INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Computes the value loss.
Args:
value_net_apply: value net apply function with signature (params, ndarray of
shape (B, T+1) + OBS) -> ndarray(B, T+1, 1)
value_net_params: params of value_net_apply.
observations: np.ndarray of shape (B, T+1) + OBS
rewards: np.ndarray of shape (B, T) of rewards.
... | def value_loss(value_net_apply,
value_net_params,
observations,
rewards,
reward_mask,
gamma=0.99):
"""Computes the value loss.
Args:
value_net_apply: value net apply function with signature (params, ndarray of
shape (B, T+1) + OBS... |
Computes the value loss given the prediction of the value function.
Args:
value_prediction: np.ndarray of shape (B, T+1, 1)
rewards: np.ndarray of shape (B, T) of rewards.
reward_mask: np.ndarray of shape (B, T), the mask over rewards.
gamma: float, discount factor.
Returns:
The average L2 val... | def value_loss_given_predictions(value_prediction,
rewards,
reward_mask,
gamma=0.99):
"""Computes the value loss given the prediction of the value function.
Args:
value_prediction: np.ndarray of shape (B, T+1, 1)... |
r"""Computes TD-residuals from V(s) and rewards.
Where a `delta`, i.e. a td-residual is defined as:
delta_{b,t} = r_{b,t} + \gamma * v_{b,t+1} - v_{b,t}.
Args:
predicted_values: ndarray of shape (B, T+1). NOTE: Expects axis 2 was
squeezed. These represent V(s_bt) for b < B and t < T+1
rewards: nd... | def deltas(predicted_values, rewards, mask, gamma=0.99):
r"""Computes TD-residuals from V(s) and rewards.
Where a `delta`, i.e. a td-residual is defined as:
delta_{b,t} = r_{b,t} + \gamma * v_{b,t+1} - v_{b,t}.
Args:
predicted_values: ndarray of shape (B, T+1). NOTE: Expects axis 2 was
squeezed. Th... |
r"""Computes the GAE advantages given the one step TD-residuals.
The formula for a GAE advantage estimator is as follows:
A_{bt} = \sum_{l=0}^{\infty}(\gamma * \lambda)^{l}(\delta_{b,t+l}).
Internally we just call rewards_to_go, since it is the same computation.
Args:
td_deltas: np.ndarray of shape (B, ... | def gae_advantages(td_deltas, mask, lambda_=0.95, gamma=0.99):
r"""Computes the GAE advantages given the one step TD-residuals.
The formula for a GAE advantage estimator is as follows:
A_{bt} = \sum_{l=0}^{\infty}(\gamma * \lambda)^{l}(\delta_{b,t+l}).
Internally we just call rewards_to_go, since it is the s... |
Picks out the probabilities of the actions along batch and time-steps.
Args:
probab_observations: ndarray of shape `[B, T+1, A]`, where
probab_observations[b, t, i] contains the log-probability of action = i at
the t^th time-step in the b^th trajectory.
actions: ndarray of shape `[B, T]`, with ea... | def chosen_probabs(probab_observations, actions):
"""Picks out the probabilities of the actions along batch and time-steps.
Args:
probab_observations: ndarray of shape `[B, T+1, A]`, where
probab_observations[b, t, i] contains the log-probability of action = i at
the t^th time-step in the b^th traj... |
Computes the probability ratios for each time-step in a trajectory.
Args:
p_new: ndarray of shape [B, T+1, A] of the log-probabilities that the policy
network assigns to all the actions at each time-step in each batch using
the old parameters.
p_old: ndarray of shape [B, T+1, A], same as above, b... | def compute_probab_ratios(p_new, p_old, actions, reward_mask):
"""Computes the probability ratios for each time-step in a trajectory.
