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mozilla/DeepSpeech | examples/mic_vad_streaming/mic_vad_streaming.py | VADAudio.vad_collector | def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
"""Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being... | python | def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
"""Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being... | [
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fxsjy/jieba | jieba/posseg/__init__.py | cut | def cut(sentence, HMM=True):
"""
Global `cut` function that supports parallel processing.
Note that this only works using dt, custom POSTokenizer
instances are not supported.
"""
global dt
if jieba.pool is None:
for w in dt.cut(sentence, HMM=HMM):
yield w
else:
... | python | def cut(sentence, HMM=True):
"""
Global `cut` function that supports parallel processing.
Note that this only works using dt, custom POSTokenizer
instances are not supported.
"""
global dt
if jieba.pool is None:
for w in dt.cut(sentence, HMM=HMM):
yield w
else:
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fxsjy/jieba | jieba/__init__.py | enable_parallel | def enable_parallel(processnum=None):
"""
Change the module's `cut` and `cut_for_search` functions to the
parallel version.
Note that this only works using dt, custom Tokenizer
instances are not supported.
"""
global pool, dt, cut, cut_for_search
from multiprocessing import cpu_count
... | python | def enable_parallel(processnum=None):
"""
Change the module's `cut` and `cut_for_search` functions to the
parallel version.
Note that this only works using dt, custom Tokenizer
instances are not supported.
"""
global pool, dt, cut, cut_for_search
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fxsjy/jieba | jieba/__init__.py | Tokenizer.cut | def cut(self, sentence, cut_all=False, HMM=True):
'''
The main function that segments an entire sentence that contains
Chinese characters into separated words.
Parameter:
- sentence: The str(unicode) to be segmented.
- cut_all: Model type. True for full pattern, ... | python | def cut(self, sentence, cut_all=False, HMM=True):
'''
The main function that segments an entire sentence that contains
Chinese characters into separated words.
Parameter:
- sentence: The str(unicode) to be segmented.
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fxsjy/jieba | jieba/__init__.py | Tokenizer.cut_for_search | def cut_for_search(self, sentence, HMM=True):
"""
Finer segmentation for search engines.
"""
words = self.cut(sentence, HMM=HMM)
for w in words:
if len(w) > 2:
for i in xrange(len(w) - 1):
gram2 = w[i:i + 2]
if s... | python | def cut_for_search(self, sentence, HMM=True):
"""
Finer segmentation for search engines.
"""
words = self.cut(sentence, HMM=HMM)
for w in words:
if len(w) > 2:
for i in xrange(len(w) - 1):
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if s... | [
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fxsjy/jieba | jieba/__init__.py | Tokenizer.load_userdict | def load_userdict(self, f):
'''
Load personalized dict to improve detect rate.
Parameter:
- f : A plain text file contains words and their ocurrences.
Can be a file-like object, or the path of the dictionary file,
whose encoding must be utf-8.
... | python | def load_userdict(self, f):
'''
Load personalized dict to improve detect rate.
Parameter:
- f : A plain text file contains words and their ocurrences.
Can be a file-like object, or the path of the dictionary file,
whose encoding must be utf-8.
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fxsjy/jieba | jieba/__init__.py | Tokenizer.add_word | def add_word(self, word, freq=None, tag=None):
"""
Add a word to dictionary.
freq and tag can be omitted, freq defaults to be a calculated value
that ensures the word can be cut out.
"""
self.check_initialized()
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freq = int(freq) if ... | python | def add_word(self, word, freq=None, tag=None):
"""
Add a word to dictionary.
freq and tag can be omitted, freq defaults to be a calculated value
that ensures the word can be cut out.
"""
self.check_initialized()
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fxsjy/jieba | jieba/__init__.py | Tokenizer.suggest_freq | def suggest_freq(self, segment, tune=False):
"""
Suggest word frequency to force the characters in a word to be
joined or splitted.
Parameter:
- segment : The segments that the word is expected to be cut into,
If the word should be treated as a whole,... | python | def suggest_freq(self, segment, tune=False):
"""
Suggest word frequency to force the characters in a word to be
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Parameter:
- segment : The segments that the word is expected to be cut into,
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fxsjy/jieba | jieba/__init__.py | Tokenizer.tokenize | def tokenize(self, unicode_sentence, mode="default", HMM=True):
"""
Tokenize a sentence and yields tuples of (word, start, end)
Parameter:
- sentence: the str(unicode) to be segmented.
- mode: "default" or "search", "search" is for finer segmentation.
- HMM: ... | python | def tokenize(self, unicode_sentence, mode="default", HMM=True):
"""
Tokenize a sentence and yields tuples of (word, start, end)
Parameter:
- sentence: the str(unicode) to be segmented.
- mode: "default" or "search", "search" is for finer segmentation.
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fxsjy/jieba | jieba/analyse/textrank.py | TextRank.textrank | def textrank(self, sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v'), withFlag=False):
"""
Extract keywords from sentence using TextRank algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
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"""
Extract keywords from sentence using TextRank algorithm.
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- topK: return how many top keywords. `None` for all possible words.
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fxsjy/jieba | jieba/analyse/tfidf.py | TFIDF.extract_tags | def extract_tags(self, sentence, topK=20, withWeight=False, allowPOS=(), withFlag=False):
"""
Extract keywords from sentence using TF-IDF algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
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"""
Extract keywords from sentence using TF-IDF algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/cleaner_en_xx.py | paracrawl_v3_pairs | def paracrawl_v3_pairs(paracrawl_file):
"""Generates raw (English, other) pairs from a ParaCrawl V3.0 data file.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
Yields:
Pairs of (sentence_en, sentence_xx), as Unicode strings.
Raises:
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"""Generates raw (English, other) pairs from a ParaCrawl V3.0 data file.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/cleaner_en_xx.py | _raw_sentences | def _raw_sentences(paracrawl_file):
"""Generates Unicode strings, one for each <seg> in a ParaCrawl data file.
Also decodes some of the most common HTML entities found in ParaCrawl data.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
Yields:
One Unicode string for each <seg> element in the Pa... | python | def _raw_sentences(paracrawl_file):
"""Generates Unicode strings, one for each <seg> in a ParaCrawl data file.
