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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | top_k_with_unique | def top_k_with_unique(inputs, k):
"""Finds the values and indices of the k largests entries.
Instead of doing sort like tf.nn.top_k, this function finds the max value
k times. The running time is proportional to k, which is be faster when k
is small. The current implementation supports only inputs of rank 2.
... | python | def top_k_with_unique(inputs, k):
"""Finds the values and indices of the k largests entries.
Instead of doing sort like tf.nn.top_k, this function finds the max value
k times. The running time is proportional to k, which is be faster when k
is small. The current implementation supports only inputs of rank 2.
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | compute_topk_scores_and_seq | def compute_topk_scores_and_seq(sequences,
scores,
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flags,
beam_size,
batch_size,
prefix="default",
... | python | def compute_topk_scores_and_seq(sequences,
scores,
scores_to_gather,
flags,
beam_size,
batch_size,
prefix="default",
... | [
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tensorflow/tensor2tensor | tensor2tensor/utils/beam_search.py | beam_search | def beam_search(symbols_to_logits_fn,
initial_ids,
beam_size,
decode_length,
vocab_size,
alpha,
states=None,
eos_id=EOS_ID,
stop_early=True,
use_tpu=False,
use_... | python | def beam_search(symbols_to_logits_fn,
initial_ids,
beam_size,
decode_length,
vocab_size,
alpha,
states=None,
eos_id=EOS_ID,
stop_early=True,
use_tpu=False,
use_... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | video_augmentation | def video_augmentation(features, hue=False, saturate=False, contrast=False):
"""Augments video with optional hue, saturation and constrast.
Args:
features: dict, with keys "inputs", "targets".
features["inputs"], 4-D Tensor, shape=(THWC)
features["targets"], 4-D Tensor, shape=(THWC)... | python | def video_augmentation(features, hue=False, saturate=False, contrast=False):
"""Augments video with optional hue, saturation and constrast.
Args:
features: dict, with keys "inputs", "targets".
features["inputs"], 4-D Tensor, shape=(THWC)
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | create_border | def create_border(video, color="blue", border_percent=2):
"""Creates a border around each frame to differentiate input and target.
Args:
video: 5-D NumPy array.
color: string, "blue", "red" or "green".
border_percent: Percentarge of the frame covered by the border.
Returns:
video: 5-D NumPy array... | python | def create_border(video, color="blue", border_percent=2):
"""Creates a border around each frame to differentiate input and target.
Args:
video: 5-D NumPy array.
color: string, "blue", "red" or "green".
border_percent: Percentarge of the frame covered by the border.
Returns:
video: 5-D NumPy array... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | convert_videos_to_summaries | def convert_videos_to_summaries(input_videos, output_videos, target_videos,
tag, decode_hparams,
display_ground_truth=False):
"""Converts input, output and target videos into video summaries.
Args:
input_videos: 5-D NumPy array, (NTHWC) conditioni... | python | def convert_videos_to_summaries(input_videos, output_videos, target_videos,
tag, decode_hparams,
display_ground_truth=False):
"""Converts input, output and target videos into video summaries.
Args:
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | display_video_hooks | def display_video_hooks(hook_args):
"""Hooks to display videos at decode time."""
predictions = hook_args.predictions
max_outputs = hook_args.decode_hparams.max_display_outputs
max_decodes = hook_args.decode_hparams.max_display_decodes
with tf.Graph().as_default():
_, best_decodes = video_metrics.compute... | python | def display_video_hooks(hook_args):
"""Hooks to display videos at decode time."""
predictions = hook_args.predictions
max_outputs = hook_args.decode_hparams.max_display_outputs
max_decodes = hook_args.decode_hparams.max_display_decodes
with tf.Graph().as_default():
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | summarize_video_metrics | def summarize_video_metrics(hook_args):
"""Computes video metrics summaries using the decoder output."""
problem_name = hook_args.problem.name
current_problem = hook_args.problem
hparams = hook_args.hparams
output_dirs = hook_args.output_dirs
predictions = hook_args.predictions
frame_shape = [
curre... | python | def summarize_video_metrics(hook_args):
"""Computes video metrics summaries using the decoder output."""
problem_name = hook_args.problem.name
current_problem = hook_args.problem
hparams = hook_args.hparams
output_dirs = hook_args.output_dirs
predictions = hook_args.predictions
frame_shape = [
curre... | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | debug_video_writer_factory | def debug_video_writer_factory(output_dir):
"""Creates a VideoWriter for debug videos."""
if FLAGS.disable_ffmpeg:
return common_video.IndividualFrameWriter(output_dir)
else:
output_path = os.path.join(output_dir, "video.avi")
return common_video.WholeVideoWriter(
fps=10, output_path=output_pa... | python | def debug_video_writer_factory(output_dir):
"""Creates a VideoWriter for debug videos."""
if FLAGS.disable_ffmpeg:
return common_video.IndividualFrameWriter(output_dir)
else:
output_path = os.path.join(output_dir, "video.avi")
return common_video.WholeVideoWriter(
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | VideoProblem.preprocess_example | def preprocess_example(self, example, mode, hparams):
"""Runtime preprocessing, e.g., resize example["frame"]."""
if getattr(hparams, "preprocess_resize_frames", None) is not None:
example["frame"] = tf.image.resize_images(
example["frame"], hparams.preprocess_resize_frames,
tf.image.R... | python | def preprocess_example(self, example, mode, hparams):
"""Runtime preprocessing, e.g., resize example["frame"]."""
if getattr(hparams, "preprocess_resize_frames", None) is not None:
example["frame"] = tf.image.resize_images(
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | VideoProblem.serving_input_fn | def serving_input_fn(self, hparams):
"""For serving/predict, assume that only video frames are provided."""
video_input_frames = tf.placeholder(
dtype=tf.float32,
shape=[
None, hparams.video_num_input_frames, self.frame_width,
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... | python | def serving_input_fn(self, hparams):
"""For serving/predict, assume that only video frames are provided."""
video_input_frames = tf.placeholder(
dtype=tf.float32,
shape=[
None, hparams.video_num_input_frames, self.frame_width,
self.frame_height, self.num_channels
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | VideoProblem.generate_encoded_samples | def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
"""Generate samples of the encoded frames with possible extra data.
