).
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Microsoft/LightGBM | python-package/lightgbm/sklearn.py | _objective_function_wrapper | def _objective_function_wrapper(func):
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Note
----
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
and you should group grad and hess ... | python | def _objective_function_wrapper(func):
"""Decorate an objective function.
Note
----
For multi-class task, the y_pred is group by class_id first, then group by row_id.
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Microsoft/LightGBM | python-package/lightgbm/sklearn.py | _eval_function_wrapper | def _eval_function_wrapper(func):
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Note
----
For multi-class task, the y_pred is group by class_id first, then group by row_id.
If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].
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Note
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Microsoft/LightGBM | python-package/lightgbm/sklearn.py | LGBMModel.get_params | def get_params(self, deep=True):
"""Get parameters for this estimator.
Parameters
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deep : bool, optional (default=True)
If True, will return the parameters for this estimator and
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Returns
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Parameters
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Microsoft/LightGBM | python-package/lightgbm/sklearn.py | LGBMModel.predict | def predict(self, X, raw_score=False, num_iteration=None,
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"""Return the predicted value for each sample.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
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Microsoft/LightGBM | python-package/lightgbm/sklearn.py | LGBMModel.feature_importances_ | def feature_importances_(self):
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Note
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``importance_type`` attribute is passed to the function
... | python | def feature_importances_(self):
"""Get feature importances.
Note
----
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Microsoft/LightGBM | python-package/lightgbm/sklearn.py | LGBMRegressor.fit | def fit(self, X, y,
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Microsoft/LightGBM | python-package/lightgbm/sklearn.py | LGBMRanker.fit | def fit(self, X, y,
sample_weight=None, init_score=None, group=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None, eval_group=None, eval_metric=None,
eval_at=[1], early_stopping_rounds=None, verbose=True,
feature_name='auto', c... | python | def fit(self, X, y,
sample_weight=None, init_score=None, group=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None, eval_group=None, eval_metric=None,
eval_at=[1], early_stopping_rounds=None, verbose=True,
feature_name='auto', c... | [
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Microsoft/LightGBM | helpers/parameter_generator.py | get_parameter_infos | def get_parameter_infos(config_hpp):
"""Parse config header file.
Parameters
----------
config_hpp : string
Path to the config header file.
Returns
-------
infos : tuple
Tuple with names and content of sections.
"""
is_inparameter = False
parameter_group = None
... | python | def get_parameter_infos(config_hpp):
"""Parse config header file.
Parameters
----------
config_hpp : string
Path to the config header file.
Returns
-------
infos : tuple
Tuple with names and content of sections.
"""
is_inparameter = False
parameter_group = None
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Microsoft/LightGBM | helpers/parameter_generator.py | get_names | def get_names(infos):
"""Get names of all parameters.
Parameters
----------
infos : list
Content of the config header file.
Returns
-------
names : list
Names of all parameters.
"""
names = []
for x in infos:
for y in x:
names.append(y["name"... | python | def get_names(infos):
"""Get names of all parameters.
Parameters
----------
infos : list
Content of the config header file.
Returns
-------
names : list
Names of all parameters.
"""
names = []
for x in infos:
for y in x:
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Microsoft/LightGBM | helpers/parameter_generator.py | get_alias | def get_alias(infos):
"""Get aliases of all parameters.
Parameters
----------
infos : list
Content of the config header file.
Returns
-------
pairs : list
List of tuples (param alias, param name).
"""
pairs = []
for x in infos:
for y in x:
if... | python | def get_alias(infos):
"""Get aliases of all parameters.
Parameters
----------
infos : list
Content of the config header file.
Returns
-------
pairs : list
List of tuples (param alias, param name).
"""
pairs = []
for x in infos:
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Microsoft/LightGBM | helpers/parameter_generator.py | set_one_var_from_string | def set_one_var_from_string(name, param_type, checks):
"""Construct code for auto config file for one param value.
Parameters
----------
name : string
Name of the parameter.
param_type : string
Type of the parameter.
checks : list
Constraints of the parameter.
Retur... | python | def set_one_var_from_string(name, param_type, checks):
"""Construct code for auto config file for one param value.