Also decodes some of the most common HTML entities found in ParaCrawl data.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/cleaner_en_xx.py | clean_en_xx_pairs | def clean_en_xx_pairs(en_xx_pairs):
"""Generates a cleaned-up stream of (English, other) translation pairs.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | _get_case_file_paths | def _get_case_file_paths(tmp_dir, case, training_fraction=0.95):
"""Obtain a list of image paths corresponding to training or eval case.
Args:
tmp_dir: str, the root path to which raw images were written, at the
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case: bool, whether obtaining file paths for tra... | python | def _get_case_file_paths(tmp_dir, case, training_fraction=0.95):
"""Obtain a list of image paths corresponding to training or eval case.
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tmp_dir: str, the root path to which raw images were written, at the
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tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | maybe_download_image_dataset | def maybe_download_image_dataset(image_ids, target_dir):
"""Download a set of images from api.brain-map.org to `target_dir`.
Args:
image_ids: list, a list of image ids.
target_dir: str, a directory to which to download the images.
"""
tf.gfile.MakeDirs(target_dir)
num_images = len(image_ids)
for... | python | def maybe_download_image_dataset(image_ids, target_dir):
"""Download a set of images from api.brain-map.org to `target_dir`.
Args:
image_ids: list, a list of image ids.
target_dir: str, a directory to which to download the images.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | random_square_mask | def random_square_mask(shape, fraction):
"""Create a numpy array with specified shape and masked fraction.
Args:
shape: tuple, shape of the mask to create.
fraction: float, fraction of the mask area to populate with `mask_scalar`.
Returns:
numpy.array: A numpy array storing the mask.
"""
mask =... | python | def random_square_mask(shape, fraction):
"""Create a numpy array with specified shape and masked fraction.
Args:
shape: tuple, shape of the mask to create.
fraction: float, fraction of the mask area to populate with `mask_scalar`.
Returns:
numpy.array: A numpy array storing the mask.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/allen_brain.py | _generator | def _generator(tmp_dir, training, size=_BASE_EXAMPLE_IMAGE_SIZE,
training_fraction=0.95):
"""Base problem example generator for Allen Brain Atlas problems.
Args:
tmp_dir: str, a directory where raw example input data has been stored.
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tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_moe.py | transformer_moe_base | def transformer_moe_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.norm_type = "layer"
hparams.hidden_size = 512
hparams.batch_size = 4096
hparams.max_length = 2001
hparams.max_input_seq_length = 2000
hparams.max_target_seq_length = 2000
hparams.dropout = 0.0
... | python | def transformer_moe_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.norm_type = "layer"
hparams.hidden_size = 512
hparams.batch_size = 4096
hparams.max_length = 2001
hparams.max_input_seq_length = 2000
hparams.max_target_seq_length = 2000
hparams.dropout = 0.0
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tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_moe.py | transformer_moe_8k | def transformer_moe_8k():
"""Hyper parameters specifics for long sequence generation."""
hparams = transformer_moe_base()
hparams.batch_size = 8192
hparams.max_length = 0 # max_length == batch_size
hparams.eval_drop_long_sequences = True
hparams.min_length_bucket = 256 # Avoid cyclic problems for big bat... | python | def transformer_moe_8k():
"""Hyper parameters specifics for long sequence generation."""
hparams = transformer_moe_base()
hparams.batch_size = 8192
hparams.max_length = 0 # max_length == batch_size
hparams.eval_drop_long_sequences = True
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tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_moe.py | transformer_moe_2k | def transformer_moe_2k():
"""Base transformers model with moe.
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* No encoder.
* Layer 0: a - sep (self-attention - unmasked separable convolutions)
* Layer 1: a - sep
* Layer 2: a - sep
* Layer 3: a - sep
* Layer 4: a - sep
* Decoder architecture:
*... | python | def transformer_moe_2k():
"""Base transformers model with moe.
Will have the following architecture:
* No encoder.
* Layer 0: a - sep (self-attention - unmasked separable convolutions)
* Layer 1: a - sep
* Layer 2: a - sep
* Layer 3: a - sep
* Layer 4: a - sep
* Decoder architecture:
*... | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_moe.py | transformer_moe_prepend_8k | def transformer_moe_prepend_8k():
"""Model which formulate a seq2seq problem as language modeling."""
hparams = transformer_moe_8k()
hparams.prepend_mode = "prepend_inputs_masked_attention"
hparams.eval_drop_long_sequences = False
hparams.max_input_seq_length = 7500
hparams.default_ff = "sepm"
hparams.lay... | python | def transformer_moe_prepend_8k():
"""Model which formulate a seq2seq problem as language modeling."""
hparams = transformer_moe_8k()
hparams.prepend_mode = "prepend_inputs_masked_attention"
hparams.eval_drop_long_sequences = False
hparams.max_input_seq_length = 7500
hparams.default_ff = "sepm"
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | f | def f(x, depth1, depth2, dim='2d', first_batch_norm=True, stride=1,
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"""Applies residual function for RevNet.
Args:
x: input tensor
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training=True, bottleneck=True, padding='SAME'):
"""Applies residual function for RevNet.
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | downsample_bottleneck | def downsample_bottleneck(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using a 1x1 convolution filter.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: What... | python | def downsample_bottleneck(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using a 1x1 convolution filter.
Args:
x: input tensor of size [N, H, W, C]
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | downsample_residual | def downsample_residual(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using average pooling.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: What stride to ... | python | def downsample_residual(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using average pooling.
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x: input tensor of size [N, H, W, C]
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | init | def init(images, num_channels, dim='2d', stride=2,
kernel_size=7, maxpool=True, training=True, scope='init'):
"""Standard ResNet initial block used as first RevNet block.
Args:
images: [N, H, W, 3] tensor of input images to the model.
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kernel_size=7, maxpool=True, training=True, scope='init'):
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | unit | def unit(x1, x2, block_num, depth, num_layers, dim='2d',
bottleneck=True, first_batch_norm=True, stride=1, training=True):
"""Implements bottleneck RevNet unit from authors' RevNet architecture.
Args:
x1: [N, H, W, C] tensor of network activations.
x2: [N, H, W, C] tensor of network activations.
... | python | def unit(x1, x2, block_num, depth, num_layers, dim='2d',
bottleneck=True, first_batch_norm=True, stride=1, training=True):
"""Implements bottleneck RevNet unit from authors' RevNet architecture.
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x1: [N, H, W, C] tensor of network activations.
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | final_block | def final_block(x1, x2, dim='2d', training=True, scope='final_block'):
"""Converts activations from last RevNet block to pre-logits.