By default this function just encodes the numpy array returned as "frame"
from `self.generate_samples` into a PNG image. Override this function to
get other encoding... | python | def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
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tensorflow/tensor2tensor | tensor2tensor/data_generators/video_utils.py | VideoProblem.generate_data | def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""The function generating the data."""
filepath_fns = {
problem.DatasetSplit.TRAIN: self.training_filepaths,
problem.DatasetSplit.EVAL: self.dev_filepaths,
problem.DatasetSplit.TEST: self.test_filepaths,
}
# We set shuffle... | python | def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""The function generating the data."""
filepath_fns = {
problem.DatasetSplit.TRAIN: self.training_filepaths,
problem.DatasetSplit.EVAL: self.dev_filepaths,
problem.DatasetSplit.TEST: self.test_filepaths,
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | add_scope | def add_scope(scope=None, scope_fn=None):
"""Return a decorator which add a TF name/variable scope to a function.
Note that the function returned by the decorator accept an additional 'name'
parameter, which can overwrite the name scope given when the function is
created.
Args:
scope (str): name of the ... | python | def add_scope(scope=None, scope_fn=None):
"""Return a decorator which add a TF name/variable scope to a function.
Note that the function returned by the decorator accept an additional 'name'
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | _add_variable_proxy_methods | def _add_variable_proxy_methods(var, proxy_tensor):
"""Proxy methods of underlying variable.
This enables our custom getters to still work with, e.g., batch norm.
Args:
var: Variable to proxy
proxy_tensor: Tensor that is identity of var
"""
proxy_tensor.read_value = lambda: tf.identity(proxy_tensor)... | python | def _add_variable_proxy_methods(var, proxy_tensor):
"""Proxy methods of underlying variable.
This enables our custom getters to still work with, e.g., batch norm.
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var: Variable to proxy
proxy_tensor: Tensor that is identity of var
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | _rowwise_unsorted_segment_sum | def _rowwise_unsorted_segment_sum(values, indices, n):
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Args:
values: a `Tensor` with shape `[batch_size, k]`.
indices: an integer `Tensor` with shape `[batch_size, k]`.
n: an integer.
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"""UnsortedSegmentSum on each row.
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values: a `Tensor` with shape `[batch_size, k]`.
indices: an integer `Tensor` with shape `[batch_size, k]`.
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | _prob_in_top_k | def _prob_in_top_k(
clean_values, noisy_values, noise_stddev, noisy_top_values, k):
"""Helper function to NoisyTopKGating.
Computes the probability that value is in top k, given different random noise.
This gives us a way of backpropagating from a loss that balances the number
of times each expert is in t... | python | def _prob_in_top_k(
clean_values, noisy_values, noise_stddev, noisy_top_values, k):
"""Helper function to NoisyTopKGating.
Computes the probability that value is in top k, given different random noise.
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | cv_squared | def cv_squared(x):
"""The squared coefficient of variation of a sample.
Useful as a loss to encourage a positive distribution to be more uniform.
Epsilons added for numerical stability.
Returns 0 for an empty Tensor.
Args:
x: a `Tensor`.
Returns:
a `Scalar`.
"""
epsilon = 1e-10
float_size =... | python | def cv_squared(x):
"""The squared coefficient of variation of a sample.
Useful as a loss to encourage a positive distribution to be more uniform.
Epsilons added for numerical stability.
Returns 0 for an empty Tensor.
Args:
x: a `Tensor`.
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | update_hparams_for_vq_gating | def update_hparams_for_vq_gating(hparams):
"""VQ Gating hparams."""
hparams.add_hparam("z_size", 4)
hparams.add_hparam("noise_dev", 0.5)
# Bottleneck kinds supported: dense, vae, dvq.
hparams.add_hparam("bottleneck_kind", "dvq")
hparams.add_hparam("num_blocks", 1)
hparams.add_hparam("num_residuals", 1)
... | python | def update_hparams_for_vq_gating(hparams):
"""VQ Gating hparams."""
hparams.add_hparam("z_size", 4)
hparams.add_hparam("noise_dev", 0.5)
# Bottleneck kinds supported: dense, vae, dvq.
hparams.add_hparam("bottleneck_kind", "dvq")
hparams.add_hparam("num_blocks", 1)
hparams.add_hparam("num_residuals", 1)
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | _my_top_k | def _my_top_k(x, k):
"""GPU-compatible version of top-k that works for very small constant k.
Calls argmax repeatedly.
tf.nn.top_k is implemented for GPU, but the gradient, sparse_to_dense,
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"""GPU-compatible version of top-k that works for very small constant k.
Calls argmax repeatedly.
tf.nn.top_k is implemented for GPU, but the gradient, sparse_to_dense,
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | vq_gating | def vq_gating(x,
num_experts,
k,
bneck,
hparams=None,
name="vq_gating"):
"""VQ gating.
Args:
x: input Tensor with shape [batch_size, input_size]
num_experts: an integer
k: an integer - number of experts per example
bneck: a bottl... | python | def vq_gating(x,
num_experts,
k,
bneck,
hparams=None,
name="vq_gating"):
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Args:
x: input Tensor with shape [batch_size, input_size]
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | noisy_top_k_gating | def noisy_top_k_gating(x,
num_experts,
train,
k=2,
initializer=tf.zeros_initializer(),
noisy_gating=True,
noise_epsilon=1e-2,
name=None):
"""Noisy top-k gati... | python | def noisy_top_k_gating(x,
num_experts,
train,
k=2,
initializer=tf.zeros_initializer(),
noisy_gating=True,
noise_epsilon=1e-2,
name=None):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | map_ids | def map_ids(x, indices, map_fn):
"""Apply a function to each coordinate ids of a multidimensional tensor.
This allows to process each sequence of a batch independently. This is
similar to tf.map_fn but with tensor where the batch dim has been flatten.
Warning: The indices ids have to be contiguous and ordered... | python | def map_ids(x, indices, map_fn):
"""Apply a function to each coordinate ids of a multidimensional tensor.