Parameters
----------
name : string
Name of the parameter.
param_type : string
Type of the parameter.
checks : list
Constraints of the parameter.
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Microsoft/LightGBM | helpers/parameter_generator.py | gen_parameter_description | def gen_parameter_description(sections, descriptions, params_rst):
"""Write descriptions of parameters to the documentation file.
Parameters
----------
sections : list
Names of parameters sections.
descriptions : list
Structured descriptions of parameters.
params_rst : string
... | python | def gen_parameter_description(sections, descriptions, params_rst):
"""Write descriptions of parameters to the documentation file.
Parameters
----------
sections : list
Names of parameters sections.
descriptions : list
Structured descriptions of parameters.
params_rst : string
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Microsoft/LightGBM | helpers/parameter_generator.py | gen_parameter_code | def gen_parameter_code(config_hpp, config_out_cpp):
"""Generate auto config file.
Parameters
----------
config_hpp : string
Path to the config header file.
config_out_cpp : string
Path to the auto config file.
Returns
-------
infos : tuple
Tuple with names and c... | python | def gen_parameter_code(config_hpp, config_out_cpp):
"""Generate auto config file.
Parameters
----------
config_hpp : string
Path to the config header file.
config_out_cpp : string
Path to the auto config file.
Returns
-------
infos : tuple
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Microsoft/LightGBM | python-package/lightgbm/basic.py | _load_lib | def _load_lib():
"""Load LightGBM library."""
lib_path = find_lib_path()
if len(lib_path) == 0:
return None
lib = ctypes.cdll.LoadLibrary(lib_path[0])
lib.LGBM_GetLastError.restype = ctypes.c_char_p
return lib | python | def _load_lib():
"""Load LightGBM library."""
lib_path = find_lib_path()
if len(lib_path) == 0:
return None
lib = ctypes.cdll.LoadLibrary(lib_path[0])
lib.LGBM_GetLastError.restype = ctypes.c_char_p
return lib | [
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Microsoft/LightGBM | python-package/lightgbm/basic.py | list_to_1d_numpy | def list_to_1d_numpy(data, dtype=np.float32, name='list'):
"""Convert data to 1-D numpy array."""
if is_numpy_1d_array(data):
if data.dtype == dtype:
return data
else:
return data.astype(dtype=dtype, copy=False)
elif is_1d_list(data):
return np.array(data, dty... | python | def list_to_1d_numpy(data, dtype=np.float32, name='list'):
"""Convert data to 1-D numpy array."""
if is_numpy_1d_array(data):
if data.dtype == dtype:
return data
else:
return data.astype(dtype=dtype, copy=False)
elif is_1d_list(data):
return np.array(data, dty... | [
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Microsoft/LightGBM | python-package/lightgbm/basic.py | cfloat32_array_to_numpy | def cfloat32_array_to_numpy(cptr, length):
"""Convert a ctypes float pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
return np.fromiter(cptr, dtype=np.float32, count=length)
else:
raise RuntimeError('Expected float pointer') | python | def cfloat32_array_to_numpy(cptr, length):
"""Convert a ctypes float pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
return np.fromiter(cptr, dtype=np.float32, count=length)
else:
raise RuntimeError('Expected float pointer') | [
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Microsoft/LightGBM | python-package/lightgbm/basic.py | cfloat64_array_to_numpy | def cfloat64_array_to_numpy(cptr, length):
"""Convert a ctypes double pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_double)):
return np.fromiter(cptr, dtype=np.float64, count=length)
else:
raise RuntimeError('Expected double pointer') | python | def cfloat64_array_to_numpy(cptr, length):
"""Convert a ctypes double pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_double)):
return np.fromiter(cptr, dtype=np.float64, count=length)
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Microsoft/LightGBM | python-package/lightgbm/basic.py | cint32_array_to_numpy | def cint32_array_to_numpy(cptr, length):
"""Convert a ctypes int pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)):
return np.fromiter(cptr, dtype=np.int32, count=length)
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raise RuntimeError('Expected int pointer') | python | def cint32_array_to_numpy(cptr, length):
"""Convert a ctypes int pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)):
return np.fromiter(cptr, dtype=np.int32, count=length)
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