Args:
x1: [NxHxWxC] tensor of network activations.
x2: [NxHxWxC] tensor of network activations.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
training: True for ... | python | def final_block(x1, x2, dim='2d', training=True, scope='final_block'):
"""Converts activations from last RevNet block to pre-logits.
Args:
x1: [NxHxWxC] tensor of network activations.
x2: [NxHxWxC] tensor of network activations.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet | def revnet(inputs, hparams, reuse=None):
"""Uses Tensor2Tensor memory optimized RevNet block to build a RevNet.
Args:
inputs: [NxHxWx3] tensor of input images to the model.
hparams: HParams object that contains the following parameters,
in addition to the parameters contained in the basic_params1() o... | python | def revnet(inputs, hparams, reuse=None):
"""Uses Tensor2Tensor memory optimized RevNet block to build a RevNet.
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inputs: [NxHxWx3] tensor of input images to the model.
hparams: HParams object that contains the following parameters,
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet_base | def revnet_base():
"""Default hparams for Revnet."""
hparams = common_hparams.basic_params1()
hparams.add_hparam('num_channels', [64, 128, 256, 416])
hparams.add_hparam('num_layers_per_block', [1, 1, 10, 1])
hparams.add_hparam('bottleneck', True)
hparams.add_hparam('first_batch_norm', [False, True, True, Tr... | python | def revnet_base():
"""Default hparams for Revnet."""
hparams = common_hparams.basic_params1()
hparams.add_hparam('num_channels', [64, 128, 256, 416])
hparams.add_hparam('num_layers_per_block', [1, 1, 10, 1])
hparams.add_hparam('bottleneck', True)
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet_cifar_base | def revnet_cifar_base():
"""Tiny hparams suitable for CIFAR/etc."""
hparams = revnet_base()
hparams.num_channels_init_block = 32
hparams.first_batch_norm = [False, True, True]
hparams.init_stride = 1
hparams.init_kernel_size = 3
hparams.init_maxpool = False
hparams.strides = [1, 2, 2]
hparams.batch_si... | python | def revnet_cifar_base():
"""Tiny hparams suitable for CIFAR/etc."""
hparams = revnet_base()
hparams.num_channels_init_block = 32
hparams.first_batch_norm = [False, True, True]
hparams.init_stride = 1
hparams.init_kernel_size = 3
hparams.init_maxpool = False
hparams.strides = [1, 2, 2]
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet_110_cifar | def revnet_110_cifar():
"""Tiny hparams suitable for CIFAR/etc."""
hparams = revnet_cifar_base()
hparams.bottleneck = False
hparams.num_channels = [16, 32, 64]
hparams.num_layers_per_block = [8, 8, 8]
return hparams | python | def revnet_110_cifar():
"""Tiny hparams suitable for CIFAR/etc."""
hparams = revnet_cifar_base()
hparams.bottleneck = False
hparams.num_channels = [16, 32, 64]
hparams.num_layers_per_block = [8, 8, 8]
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet_164_cifar | def revnet_164_cifar():
"""Tiny hparams suitable for CIFAR/etc."""
hparams = revnet_cifar_base()
hparams.bottleneck = True
hparams.num_channels = [16, 32, 64]
hparams.num_layers_per_block = [8, 8, 8]
return hparams | python | def revnet_164_cifar():
"""Tiny hparams suitable for CIFAR/etc."""
hparams = revnet_cifar_base()
hparams.bottleneck = True
hparams.num_channels = [16, 32, 64]
hparams.num_layers_per_block = [8, 8, 8]
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/revnet.py | revnet_range | def revnet_range(rhp):
"""Hyperparameters for tuning revnet."""
rhp.set_float('learning_rate', 0.05, 0.2, scale=rhp.LOG_SCALE)
rhp.set_float('weight_decay', 1e-5, 1e-3, scale=rhp.LOG_SCALE)
rhp.set_discrete('num_channels_init_block', [64, 128])
return rhp | python | def revnet_range(rhp):
"""Hyperparameters for tuning revnet."""
rhp.set_float('learning_rate', 0.05, 0.2, scale=rhp.LOG_SCALE)
rhp.set_float('weight_decay', 1e-5, 1e-3, scale=rhp.LOG_SCALE)
rhp.set_discrete('num_channels_init_block', [64, 128])
return rhp | [
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_basic_deterministic | def next_frame_basic_deterministic():
"""Basic 2-frame conv model."""
hparams = base.next_frame_base()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 1
hparams.hidden_size = 64
hparams.batch_size = 4
hparams.num_hidden_layers = 2
hparams.optimizer = "Adafactor"
hparams.learning_r... | python | def next_frame_basic_deterministic():
"""Basic 2-frame conv model."""
hparams = base.next_frame_base()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 1
hparams.hidden_size = 64
hparams.batch_size = 4
hparams.num_hidden_layers = 2
hparams.optimizer = "Adafactor"
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_pixel_noise | def next_frame_pixel_noise():
"""Basic 2-frame conv model with pixel noise."""
hparams = next_frame_basic_deterministic()
hparams.add_hparam("video_modality_input_noise", 0.05)
hparams.bottom["inputs"] = modalities.video_pixel_noise_bottom
hparams.top["inputs"] = modalities.video_top
return hparams | python | def next_frame_pixel_noise():
"""Basic 2-frame conv model with pixel noise."""
hparams = next_frame_basic_deterministic()
hparams.add_hparam("video_modality_input_noise", 0.05)
hparams.bottom["inputs"] = modalities.video_pixel_noise_bottom
hparams.top["inputs"] = modalities.video_top
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_sampling | def next_frame_sampling():
"""Basic conv model with scheduled sampling."""
hparams = next_frame_basic_deterministic()
hparams.scheduled_sampling_mode = "prob_inverse_exp"
hparams.scheduled_sampling_max_prob = 1.0
hparams.scheduled_sampling_decay_steps = 10000
return hparams | python | def next_frame_sampling():
"""Basic conv model with scheduled sampling."""
hparams = next_frame_basic_deterministic()
hparams.scheduled_sampling_mode = "prob_inverse_exp"
hparams.scheduled_sampling_max_prob = 1.0
hparams.scheduled_sampling_decay_steps = 10000
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_ae | def next_frame_ae():
"""Conv autoencoder."""
hparams = next_frame_basic_deterministic()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.hidden_size = 256
hparams.batch_size = 8
hparams.num_hidden_layers = 4
hparams.num_compress_steps = 4
... | python | def next_frame_ae():
"""Conv autoencoder."""