This allows to process each sequence of a batch independently. This is
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | ffn_expert_fn | def ffn_expert_fn(input_size,
hidden_sizes,
output_size,
hidden_activation=tf.nn.relu):
"""Returns a function that creates a feed-forward network.
Use this function to create the expert_fn argument to distributed_moe.
Args:
input_size: an integer
hid... | python | def ffn_expert_fn(input_size,
hidden_sizes,
output_size,
hidden_activation=tf.nn.relu):
"""Returns a function that creates a feed-forward network.
Use this function to create the expert_fn argument to distributed_moe.
Args:
input_size: an integer
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | flatten_all_but_last | def flatten_all_but_last(a):
"""Flatten all dimensions of a except the last."""
ret = tf.reshape(a, [-1, tf.shape(a)[-1]])
if not tf.executing_eagerly():
ret.set_shape([None] + a.get_shape().as_list()[-1:])
return ret | python | def flatten_all_but_last(a):
"""Flatten all dimensions of a except the last."""
ret = tf.reshape(a, [-1, tf.shape(a)[-1]])
if not tf.executing_eagerly():
ret.set_shape([None] + a.get_shape().as_list()[-1:])
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | local_moe | def local_moe(x,
train,
expert_fn,
num_experts,
k=1,
loss_coef=1e-2,
hparams=None,
pass_x=True,
pass_gates=False,
additional_dispatch_params=None,
name=None):
"""Call a local mix... | python | def local_moe(x,
train,
expert_fn,
num_experts,
k=1,
loss_coef=1e-2,
hparams=None,
pass_x=True,
pass_gates=False,
additional_dispatch_params=None,
name=None):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | local_moe_tpu | def local_moe_tpu(inputs,
hidden_size,
output_size,
num_experts,
loss_coef=1e-3,
overhead=1.0):
"""Local mixture of experts that works well on TPU.
See https://arxiv.org/abs/1701.06538
There are num_experts expert networks... | python | def local_moe_tpu(inputs,
hidden_size,
output_size,
num_experts,
loss_coef=1e-3,
overhead=1.0):
"""Local mixture of experts that works well on TPU.
See https://arxiv.org/abs/1701.06538
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | reduce_by_device | def reduce_by_device(parallelism, data, reduce_fn):
"""Reduces data per device.
This can be useful, for example, if we want to all-reduce n tensors on k<n
devices (like during eval when we have only one device). We call
reduce_by_device() to first sum the tensors per device, then call our usual
all-reduce o... | python | def reduce_by_device(parallelism, data, reduce_fn):
"""Reduces data per device.
This can be useful, for example, if we want to all-reduce n tensors on k<n
devices (like during eval when we have only one device). We call
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | expand_by_device | def expand_by_device(original_parallelism, device_parallelism, data):
"""Opposite of reduce_by_device().
Args:
original_parallelism: a expert_utils.Parallelism object.
device_parallelism: a expert_utils.Parallelism object.
data: a list of tensors with length device_parallelism.n
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"""Opposite of reduce_by_device().
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original_parallelism: a expert_utils.Parallelism object.
device_parallelism: a expert_utils.Parallelism object.
data: a list of tensors with length device_parallelism.n
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | all_reduce_ring | def all_reduce_ring(x, parallelism, maybe_reduce=True, use_bfloat16=True):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | Parallelism._maybe_repeat | def _maybe_repeat(self, x):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | PadRemover.remove | def remove(self, x):
"""Remove padding from the given tensor.
Args:
x (tf.Tensor): of shape [dim_origin,...]
Returns:
a tensor of shape [dim_compressed,...] with dim_compressed <= dim_origin
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | PadRemover.restore | def restore(self, x):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | SparseDispatcher.dispatch | def dispatch(self, inp):
"""Create one input Tensor for each expert.
The `Tensor` for a expert `i` contains the slices of `inp` corresponding
to the batch elements `b` where `gates[b, i] > 0`.
Args:
inp: a `Tensor` of shape "[batch_size, <extra_input_dims>]`
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a list of `num_exp... | python | def dispatch(self, inp):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | SparseDispatcher.combine | def combine(self, expert_out, multiply_by_gates=True):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | SparseDispatcher.expert_to_gates | def expert_to_gates(self):
"""Gate values corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s with type `tf.float32`
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"""
return tf.split(
self._nonzero_gates, self._part_sizes_te... | python | def expert_to_gates(self):
"""Gate values corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s with type `tf.float32`
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | SparseDispatcher.expert_to_batch_indices | def expert_to_batch_indices(self):
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Returns:
a list of `num_experts` one-dimensional `Tensor`s with type `tf.int64`
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"""Batch indices corresponding to the examples in the per-expert `Tensor`s.
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | DistributedSparseDispatcher.dispatch | def dispatch(self, inp):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | DistributedSparseDispatcher.combine | def combine(self, expert_out, multiply_by_gates=True):
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expert_out: a list of `num_experts` `Tensor`s, each with shape
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | DistributedSparseDispatcher.expert_to_gates | def expert_to_gates(self):
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | TruncatingDispatcher.dispatch | def dispatch(self, inp):
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Args:
inp: a `Tensor` of shape "[batch, length, depth]`
Returns:
a tensor with shape [batch, num_experts, expert_capacity, depth]
"""
inp = tf.reshape(inp, [self._batch * self._length, -1])
# [batch, num_experts, expert_cap... | python | def dispatch(self, inp):
"""Send the inputs to the experts.
Args:
inp: a `Tensor` of shape "[batch, length, depth]`
Returns:
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tensorflow/tensor2tensor | tensor2tensor/utils/expert_utils.py | TruncatingDispatcher.combine | def combine(self, x):
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x: a Tensor with shape [batch, num_experts, expert_capacity, depth]
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When one example goes to multiple experts, the outputs are summed.