hparams = next_frame_basic_deterministic()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.hidden_size = 256
hparams.batch_size = 8
hparams.num_hidden_layers = 4
hparams.num_compress_steps = 4
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_ae_tiny | def next_frame_ae_tiny():
"""Conv autoencoder, tiny set for testing."""
hparams = next_frame_tiny()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.batch_size = 8
hparams.dropout = 0.4
return hparams | python | def next_frame_ae_tiny():
"""Conv autoencoder, tiny set for testing."""
hparams = next_frame_tiny()
hparams.bottom["inputs"] = modalities.video_bitwise_bottom
hparams.top["inputs"] = modalities.video_top
hparams.batch_size = 8
hparams.dropout = 0.4
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_tiny | def next_frame_tiny():
"""Tiny for testing."""
hparams = next_frame_basic_deterministic()
hparams.hidden_size = 32
hparams.num_hidden_layers = 1
hparams.num_compress_steps = 2
hparams.filter_double_steps = 1
return hparams | python | def next_frame_tiny():
"""Tiny for testing."""
hparams = next_frame_basic_deterministic()
hparams.hidden_size = 32
hparams.num_hidden_layers = 1
hparams.num_compress_steps = 2
hparams.filter_double_steps = 1
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_l1 | def next_frame_l1():
"""Basic conv model with L1 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l1_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
return hparams | python | def next_frame_l1():
"""Basic conv model with L1 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l1_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_l2 | def next_frame_l2():
"""Basic conv model with L2 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l2_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
return hparams | python | def next_frame_l2():
"""Basic conv model with L2 modality."""
hparams = next_frame_basic_deterministic()
hparams.loss["targets"] = modalities.video_l2_loss
hparams.top["targets"] = modalities.video_l1_top
hparams.video_modality_loss_cutoff = 2.4
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_base_range | def next_frame_base_range(rhp):
"""Basic tuning grid."""
rhp.set_float("dropout", 0.2, 0.6)
rhp.set_discrete("hidden_size", [64, 128, 256])
rhp.set_int("num_compress_steps", 5, 8)
rhp.set_discrete("batch_size", [4, 8, 16, 32])
rhp.set_int("num_hidden_layers", 1, 3)
rhp.set_int("filter_double_steps", 1, 6)... | python | def next_frame_base_range(rhp):
"""Basic tuning grid."""
rhp.set_float("dropout", 0.2, 0.6)
rhp.set_discrete("hidden_size", [64, 128, 256])
rhp.set_int("num_compress_steps", 5, 8)
rhp.set_discrete("batch_size", [4, 8, 16, 32])
rhp.set_int("num_hidden_layers", 1, 3)
rhp.set_int("filter_double_steps", 1, 6)... | [
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tensorflow/tensor2tensor | tensor2tensor/models/video/basic_deterministic_params.py | next_frame_ae_range | def next_frame_ae_range(rhp):
"""Autoencoder world model tuning grid."""
rhp.set_float("dropout", 0.3, 0.5)
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rhp.set_int("num_hidden_layers", 2, 6)
rhp.set_float("learning_rate_constant", 1., 2.)
rhp.set_float("initializer_gain", 0.8, 1.5)
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"""Autoencoder world model tuning grid."""
rhp.set_float("dropout", 0.3, 0.5)
rhp.set_int("num_compress_steps", 1, 3)
rhp.set_int("num_hidden_layers", 2, 6)
rhp.set_float("learning_rate_constant", 1., 2.)
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tensorflow/tensor2tensor | tensor2tensor/models/research/multiquery_paper.py | mqp_lm1b_base | def mqp_lm1b_base():
"""Series of architectures for language modeling."""
hparams = mtf_transformer2.mtf_unitransformer_base()
hparams.d_model = 1024
hparams.max_length = 256
hparams.batch_size = 256
# Parameters for my_layer_stack()
hparams.num_hidden_layers = 6
hparams.d_ff = 8192
hparams.d_kv = 128... | python | def mqp_lm1b_base():
"""Series of architectures for language modeling."""
hparams = mtf_transformer2.mtf_unitransformer_base()
hparams.d_model = 1024
hparams.max_length = 256
hparams.batch_size = 256
# Parameters for my_layer_stack()
hparams.num_hidden_layers = 6
hparams.d_ff = 8192
hparams.d_kv = 128... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_free.py | initialize_env_specs | def initialize_env_specs(hparams, env_problem_name):
"""Initializes env_specs using the appropriate env."""
if env_problem_name:
env = registry.env_problem(env_problem_name, batch_size=hparams.batch_size)
else:
env = rl_utils.setup_env(hparams, hparams.batch_size,
hparams.eval... | python | def initialize_env_specs(hparams, env_problem_name):
"""Initializes env_specs using the appropriate env."""
if env_problem_name:
env = registry.env_problem(env_problem_name, batch_size=hparams.batch_size)
else:
env = rl_utils.setup_env(hparams, hparams.batch_size,
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tensorflow/tensor2tensor | tensor2tensor/rl/trainer_model_free.py | train | def train(hparams, output_dir, env_problem_name, report_fn=None):
"""Train."""
env_fn = initialize_env_specs(hparams, env_problem_name)
tf.logging.vlog(1, "HParams in trainer_model_free.train : %s",
misc_utils.pprint_hparams(hparams))
tf.logging.vlog(1, "Using hparams.base_algo: %s", hparams.... | python | def train(hparams, output_dir, env_problem_name, report_fn=None):
"""Train."""
env_fn = initialize_env_specs(hparams, env_problem_name)
tf.logging.vlog(1, "HParams in trainer_model_free.train : %s",
misc_utils.pprint_hparams(hparams))
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tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | learning_rate_factor | def learning_rate_factor(name, step_num, hparams):
"""Compute the designated learning rate factor from hparams."""
if name == "constant":
tf.logging.info("Base learning rate: %f", hparams.learning_rate_constant)
return hparams.learning_rate_constant
elif name == "linear_warmup":
return tf.minimum(1.0,... | python | def learning_rate_factor(name, step_num, hparams):
"""Compute the designated learning rate factor from hparams."""
if name == "constant":
tf.logging.info("Base learning rate: %f", hparams.learning_rate_constant)
return hparams.learning_rate_constant
elif name == "linear_warmup":
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tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | learning_rate_schedule | def learning_rate_schedule(hparams):
"""Learning rate schedule based on hparams."""
mlperf_log.transformer_print(key=mlperf_log.OPT_LR, deferred=True)
mlperf_log.transformer_print(
key=mlperf_log.OPT_LR_WARMUP_STEPS,
value=hparams.learning_rate_warmup_steps)
step_num = _global_step(hparams)
schedu... | python | def learning_rate_schedule(hparams):
"""Learning rate schedule based on hparams."""
mlperf_log.transformer_print(key=mlperf_log.OPT_LR, deferred=True)
mlperf_log.transformer_print(
key=mlperf_log.OPT_LR_WARMUP_STEPS,
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tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | legacy_learning_rate_schedule | def legacy_learning_rate_schedule(hparams):
"""Backwards-compatible learning-rate schedule."""
step_num = _global_step(hparams)
warmup_steps = tf.to_float(hparams.learning_rate_warmup_steps)
if hparams.learning_rate_decay_scheme == "noam":
ret = 5000.0 * hparams.hidden_size**-0.5 * tf.minimum(
(step... | python | def legacy_learning_rate_schedule(hparams):
"""Backwards-compatible learning-rate schedule."""
step_num = _global_step(hparams)
warmup_steps = tf.to_float(hparams.learning_rate_warmup_steps)
if hparams.learning_rate_decay_scheme == "noam":
ret = 5000.0 * hparams.hidden_size**-0.5 * tf.minimum(
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tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _global_step | def _global_step(hparams):
"""Adjust global step if a multi-step optimizer is used."""
step = tf.to_float(tf.train.get_or_create_global_step())
multiplier = hparams.optimizer_multistep_accumulate_steps
if not multiplier:
return step
tf.logging.info("Dividing global step by %d for multi-step optimizer."
... | python | def _global_step(hparams):
"""Adjust global step if a multi-step optimizer is used."""
step = tf.to_float(tf.train.get_or_create_global_step())
multiplier = hparams.optimizer_multistep_accumulate_steps
if not multiplier:
return step
tf.logging.info("Dividing global step by %d for multi-step optimizer."
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tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _piecewise_learning_rate | def _piecewise_learning_rate(step, boundaries, values):
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Args:
step: global step
boundaries: List of steps to transition on.
values: Multiplier to apply at each boundary transition.
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"""Scale learning rate according to the given schedule.
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step: global step
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tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _learning_rate_decay | def _learning_rate_decay(hparams, warmup_steps=0):
"""Learning rate decay multiplier."""
scheme = hparams.learning_rate_decay_scheme
warmup_steps = tf.to_float(warmup_steps)
global_step = _global_step(hparams)
if not scheme or scheme == "none":
return tf.constant(1.)
tf.logging.info("Applying learning... | python | def _learning_rate_decay(hparams, warmup_steps=0):
"""Learning rate decay multiplier."""
scheme = hparams.learning_rate_decay_scheme
warmup_steps = tf.to_float(warmup_steps)
global_step = _global_step(hparams)
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tensorflow/tensor2tensor | tensor2tensor/utils/learning_rate.py | _learning_rate_warmup | def _learning_rate_warmup(warmup_steps, warmup_schedule="exp", hparams=None):
"""Learning rate warmup multiplier."""
if not warmup_steps:
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tf.logging.info("Applying %s learning rate warmup for %d steps",
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"""Learning rate warmup multiplier."""
if not warmup_steps:
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | is_in_expr | def is_in_expr(expr, find):
"""Returns True if `find` is a subtree of `expr`."""
return expr == find or (isinstance(expr, ExprNode) and expr.is_in(find)) | python | def is_in_expr(expr, find):
"""Returns True if `find` is a subtree of `expr`."""
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | random_expr_with_required_var | def random_expr_with_required_var(depth, required_var, optional_list, ops):
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depth: At least one leaf will be this many levels down from the top.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | random_expr | def random_expr(depth, vlist, ops):
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depth: At least one leaf will be this many levels down from the top.
vlist: A list of chars. These chars are randomly selected as leaf values.
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"""Generate a random expression tree.
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depth: At least one leaf will be this many levels down from the top.
vlist: A list of chars. These chars are randomly selected as leaf values.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | algebra_inverse_solve | def algebra_inverse_solve(left, right, var, solve_ops):
"""Solves for the value of the given var in an expression.
Args:
left: The root of the ExprNode tree on the left side of the equals sign.
right: The root of the ExprNode tree on the right side of the equals sign.
var: A char. The variable to solve... | python | def algebra_inverse_solve(left, right, var, solve_ops):
"""Solves for the value of the given var in an expression.
Args:
left: The root of the ExprNode tree on the left side of the equals sign.
right: The root of the ExprNode tree on the right side of the equals sign.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | format_sympy_expr | def format_sympy_expr(sympy_expr, functions=None):
"""Convert sympy expression into a string which can be encoded.
Args:
sympy_expr: Any sympy expression tree or string.
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sympy_expr: Any sympy expression tree or string.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | generate_algebra_inverse_sample | def generate_algebra_inverse_sample(vlist, ops, solve_ops, min_depth,
max_depth):
"""Randomly generate an algebra inverse dataset sample.
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variable.
Args:
vlist: Variable list. List of chars that c... | python | def generate_algebra_inverse_sample(vlist, ops, solve_ops, min_depth,
max_depth):
"""Randomly generate an algebra inverse dataset sample.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | generate_algebra_simplify_sample | def generate_algebra_simplify_sample(vlist, ops, min_depth, max_depth):
"""Randomly generate an algebra simplify dataset sample.
Given an input expression, produce the simplified expression.
Args:
vlist: Variable list. List of chars that can be used in the expression.
ops: List of ExprOp instances. The ... | python | def generate_algebra_simplify_sample(vlist, ops, min_depth, max_depth):
"""Randomly generate an algebra simplify dataset sample.
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vlist: Variable list. List of chars that can be used in the expression.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | generate_calculus_integrate_sample | def generate_calculus_integrate_sample(vlist, ops, min_depth, max_depth,
functions):
"""Randomly generate a symbolic integral dataset sample.
Given an input expression, produce the indefinite integral.
Args:
vlist: Variable list. List of chars that can be used in the e... | python | def generate_calculus_integrate_sample(vlist, ops, min_depth, max_depth,
functions):
"""Randomly generate a symbolic integral dataset sample.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | math_dataset_init | def math_dataset_init(alphabet_size=26, digits=None, functions=None):
"""Initializes required objects to generate symbolic math datasets.