Args:
x: a Tensor with shape [batch, num_experts, expert_capacity, depth]
Returns:
a `Tensor` with shape `[batch, length, depth] | [
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | make_env | def make_env(env_type, real_env, sim_env_kwargs):
"""Factory function for envs."""
return {
"real": lambda: real_env.new_like( # pylint: disable=g-long-lambda
batch_size=sim_env_kwargs["batch_size"],
store_rollouts=False,
),
"simulated": lambda: rl_utils.SimulatedBatchGymEnvWi... | python | def make_env(env_type, real_env, sim_env_kwargs):
"""Factory function for envs."""
return {
"real": lambda: real_env.new_like( # pylint: disable=g-long-lambda
batch_size=sim_env_kwargs["batch_size"],
store_rollouts=False,
),
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | make_agent | def make_agent(
agent_type, env, policy_hparams, policy_dir, sampling_temp,
sim_env_kwargs_fn=None, frame_stack_size=None, rollout_agent_type=None,
batch_size=None, inner_batch_size=None, env_type=None, **planner_kwargs
):
"""Factory function for Agents."""
if batch_size is None:
batch_size = env.ba... | python | def make_agent(
agent_type, env, policy_hparams, policy_dir, sampling_temp,
sim_env_kwargs_fn=None, frame_stack_size=None, rollout_agent_type=None,
batch_size=None, inner_batch_size=None, env_type=None, **planner_kwargs
):
"""Factory function for Agents."""
if batch_size is None:
batch_size = env.ba... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | collect_frames_for_random_starts | def collect_frames_for_random_starts(
storage_env, stacked_env, agent, frame_stack_size, random_starts_step_limit,
log_every_steps=None
):
"""Collects frames from real env for random starts of simulated env."""
del frame_stack_size
storage_env.start_new_epoch(0)
tf.logging.info(
"Collecting %d fra... | python | def collect_frames_for_random_starts(
storage_env, stacked_env, agent, frame_stack_size, random_starts_step_limit,
log_every_steps=None
):
"""Collects frames from real env for random starts of simulated env."""
del frame_stack_size
storage_env.start_new_epoch(0)
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | make_agent_from_hparams | def make_agent_from_hparams(
agent_type, base_env, stacked_env, loop_hparams, policy_hparams,
planner_hparams, model_dir, policy_dir, sampling_temp, video_writers=()
):
"""Creates an Agent from hparams."""
def sim_env_kwargs_fn():
return rl.make_simulated_env_kwargs(
base_env, loop_hparams, batc... | python | def make_agent_from_hparams(
agent_type, base_env, stacked_env, loop_hparams, policy_hparams,
planner_hparams, model_dir, policy_dir, sampling_temp, video_writers=()
):
"""Creates an Agent from hparams."""
def sim_env_kwargs_fn():
return rl.make_simulated_env_kwargs(
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | make_eval_fn_with_agent | def make_eval_fn_with_agent(
agent_type, eval_mode, planner_hparams, model_dir, log_every_steps=None,
video_writers=(), random_starts_step_limit=None
):
"""Returns an out-of-graph eval_fn using the Agent API."""
def eval_fn(env, loop_hparams, policy_hparams, policy_dir, sampling_temp):
"""Eval function.... | python | def make_eval_fn_with_agent(
agent_type, eval_mode, planner_hparams, model_dir, log_every_steps=None,
video_writers=(), random_starts_step_limit=None
):
"""Returns an out-of-graph eval_fn using the Agent API."""
def eval_fn(env, loop_hparams, policy_hparams, policy_dir, sampling_temp):
"""Eval function.... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | evaluate_world_model | def evaluate_world_model(
agent_type, loop_hparams, planner_hparams, model_dir, policy_dir,
random_starts_step_limit, debug_video_path, log_every_steps
):
"""Evaluates the world model."""
if debug_video_path:
debug_video_path = os.path.join(debug_video_path, "0.avi")
storage_env = rl_utils.setup_env(... | python | def evaluate_world_model(
agent_type, loop_hparams, planner_hparams, model_dir, policy_dir,
random_starts_step_limit, debug_video_path, log_every_steps
):
"""Evaluates the world model."""
if debug_video_path:
debug_video_path = os.path.join(debug_video_path, "0.avi")
storage_env = rl_utils.setup_env(... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | evaluate | def evaluate(
loop_hparams, planner_hparams, policy_dir, model_dir, eval_metrics_dir,
agent_type, eval_mode, eval_with_learner, log_every_steps, debug_video_path,
num_debug_videos=1, random_starts_step_limit=None,
report_fn=None, report_metric=None
):
"""Evaluate."""
if eval_with_learner:
assert... | python | def evaluate(
loop_hparams, planner_hparams, policy_dir, model_dir, eval_metrics_dir,
agent_type, eval_mode, eval_with_learner, log_every_steps, debug_video_path,
num_debug_videos=1, random_starts_step_limit=None,
report_fn=None, report_metric=None
):
"""Evaluate."""
if eval_with_learner:
assert... | [
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tensorflow/tensor2tensor | tensor2tensor/rl/evaluator.py | get_game_for_worker | def get_game_for_worker(map_name, directory_id):
"""Get game for the given worker (directory) id."""
if map_name == "v100unfriendly":
games = ["chopper_command", "boxing", "asterix", "seaquest"]
worker_per_game = 5
elif map_name == "human_nice":
games = gym_env.ATARI_GAMES_WITH_HUMAN_SCORE_NICE
wo... | python | def get_game_for_worker(map_name, directory_id):
"""Get game for the given worker (directory) id."""
if map_name == "v100unfriendly":
games = ["chopper_command", "boxing", "asterix", "seaquest"]
worker_per_game = 5
elif map_name == "human_nice":
games = gym_env.ATARI_GAMES_WITH_HUMAN_SCORE_NICE
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tensorflow/tensor2tensor | tensor2tensor/envs/tic_tac_toe_env.py | get_open_spaces | def get_open_spaces(board):
"""Given a representation of the board, returns a list of open spaces."""
open_spaces = []
for i in range(3):
for j in range(3):
if board[i][j] == 0:
open_spaces.append(encode_pos(i, j))
return open_spaces | python | def get_open_spaces(board):
"""Given a representation of the board, returns a list of open spaces."""
open_spaces = []
for i in range(3):
for j in range(3):
if board[i][j] == 0:
open_spaces.append(encode_pos(i, j))
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tensorflow/tensor2tensor | tensor2tensor/envs/tic_tac_toe_env.py | get_reward_and_done | def get_reward_and_done(board):
"""Given a representation of the board, returns reward and done."""
# Returns (reward, done) where:
# reward: -1 means lost, +1 means win, 0 means draw or continuing.
# done: True if the game is over, i.e. someone won or it is a draw.
# Sum all rows ...
all_sums = [np.sum(bo... | python | def get_reward_and_done(board):
"""Given a representation of the board, returns reward and done."""
# Returns (reward, done) where:
# reward: -1 means lost, +1 means win, 0 means draw or continuing.
# done: True if the game is over, i.e. someone won or it is a draw.
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | decode_hparams | def decode_hparams(overrides=""):
"""Hyperparameters for decoding."""
hp = hparam.HParams(
save_images=False,
log_results=True,
extra_length=100,
min_length_ratio=0.0,
batch_size=0,
beam_size=4,
alpha=0.6,
eos_penalty=0.0,
block_size=0,
guess_and_check_top... | python | def decode_hparams(overrides=""):
"""Hyperparameters for decoding."""
hp = hparam.HParams(
save_images=False,
log_results=True,
extra_length=100,
min_length_ratio=0.0,
batch_size=0,
beam_size=4,
alpha=0.6,
eos_penalty=0.0,
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | log_decode_results | def log_decode_results(inputs,
outputs,
problem_name,
prediction_idx,
inputs_vocab,
targets_vocab,
targets=None,
save_images=False,
outp... | python | def log_decode_results(inputs,
outputs,
problem_name,
prediction_idx,
inputs_vocab,
targets_vocab,
targets=None,
save_images=False,
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | decode_from_dataset | def decode_from_dataset(estimator,
problem_name,
hparams,
decode_hp,
decode_to_file=None,
dataset_split=None,
checkpoint_path=None):
"""Perform decoding from dataset."""
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hparams,
decode_hp,
decode_to_file=None,
dataset_split=None,
checkpoint_path=None):
"""Perform decoding from dataset."""
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | decode_once | def decode_once(estimator,
problem_name,
hparams,
infer_input_fn,
decode_hp,
decode_to_file,
output_dir,
log_results=True,
checkpoint_path=None):
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Args:
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hparams,
infer_input_fn,
decode_hp,
decode_to_file,
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log_results=True,
checkpoint_path=None):
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | decode_from_file | def decode_from_file(estimator,
filename,
hparams,
decode_hp,
decode_to_file=None,
checkpoint_path=None):
"""Compute predictions on entries in filename and write them out."""
if not decode_hp.batch_size:
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filename,
hparams,
decode_hp,
decode_to_file=None,
checkpoint_path=None):
"""Compute predictions on entries in filename and write them out."""
if not decode_hp.batch_size:
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | _decode_filename | def _decode_filename(base_filename, problem_name, decode_hp):
"""Generates decode filename.
Args:
base_filename: A string, base of the decode filename.
problem_name: A string, name of the problem.
decode_hp: HParams for decoding.
Returns:
A string, produced decode filename.
"""
if decode_hp.... | python | def _decode_filename(base_filename, problem_name, decode_hp):
"""Generates decode filename.
Args:
base_filename: A string, base of the decode filename.
problem_name: A string, name of the problem.
decode_hp: HParams for decoding.
Returns:
A string, produced decode filename.
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | make_input_fn_from_generator | def make_input_fn_from_generator(gen):
"""Use py_func to yield elements from the given generator."""
first_ex = six.next(gen)
flattened = tf.contrib.framework.nest.flatten(first_ex)
types = [t.dtype for t in flattened]
shapes = [[None] * len(t.shape) for t in flattened]
first_ex_list = [first_ex]
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"""Use py_func to yield elements from the given generator."""
first_ex = six.next(gen)
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | decode_interactively | def decode_interactively(estimator, hparams, decode_hp, checkpoint_path=None):
"""Interactive decoding."""
is_image = "image" in hparams.problem.name
is_text2class = isinstance(hparams.problem,
text_problems.Text2ClassProblem)
skip_eos_postprocess = (
is_image or is_text2clas... | python | def decode_interactively(estimator, hparams, decode_hp, checkpoint_path=None):
"""Interactive decoding."""
is_image = "image" in hparams.problem.name
is_text2class = isinstance(hparams.problem,
text_problems.Text2ClassProblem)
skip_eos_postprocess = (
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | _decode_batch_input_fn | def _decode_batch_input_fn(num_decode_batches, sorted_inputs, vocabulary,
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task_id=-1, has_input=True):
"""Generator to produce batches of inputs."""
tf.logging.info(" batch %d" % num_decode_batches)
for b in range(num_decode_batches... | python | def _decode_batch_input_fn(num_decode_batches, sorted_inputs, vocabulary,
batch_size, max_input_size,
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"""Generator to produce batches of inputs."""
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | _interactive_input_fn | def _interactive_input_fn(hparams, decode_hp):
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | save_video | def save_video(video, save_path_template):
"""Save frames of the videos into files."""
try:
from PIL import Image # pylint: disable=g-import-not-at-top
except ImportError as e:
tf.logging.warning(
"Showing and saving an image requires PIL library to be "
"installed: %s", e)
raise NotI... | python | def save_video(video, save_path_template):
"""Save frames of the videos into files."""
try:
from PIL import Image # pylint: disable=g-import-not-at-top
except ImportError as e:
tf.logging.warning(
"Showing and saving an image requires PIL library to be "
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | show_and_save_image | def show_and_save_image(img, save_path):
"""Shows an image using matplotlib and saves it."""
try:
import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top
except ImportError as e:
tf.logging.warning(
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"""Shows an image using matplotlib and saves it."""
try:
import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | _get_language_modeling_inputs | def _get_language_modeling_inputs(filename,
delimiter="\n",
repeat=1,
append_space_to_final_punctionation=True):
"""Read a file of partial texts to continue.