Produces token set, ExprOp instances, solve_op dictionary, encoders, and
decoders needed to generate the algebra inverse dataset.
Args:
alphabet_size: How many possible... | python | def math_dataset_init(alphabet_size=26, digits=None, functions=None):
"""Initializes required objects to generate symbolic math datasets.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | algebra_inverse | def algebra_inverse(alphabet_size=26, min_depth=0, max_depth=2,
nbr_cases=10000):
"""Generate the algebra inverse dataset.
Each sample is a symbolic math equation involving unknown variables. The
task is to solve for the given variable. The target is the resulting
expression.
Args:
a... | python | def algebra_inverse(alphabet_size=26, min_depth=0, max_depth=2,
nbr_cases=10000):
"""Generate the algebra inverse dataset.
Each sample is a symbolic math equation involving unknown variables. The
task is to solve for the given variable. The target is the resulting
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | algebra_simplify | def algebra_simplify(alphabet_size=26,
min_depth=0,
max_depth=2,
nbr_cases=10000):
"""Generate the algebra simplify dataset.
Each sample is a symbolic math expression involving unknown variables. The
task is to simplify the expression. The target is ... | python | def algebra_simplify(alphabet_size=26,
min_depth=0,
max_depth=2,
nbr_cases=10000):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | calculus_integrate | def calculus_integrate(alphabet_size=26,
min_depth=0,
max_depth=2,
nbr_cases=10000):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/algorithmic_math.py | ExprNode.is_in | def is_in(self, expr):
"""Returns True if `expr` is a subtree."""
if expr == self:
return True
is_in_left = is_in_expr(self.left, expr)
is_in_right = is_in_expr(self.right, expr)
return is_in_left or is_in_right | python | def is_in(self, expr):
"""Returns True if `expr` is a subtree."""
if expr == self:
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is_in_left = is_in_expr(self.left, expr)
is_in_right = is_in_expr(self.right, expr)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | preprocess_example_common | def preprocess_example_common(example, mode, hparams):
"""Preprocessing steps common to all models."""
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"""Preprocessing steps common to all models."""
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example["inputs"] = example["inputs"][:hparams.max_input_seq_length]
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | _copy_problem_hparams | def _copy_problem_hparams(p_hparams):
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p = p_hparams
# Duplicate input modality.
p.modality["targets"] = p.modality["inputs"]
# Duplicate input vocab size.
p.vocab_size["targets"] = p.vocab_size["inputs"]
# Duplicate input vocabulary.
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"""Use input modality, vocab, and space id for target."""
p = p_hparams
# Duplicate input modality.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | _reverse_problem_hparams | def _reverse_problem_hparams(p_hparams):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | _default_hparams | def _default_hparams():
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.tpu_batch_size_per_shard | def tpu_batch_size_per_shard(self, model_hparams):
"""Batch size in examples per TPU core.
Args:
model_hparams: model hyperparameters
Returns:
an integer
"""
if self.batch_size_means_tokens and not model_hparams.use_fixed_batch_size:
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model_hparams: model hyperparameters
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an integer
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.preprocess | def preprocess(self, dataset, mode, hparams, interleave=True):
"""Runtime preprocessing on the whole dataset.
Return a tf.data.Datset -- the preprocessed version of the given one.
By default this function calls preprocess_example.
Args:
dataset: the Dataset of already decoded but not yet preproc... | python | def preprocess(self, dataset, mode, hparams, interleave=True):
"""Runtime preprocessing on the whole dataset.
Return a tf.data.Datset -- the preprocessed version of the given one.
By default this function calls preprocess_example.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.filepattern | def filepattern(self, data_dir, mode, shard=None):
"""Get filepattern for data files for mode.
Matches mode to a suffix.
* DatasetSplit.TRAIN: train
* DatasetSplit.EVAL: dev
* DatasetSplit.TEST: test
* tf.estimator.ModeKeys.PREDICT: dev
Args:
data_dir: str, data directory.
mode... | python | def filepattern(self, data_dir, mode, shard=None):
"""Get filepattern for data files for mode.
Matches mode to a suffix.
* DatasetSplit.TRAIN: train
* DatasetSplit.EVAL: dev
* DatasetSplit.TEST: test
* tf.estimator.ModeKeys.PREDICT: dev
Args:
data_dir: str, data directory.
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.get_hparams | def get_hparams(self, model_hparams=None):
"""Returns problem_hparams."""
if self._hparams is not None:
return self._hparams
if model_hparams is None:
model_hparams = default_model_hparams()
if self._encoders is None:
data_dir = (model_hparams and hasattr(model_hparams, "data_dir") a... | python | def get_hparams(self, model_hparams=None):
"""Returns problem_hparams."""
if self._hparams is not None:
return self._hparams
if model_hparams is None:
model_hparams = default_model_hparams()
if self._encoders is None:
data_dir = (model_hparams and hasattr(model_hparams, "data_dir") a... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.maybe_reverse_features | def maybe_reverse_features(self, feature_map):
"""Reverse features between inputs and targets if the problem is '_rev'."""
if not self._was_reversed:
return
inputs = feature_map.pop("inputs", None)
targets = feature_map.pop("targets", None)
inputs_seg = feature_map.pop("inputs_segmentation", N... | python | def maybe_reverse_features(self, feature_map):
"""Reverse features between inputs and targets if the problem is '_rev'."""
if not self._was_reversed:
return
inputs = feature_map.pop("inputs", None)
targets = feature_map.pop("targets", None)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.dataset | def dataset(self,
mode,
data_dir=None,
num_threads=None,
output_buffer_size=None,
shuffle_files=None,
hparams=None,
preprocess=True,
dataset_split=None,
shard=None,
partition_id=0,... | python | def dataset(self,
mode,
data_dir=None,
num_threads=None,
output_buffer_size=None,
shuffle_files=None,
hparams=None,
preprocess=True,
dataset_split=None,
shard=None,
partition_id=0,... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.decode_example | def decode_example(self, serialized_example):
"""Return a dict of Tensors from a serialized tensorflow.Example."""
data_fields, data_items_to_decoders = self.example_reading_spec()
# Necessary to rejoin examples in the correct order with the Cloud ML Engine
# batch prediction API.
data_fields["batch... | python | def decode_example(self, serialized_example):
"""Return a dict of Tensors from a serialized tensorflow.Example."""
data_fields, data_items_to_decoders = self.example_reading_spec()
# Necessary to rejoin examples in the correct order with the Cloud ML Engine
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.feature_info | def feature_info(self):
"""Retrieve dict<feature name, FeatureInfo>.