The purpose of append_space_to_final_punctionation is t... | python | def _get_language_modeling_inputs(filename,
delimiter="\n",
repeat=1,
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | _get_sorted_inputs | def _get_sorted_inputs(filename, delimiter="\n"):
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | _save_until_eos | def _save_until_eos(ids, skip=False):
"""Strips everything after the first <EOS> token, which is normally 1."""
ids = ids.flatten()
if skip:
return ids
try:
index = list(ids).index(text_encoder.EOS_ID)
return ids[0:index]
except ValueError:
# No EOS_ID: return the array as-is.
return ids | python | def _save_until_eos(ids, skip=False):
"""Strips everything after the first <EOS> token, which is normally 1."""
ids = ids.flatten()
if skip:
return ids
try:
index = list(ids).index(text_encoder.EOS_ID)
return ids[0:index]
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | _interactive_input_tensor_to_features_dict | def _interactive_input_tensor_to_features_dict(feature_map, hparams):
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Args:
feature_map: dict with inputs.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
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feature_map: dict with inputs.
hparams: model hyperparameters
Returns:
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feature_map: dict with inputs.
hparams: model hyperparameters
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feature_map: dict with inputs.
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tensorflow/tensor2tensor | tensor2tensor/utils/decoding.py | run_postdecode_hooks | def run_postdecode_hooks(decode_hook_args, dataset_split):
"""Run hooks after decodes have run."""
hooks = decode_hook_args.problem.decode_hooks
if not hooks:
return
global_step = latest_checkpoint_step(decode_hook_args.estimator.model_dir)
if global_step is None:
tf.logging.info(
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"""Run hooks after decodes have run."""
hooks = decode_hook_args.problem.decode_hooks
if not hooks:
return
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tensorflow/tensor2tensor | tensor2tensor/data_generators/style_transfer.py | StyleTransferProblemShakespeare.dataset_splits | def dataset_splits(self):
"""Splits of data to produce and number of output shards for each."""
return [{
"split": problem.DatasetSplit.TRAIN,
"shards": _TRAIN_SHARDS,
}, {
"split": problem.DatasetSplit.EVAL,
"shards": _DEV_SHARDS,
}] | python | def dataset_splits(self):
"""Splits of data to produce and number of output shards for each."""
return [{
"split": problem.DatasetSplit.TRAIN,
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | local_attention1d_spatial_decoder | def local_attention1d_spatial_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local1D spatial layers."""
batch_dim, length_dim, model_dim = x.shape.dims
blocks_w_dim = mtf.Dimension("blocksw", hparams.block_length)
num_w_blocks_dim... | python | def local_attention1d_spatial_decoder(x, kv_dim, heads_dim,
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"""Image Transformer decoder with local1D spatial layers."""
batch_dim, length_dim, model_dim = x.shape.dims
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | local_attention2d_spatial_decoder | def local_attention2d_spatial_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local2D spatial layers."""
batch_dim, length_dim, model_dim = x.shape.dims
blocks_h_dim = mtf.Dimension("blocksh", hparams.block_height)
blocks_w_dim = m... | python | def local_attention2d_spatial_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local2D spatial layers."""
batch_dim, length_dim, model_dim = x.shape.dims
blocks_h_dim = mtf.Dimension("blocksh", hparams.block_height)
blocks_w_dim = m... | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | local_attention1d_masked_decoder | def local_attention1d_masked_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local1D masked layers."""
print(x)
_, length_dim, model_dim = x.shape.dims
for layer in range(hparams.num_decoder_layers):
layer_name = "decoder_layer_... | python | def local_attention1d_masked_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local1D masked layers."""
print(x)
_, length_dim, model_dim = x.shape.dims
for layer in range(hparams.num_decoder_layers):
layer_name = "decoder_layer_... | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base | def mtf_image_transformer_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.no_data_parallelism = True
hparams.use_fixed_batch_size = True
hparams.batch_size = 1
hparams.max_length = 3072
hparams.hidden_size = 256
hparams.label_smoothing = 0.0
# 8-way model-paralle... | python | def mtf_image_transformer_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.no_data_parallelism = True
hparams.use_fixed_batch_size = True
hparams.batch_size = 1
hparams.max_length = 3072
hparams.hidden_size = 256
hparams.label_smoothing = 0.0
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_tiny | def mtf_image_transformer_tiny():
"""Catch bugs locally..."""
hparams = mtf_image_transformer_base()
hparams.hidden_size = 128
hparams.d_ff = 256
hparams.batch_size = 4
hparams.num_encoder_layers = 1
hparams.num_decoder_layers = 4
hparams.num_heads = 4
hparams.attention_key_size = 128
hparams.attent... | python | def mtf_image_transformer_tiny():
"""Catch bugs locally..."""
hparams = mtf_image_transformer_base()
hparams.hidden_size = 128
hparams.d_ff = 256
hparams.batch_size = 4
hparams.num_encoder_layers = 1
hparams.num_decoder_layers = 4
hparams.num_heads = 4
hparams.attention_key_size = 128
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_single | def mtf_image_transformer_single():
"""Small single parameters."""
hparams = mtf_image_transformer_tiny()
hparams.mesh_shape = ""
hparams.layout = ""
hparams.hidden_size = 32
hparams.filter_size = 32
hparams.batch_size = 1
hparams.num_encoder_layers = 1
hparams.num_decoder_layers = 1
hparams.num_hea... | python | def mtf_image_transformer_single():
"""Small single parameters."""
hparams = mtf_image_transformer_tiny()
hparams.mesh_shape = ""
hparams.layout = ""
hparams.hidden_size = 32
hparams.filter_size = 32
hparams.batch_size = 1
hparams.num_encoder_layers = 1
hparams.num_decoder_layers = 1
hparams.num_hea... | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base_single | def mtf_image_transformer_base_single():
"""Small single parameters."""
hparams = mtf_image_transformer_base()
hparams.num_decoder_layers = 6
hparams.filter_size = 256
hparams.block_length = 128
hparams.mesh_shape = ""
hparams.layout = ""
return hparams | python | def mtf_image_transformer_base_single():
"""Small single parameters."""
hparams = mtf_image_transformer_base()
hparams.num_decoder_layers = 6
hparams.filter_size = 256
hparams.block_length = 128
hparams.mesh_shape = ""
hparams.layout = ""
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_tiny_spatial1d | def mtf_image_transformer_tiny_spatial1d():
"""Small single parameters."""
hparams = mtf_image_transformer_tiny()
hparams.num_decoder_layers = 6
hparams.filter_size = 128
hparams.block_height = 8
hparams.block_width = 8
hparams.attention_type = "local1d_spatial"
hparams.mesh_shape = ""
hparams.layout ... | python | def mtf_image_transformer_tiny_spatial1d():
"""Small single parameters."""
hparams = mtf_image_transformer_tiny()
hparams.num_decoder_layers = 6
hparams.filter_size = 128
hparams.block_height = 8
hparams.block_width = 8
hparams.attention_type = "local1d_spatial"
hparams.mesh_shape = ""
hparams.layout ... | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base_cifar | def mtf_image_transformer_base_cifar():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base()
hparams.mesh_shape = "batch:8"
hparams.layout = "batch:batch"
hparams.learning_rate_decay_steps = 13600 # one epoch
hparams.batch_size = 32
hparams.num_heads = 4
hparams.num_decoder_laye... | python | def mtf_image_transformer_base_cifar():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base()
hparams.mesh_shape = "batch:8"
hparams.layout = "batch:batch"
hparams.learning_rate_decay_steps = 13600 # one epoch
hparams.batch_size = 32
hparams.num_heads = 4
hparams.num_decoder_laye... | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_cifar_4x | def mtf_image_transformer_cifar_4x():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "batch:32"
hparams.layout = "batch:batch"
hparams.batch_size = 128
return hparams | python | def mtf_image_transformer_cifar_4x():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "batch:32"
hparams.layout = "batch:batch"
hparams.batch_size = 128
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_cifar_mp_4x | def mtf_image_transformer_cifar_mp_4x():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "model:4;batch:8"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 32
hparams.num_heads = 8
hparams.d_ff = 8192
return hparams | python | def mtf_image_transformer_cifar_mp_4x():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "model:4;batch:8"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 32
hparams.num_heads = 8
hparams.d_ff = 8192
return hparams | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base_imagenet | def mtf_image_transformer_base_imagenet():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "batch:32"
hparams.layout = "batch:batch"
hparams.batch_size = 128
hparams.d_ff = 2048
hparams.hidden_size = 512
hparams.num_decoder_layers = 12
hparams.le... | python | def mtf_image_transformer_base_imagenet():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "batch:32"
hparams.layout = "batch:batch"
hparams.batch_size = 128
hparams.d_ff = 2048
hparams.hidden_size = 512
hparams.num_decoder_layers = 12
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base_imagenet_mp | def mtf_image_transformer_base_imagenet_mp():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:4;batch:8"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 32
hparams.num_heads = 8
hparams.d_ff = 8192
hparams.l... | python | def mtf_image_transformer_base_imagenet_mp():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:4;batch:8"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 32
hparams.num_heads = 8
hparams.d_ff = 8192
hparams.l... | [
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base_imagenet_mp128 | def mtf_image_transformer_base_imagenet_mp128():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 8
hparams.img_len = 128
hparams.block_length = 128
... | python | def mtf_image_transformer_base_imagenet_mp128():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 8
hparams.img_len = 128
hparams.block_length = 128
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base_imagenet_mp_sp | def mtf_image_transformer_base_imagenet_mp_sp():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet_mp128()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;num_wblocks:model"
hparams.batch_size = 8
hparams.img_len = 128
hparams.block_len... | python | def mtf_image_transformer_base_imagenet_mp_sp():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet_mp128()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;num_wblocks:model"
hparams.batch_size = 8
hparams.img_len = 128
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tensorflow/tensor2tensor | tensor2tensor/models/mtf_image_transformer.py | mtf_image_transformer_base_imagenet_mp64 | def mtf_image_transformer_base_imagenet_mp64():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 8
hparams.img_len = 64
hparams.num_decoder_layers = 8
... | python | def mtf_image_transformer_base_imagenet_mp64():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 8
hparams.img_len = 64
hparams.num_decoder_layers = 8
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tensorflow/tensor2tensor | tensor2tensor/layers/reversible_layers.py | create_degrees | def create_degrees(input_dim,
hidden_dims,
input_order='left-to-right',
hidden_order='left-to-right'):
"""Returns a list of degree vectors, one for each input and hidden layer.
A unit with degree d can only receive input from units with degree < d. Output
... | python | def create_degrees(input_dim,
hidden_dims,
input_order='left-to-right',
hidden_order='left-to-right'):
"""Returns a list of degree vectors, one for each input and hidden layer.
A unit with degree d can only receive input from units with degree < d. Output
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tensorflow/tensor2tensor | tensor2tensor/layers/reversible_layers.py | create_masks | def create_masks(input_dim,
hidden_dims,
input_order='left-to-right',
hidden_order='left-to-right'):
"""Returns a list of binary mask matrices respecting autoregressive ordering.
Args:
input_dim: Number of inputs.
hidden_dims: list with the number of hidde... | python | def create_masks(input_dim,
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tensorflow/tensor2tensor | tensor2tensor/layers/reversible_layers.py | sinkhorn | def sinkhorn(inputs, n_iters=20):
"""Performs incomplete Sinkhorn normalization to inputs.
By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix
with positive entries can be turned into a doubly-stochastic matrix
(i.e. its rows and columns add up to one) via the succesive row and column
... | python | def sinkhorn(inputs, n_iters=20):
"""Performs incomplete Sinkhorn normalization to inputs.