Must first call Problem.get_hparams or Problem.dataset to have the problem's
internal hparams already constructed.
Returns:
dict<feature name, FeatureInfo>
"""
if self._feature_info is not None:
return self._featu... | python | def feature_info(self):
"""Retrieve dict<feature name, FeatureInfo>.
Must first call Problem.get_hparams or Problem.dataset to have the problem's
internal hparams already constructed.
Returns:
dict<feature name, FeatureInfo>
"""
if self._feature_info is not None:
return self._featu... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.make_estimator_input_fn | def make_estimator_input_fn(self,
mode,
hparams,
data_dir=None,
force_repeat=False,
prevent_repeat=False,
dataset_kwargs=None):
"""Retur... | python | def make_estimator_input_fn(self,
mode,
hparams,
data_dir=None,
force_repeat=False,
prevent_repeat=False,
dataset_kwargs=None):
"""Retur... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem._dataset_partition | def _dataset_partition(self, mode, config, params):
"""Which part of the training data to read.
If there are multiple parallel calls to input_fn (multiple TPU hosts),
then we want each one to read from a separate partition of the training
data.
Args:
mode: tf.estimator.ModeKeys
config:... | python | def _dataset_partition(self, mode, config, params):
"""Which part of the training data to read.
If there are multiple parallel calls to input_fn (multiple TPU hosts),
then we want each one to read from a separate partition of the training
data.
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mode: tf.estimator.ModeKeys
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.input_fn | def input_fn(self,
mode,
hparams,
data_dir=None,
params=None,
config=None,
force_repeat=False,
prevent_repeat=False,
dataset_kwargs=None):
"""Builds input pipeline for problem.
Args:
mo... | python | def input_fn(self,
mode,
hparams,
data_dir=None,
params=None,
config=None,
force_repeat=False,
prevent_repeat=False,
dataset_kwargs=None):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/problem.py | Problem.serving_input_fn | def serving_input_fn(self, hparams, decode_hparams=None, use_tpu=False):
"""Input fn for serving export, starting from serialized example."""
mode = tf.estimator.ModeKeys.PREDICT
serialized_example = tf.placeholder(
dtype=tf.string, shape=[None], name="serialized_example")
dataset = tf.data.Data... | python | def serving_input_fn(self, hparams, decode_hparams=None, use_tpu=False):
"""Input fn for serving export, starting from serialized example."""
mode = tf.estimator.ModeKeys.PREDICT
serialized_example = tf.placeholder(
dtype=tf.string, shape=[None], name="serialized_example")
dataset = tf.data.Data... | [
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tensorflow/tensor2tensor | tensor2tensor/serving/export.py | _get_hparams_path | def _get_hparams_path():
"""Get hyper-parameters file path."""
hparams_path = None
if FLAGS.output_dir:
hparams_path = os.path.join(FLAGS.output_dir, "hparams.json")
else:
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"--hparams_set and --hparams... | python | def _get_hparams_path():
"""Get hyper-parameters file path."""
hparams_path = None
if FLAGS.output_dir:
hparams_path = os.path.join(FLAGS.output_dir, "hparams.json")
else:
tf.logging.warning(
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tensorflow/tensor2tensor | tensor2tensor/serving/export.py | export_module_spec_with_checkpoint | def export_module_spec_with_checkpoint(module_spec,
checkpoint_path,
export_path,
scope_prefix=""):
"""Exports given checkpoint as tfhub module with given spec."""
# The main requirement is that it ... | python | def export_module_spec_with_checkpoint(module_spec,
checkpoint_path,
export_path,
scope_prefix=""):
"""Exports given checkpoint as tfhub module with given spec."""
# The main requirement is that it ... | [
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tensorflow/tensor2tensor | tensor2tensor/serving/export.py | export_as_tfhub_module | def export_as_tfhub_module(model_name,
hparams,
decode_hparams,
problem,
checkpoint_path,
export_dir):
"""Exports the last checkpoint from the directory as tfhub module.
It creates... | python | def export_as_tfhub_module(model_name,
hparams,
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problem,
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tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | build_model | def build_model(hparams_set, model_name, data_dir, problem_name, beam_size=1):
"""Build the graph required to fetch the attention weights.
Args:
hparams_set: HParams set to build the model with.
model_name: Name of model.
data_dir: Path to directory containing training data.
problem_name: Name of p... | python | def build_model(hparams_set, model_name, data_dir, problem_name, beam_size=1):
"""Build the graph required to fetch the attention weights.
Args:
hparams_set: HParams set to build the model with.
model_name: Name of model.
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tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | get_att_mats | def get_att_mats(translate_model):
"""Get's the tensors representing the attentions from a build model.
The attentions are stored in a dict on the Transformer object while building
the graph.
Args:
translate_model: Transformer object to fetch the attention weights from.
Returns:
Tuple of attention ma... | python | def get_att_mats(translate_model):
"""Get's the tensors representing the attentions from a build model.
The attentions are stored in a dict on the Transformer object while building
the graph.
Args:
translate_model: Transformer object to fetch the attention weights from.
Returns:
Tuple of attention ma... | [
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tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.encode | def encode(self, input_str):
"""Input str to features dict, ready for inference."""
inputs = self.encoders["inputs"].encode(input_str) + [EOS_ID]
batch_inputs = np.reshape(inputs, [1, -1, 1, 1]) # Make it 3D.
return batch_inputs | python | def encode(self, input_str):
"""Input str to features dict, ready for inference."""
inputs = self.encoders["inputs"].encode(input_str) + [EOS_ID]
batch_inputs = np.reshape(inputs, [1, -1, 1, 1]) # Make it 3D.
return batch_inputs | [
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tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.decode | def decode(self, integers):
"""List of ints to str."""
integers = list(np.squeeze(integers))
return self.encoders["inputs"].decode(integers) | python | def decode(self, integers):
"""List of ints to str."""
integers = list(np.squeeze(integers))
return self.encoders["inputs"].decode(integers) | [
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tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.decode_list | def decode_list(self, integers):
"""List of ints to list of str."""
integers = list(np.squeeze(integers))
return self.encoders["inputs"].decode_list(integers) | python | def decode_list(self, integers):
"""List of ints to list of str."""
integers = list(np.squeeze(integers))
return self.encoders["inputs"].decode_list(integers) | [
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tensorflow/tensor2tensor | tensor2tensor/visualization/visualization.py | AttentionVisualizer.get_vis_data_from_string | def get_vis_data_from_string(self, sess, input_string):
"""Constructs the data needed for visualizing attentions.