By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix
with positive entries can be turned into a doubly-stochastic matrix
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tensorflow/tensor2tensor | tensor2tensor/layers/reversible_layers.py | TransformedRandomVariable | def TransformedRandomVariable(random_variable, # pylint: disable=invalid-name
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name=None,
sample_shape=(),
value=None):
"""Random variable for f(x), where x ~ p(x) and f is reversi... | python | def TransformedRandomVariable(random_variable, # pylint: disable=invalid-name
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tensorflow/tensor2tensor | tensor2tensor/layers/reversible_layers.py | ActNorm.log_det_jacobian | def log_det_jacobian(self, inputs):
"""Returns log det | dx / dy | = num_events * sum log | scale |."""
del inputs # unused
# Number of events is number of all elements excluding the batch and
# channel dimensions.
num_events = tf.reduce_prod(tf.shape(inputs)[1:-1])
log_det_jacobian = num_event... | python | def log_det_jacobian(self, inputs):
"""Returns log det | dx / dy | = num_events * sum log | scale |."""
del inputs # unused
# Number of events is number of all elements excluding the batch and
# channel dimensions.
num_events = tf.reduce_prod(tf.shape(inputs)[1:-1])
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tensorflow/tensor2tensor | tensor2tensor/layers/vq_discrete.py | DiscreteBottleneck.slice_hidden | def slice_hidden(self, x):
"""Slice encoder hidden state into block_dim.
Args:
x: Encoder hidden state of shape [-1, hidden_size].
Returns:
Sliced states of shape [-1, num_blocks, block_dim].
"""
x_sliced = tf.reshape(
x, shape=[-1, self.hparams.num_blocks, self.hparams.blo... | python | def slice_hidden(self, x):
"""Slice encoder hidden state into block_dim.
Args:
x: Encoder hidden state of shape [-1, hidden_size].
Returns:
Sliced states of shape [-1, num_blocks, block_dim].
"""
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tensorflow/tensor2tensor | tensor2tensor/layers/vq_discrete.py | DiscreteBottleneck.nearest_neighbor | def nearest_neighbor(self, x, means):
"""Find the nearest element in means to elements in x.
Args:
x: Batch of encoder continuous latent states sliced/projected into
shape [-1, num_blocks, block_dim].
means: Embedding means of shape.
Returns:
Tensor with nearest element in... | python | def nearest_neighbor(self, x, means):
"""Find the nearest element in means to elements in x.
Args:
x: Batch of encoder continuous latent states sliced/projected into
shape [-1, num_blocks, block_dim].
means: Embedding means of shape.
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tensorflow/tensor2tensor | tensor2tensor/layers/vq_discrete.py | DiscreteBottleneck.embedding_lookup | def embedding_lookup(self, x, means):
"""Compute nearest neighbors and loss for training the embeddings.
Args:
x: Batch of encoder continuous latent states sliced/projected into
shape
[-1, num_blocks, block_dim].
means: Embedding means.
Returns:
The nearest neighbor... | python | def embedding_lookup(self, x, means):
"""Compute nearest neighbors and loss for training the embeddings.
Args:
x: Batch of encoder continuous latent states sliced/projected into
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means: Embedding means.
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tensorflow/tensor2tensor | tensor2tensor/layers/vq_discrete.py | DiscreteBottleneck.int_to_bit | def int_to_bit(self, x_int, num_bits, base=2):
"""Turn x_int representing numbers into a bitwise (lower-endian) tensor.
Args:
x_int: Tensor containing integer to be converted into base
notation.
num_bits: Number of bits in the representation.
base: Base of the representation.
... | python | def int_to_bit(self, x_int, num_bits, base=2):
"""Turn x_int representing numbers into a bitwise (lower-endian) tensor.
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x_int: Tensor containing integer to be converted into base
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num_bits: Number of bits in the representation.
base: Base of the representation.
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tensorflow/tensor2tensor | tensor2tensor/layers/vq_discrete.py | DiscreteBottleneck.embed | def embed(self, x):
"""Embedding function that takes discrete latent and returns embedding.
Args:
x: Input to the discretization bottleneck.
Returns:
Continuous embedding to be passed on to the decoder.
Raises:
ValueError: For unknown or missing arguments.
"""
shape_x =... | python | def embed(self, x):
"""Embedding function that takes discrete latent and returns embedding.
Args:
x: Input to the discretization bottleneck.
Returns:
Continuous embedding to be passed on to the decoder.
Raises:
ValueError: For unknown or missing arguments.
"""
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tensorflow/tensor2tensor | tensor2tensor/layers/vq_discrete.py | DiscreteBottleneck.discrete_bottleneck | def discrete_bottleneck(self, x):
"""Discretization bottleneck for latent variables.
Args:
x: Input to the discretization bottleneck.
Returns:
Embedding to pass to the decoder, discrete latent, loss, and the
embedding
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Raises:
ValueError: If projection... | python | def discrete_bottleneck(self, x):
"""Discretization bottleneck for latent variables.
Args:
x: Input to the discretization bottleneck.
Returns:
Embedding to pass to the decoder, discrete latent, loss, and the
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tensorflow/tensor2tensor | tensor2tensor/models/research/adafactor_experiments.py | mimic_adam_with_adafactor | def mimic_adam_with_adafactor(hparams):
"""Switch from Adam to Adafactor, approximating the behavior of Adam.
Some minor things may be different, like epsilon and beta1 correction.
Args:
hparams: model hyperparameters where "adam" in hparams.optimizer
"""
assert "adam" in hparams.optimizer
hparams.opt... | python | def mimic_adam_with_adafactor(hparams):
"""Switch from Adam to Adafactor, approximating the behavior of Adam.
Some minor things may be different, like epsilon and beta1 correction.
Args:
hparams: model hyperparameters where "adam" in hparams.optimizer
"""
assert "adam" in hparams.optimizer
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tensorflow/tensor2tensor | tensor2tensor/models/research/adafactor_experiments.py | afx_adam | def afx_adam():
"""Old version - Adam."""
hparams = transformer.transformer_base_v2()
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.999
hparams.symbol_modality_num_shards = 1
hparams.batch_size = 2048
hparams.optimizer = "adam"
hparams.learning_rate_schedule = (
"constant*rsq... | python | def afx_adam():
"""Old version - Adam."""
hparams = transformer.transformer_base_v2()
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.999
hparams.symbol_modality_num_shards = 1
hparams.batch_size = 2048
hparams.optimizer = "adam"
hparams.learning_rate_schedule = (
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