Args:
sess: A tf.Session object.
input_string: The input sentence to be translated and visualized.
Returns:
Tuple of (
output_string: The translated sentence.
... | python | def get_vis_data_from_string(self, sess, input_string):
"""Constructs the data needed for visualizing attentions.
Args:
sess: A tf.Session object.
input_string: The input sentence to be translated and visualized.
Returns:
Tuple of (
output_string: The translated sentence.
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tensorflow/tensor2tensor | tensor2tensor/models/research/glow.py | glow_hparams | def glow_hparams():
"""Glow Hparams."""
hparams = common_hparams.basic_params1()
hparams.clip_grad_norm = None
hparams.weight_decay = 0.0
hparams.learning_rate_constant = 3e-4
hparams.batch_size = 32
# can be prev_level, prev_step or normal.
# see: glow_ops.merge_level_and_latent_dist
hparams.add_hpar... | python | def glow_hparams():
"""Glow Hparams."""
hparams = common_hparams.basic_params1()
hparams.clip_grad_norm = None
hparams.weight_decay = 0.0
hparams.learning_rate_constant = 3e-4
hparams.batch_size = 32
# can be prev_level, prev_step or normal.
# see: glow_ops.merge_level_and_latent_dist
hparams.add_hpar... | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_aux.py | shift_and_pad | def shift_and_pad(tensor, shift, axis=0):
"""Shifts and pads with zero along an axis.
Example:
shift_and_pad([1, 2, 3, 4], 2) --> [0, 0, 1, 2]
shift_and_pad([1, 2, 3, 4], -2) --> [3, 4, 0, 0]
Args:
tensor: Tensor; to be shifted and padded.
shift: int; number of positions to shift by.
axis: ... | python | def shift_and_pad(tensor, shift, axis=0):
"""Shifts and pads with zero along an axis.
Example:
shift_and_pad([1, 2, 3, 4], 2) --> [0, 0, 1, 2]
shift_and_pad([1, 2, 3, 4], -2) --> [3, 4, 0, 0]
Args:
tensor: Tensor; to be shifted and padded.
shift: int; number of positions to shift by.
axis: ... | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_aux.py | transformer_aux_base | def transformer_aux_base():
"""Set of hyperparameters."""
hparams = transformer.transformer_base()
hparams.shared_embedding_and_softmax_weights = False
hparams.add_hparam("shift_values", "1,2,3,4")
return hparams | python | def transformer_aux_base():
"""Set of hyperparameters."""
hparams = transformer.transformer_base()
hparams.shared_embedding_and_softmax_weights = False
hparams.add_hparam("shift_values", "1,2,3,4")
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/research/transformer_aux.py | transformer_aux_tiny | def transformer_aux_tiny():
"""Set of hyperparameters."""
hparams = transformer.transformer_tiny()
hparams.shared_embedding_and_softmax_weights = False
hparams.add_hparam("shift_values", "1,2")
return hparams | python | def transformer_aux_tiny():
"""Set of hyperparameters."""
hparams = transformer.transformer_tiny()
hparams.shared_embedding_and_softmax_weights = False
hparams.add_hparam("shift_values", "1,2")
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/video/base.py | pixels_from_softmax | def pixels_from_softmax(frame_logits, pure_sampling=False,
temperature=1.0, gumbel_noise_factor=0.2):
"""Given frame_logits from a per-pixel softmax, generate colors."""
# If we're purely sampling, just sample each pixel.
if pure_sampling or temperature == 0.0:
return common_layers.sam... | python | def pixels_from_softmax(frame_logits, pure_sampling=False,
temperature=1.0, gumbel_noise_factor=0.2):
"""Given frame_logits from a per-pixel softmax, generate colors."""
# If we're purely sampling, just sample each pixel.
if pure_sampling or temperature == 0.0:
return common_layers.sam... | [
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tensorflow/tensor2tensor | tensor2tensor/models/video/base.py | next_frame_base | def next_frame_base():
"""Common HParams for next_frame models."""
hparams = common_hparams.basic_params1()
# Loss cutoff.
hparams.add_hparam("video_modality_loss_cutoff", 0.01)
# Additional resizing the frames before feeding them to model.
hparams.add_hparam("preprocess_resize_frames", None)
# How many d... | python | def next_frame_base():
"""Common HParams for next_frame models."""
hparams = common_hparams.basic_params1()
# Loss cutoff.
hparams.add_hparam("video_modality_loss_cutoff", 0.01)
# Additional resizing the frames before feeding them to model.
hparams.add_hparam("preprocess_resize_frames", None)
# How many d... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/gym_utils.py | remove_time_limit_wrapper | def remove_time_limit_wrapper(env):
"""Removes top level TimeLimit Wrapper.
Removes TimeLimit Wrapper from top level if exists, throws error if any other
TimeLimit Wrapper is present in stack.
Args:
env: environment
Returns:
the env with removed time limit wrapper.
"""
if isinstance(env, gym.wr... | python | def remove_time_limit_wrapper(env):
"""Removes top level TimeLimit Wrapper.
Removes TimeLimit Wrapper from top level if exists, throws error if any other
TimeLimit Wrapper is present in stack.
Args:
env: environment
Returns:
the env with removed time limit wrapper.
"""
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tensorflow/tensor2tensor | tensor2tensor/rl/gym_utils.py | gym_env_wrapper | def gym_env_wrapper(env, rl_env_max_episode_steps, maxskip_env, rendered_env,
rendered_env_resize_to, sticky_actions):
"""Wraps a gym environment. see make_gym_env for details."""
# rl_env_max_episode_steps is None or int.
assert ((not rl_env_max_episode_steps) or
isinstance(rl_env_m... | python | def gym_env_wrapper(env, rl_env_max_episode_steps, maxskip_env, rendered_env,
rendered_env_resize_to, sticky_actions):
"""Wraps a gym environment. see make_gym_env for details."""
# rl_env_max_episode_steps is None or int.
assert ((not rl_env_max_episode_steps) or
isinstance(rl_env_m... | [
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