Args:
p_new: ndarray of shape [B, T+1, A] of the log-probabilities that the policy
network assigns to all the actions at each time-step in each batch using
the old p... |
PPO objective, with an eventual minus sign, given observations. | def ppo_loss(policy_net_apply,
new_policy_params,
old_policy_params,
value_net_apply,
value_net_params,
padded_observations,
padded_actions,
padded_rewards,
reward_mask,
gamma=0.99,
lambda_=... |
PPO objective, with an eventual minus sign, given predictions. | def ppo_loss_given_predictions(log_probab_actions_new,
log_probab_actions_old,
predicted_values,
padded_actions,
padded_rewards,
reward_mask,
... |
Computes the combined (clipped loss + value loss) given predictions. | def combined_loss_given_predictions(log_probab_actions_new,
log_probab_actions_old,
value_prediction,
padded_actions,
padded_rewards,
reward... |
Computes the combined (clipped loss + value loss) given observations. | def combined_loss(new_params,
old_params,
policy_and_value_net_apply,
padded_observations,
padded_actions,
padded_rewards,
reward_mask,
gamma=0.99,
lambda_=0.95,
... |
PPO optimizer step. | def ppo_opt_step(i,
opt_state,
ppo_opt_update,
policy_net_apply,
old_policy_params,
value_net_apply,
value_net_params,
padded_observations,
padded_actions,
padded_rewa... |
Value optimizer step. | def value_opt_step(i,
opt_state,
opt_update,
value_net_apply,
padded_observations,
padded_rewards,
reward_mask,
gamma=0.99):
"""Value optimizer step."""
value_params = trax_opt.get_pa... |
Policy and Value optimizer step. | def policy_and_value_opt_step(i,
opt_state,
opt_update,
policy_and_value_net_apply,
old_params,
padded_observations,
padded_actions,
... |
Runs the training loop for PPO, with fixed policy and value nets. | def training_loop(env=None,
env_name="CartPole-v0",
epochs=EPOCHS,
policy_net_fun=None,
value_net_fun=None,
policy_and_value_net_fun=None,
policy_optimizer_fun=None,
value_optimizer_fun=None,
... |
Download corpora for multinli.
Args:
tmp_dir: a string
Returns:
a string | def _maybe_download_corpora(tmp_dir):
"""Download corpora for multinli.
Args:
tmp_dir: a string
Returns:
a string
"""
mnli_filename = "MNLI.zip"
mnli_finalpath = os.path.join(tmp_dir, "MNLI")
if not tf.gfile.Exists(mnli_finalpath):
zip_filepath = generator_utils.maybe_download(
tmp_di... |
Generate mnli examples.
Args:
filename: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings | def _example_generator(filename):
"""Generate mnli examples.
Args:
filename: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings
"""
for idx, line in enumerate(tf.gfile.Open(filename, "rb")):
if idx == 0: continue # skip header
line = text_encoder.to_unicode_... |
Adds a residual connection to the filter x for the shake-shake model. | def shake_shake_skip_connection(x, output_filters, stride, is_training):
"""Adds a residual connection to the filter x for the shake-shake model."""
curr_filters = common_layers.shape_list(x)[-1]
if curr_filters == output_filters:
return x
stride_spec = [1, stride, stride, 1]
# Skip path 1.
path1 = tf.n... |
Building a 2 branching convnet. | def shake_shake_branch(x, output_filters, stride, rand_forward, rand_backward,
hparams):
"""Building a 2 branching convnet."""
is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
x = tf.nn.relu(x)
x = tf.layers.conv2d(
x,
output_filters, (3, 3),
strides=(stride, st... |
Builds a full shake-shake sub layer. | def shake_shake_block(x, output_filters, stride, hparams):
"""Builds a full shake-shake sub layer."""
is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
batch_size = common_layers.shape_list(x)[0]
# Generate random numbers for scaling the branches.
rand_forward = [
tf.random_uniform(
[... |
Builds many sub layers into one full layer. | def shake_shake_layer(x, output_filters, num_blocks, stride, hparams):
"""Builds many sub layers into one full layer."""
for block_num in range(num_blocks):
curr_stride = stride if (block_num == 0) else 1
with tf.variable_scope("layer_{}".format(block_num)):
x = shake_shake_block(x, output_filters, cu... |
Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU. | def shakeshake_small():
"""Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU."""
hparams = common_hparams.basic_params1()
hparams.batch_size = 128
hparams.hidden_size = 32
hparams.layer_prepostprocess_dropout = 0.0
hparams.dropout = 0
hparams.label_smoothing = 0.0
hparams.clip_grad_n... |
Check if metric has plateaued.
A metric has plateaued if the value has not increased/decreased (depending on
`decrease`) by `delta` for at least `num_steps`.
Args:
steps: list<int> list of global steps for values.
values: list<float> list of metric values.
num_steps: int, number of steps the metric ... | def has_metric_plateaued(steps, values, num_steps=100, delta=0.1,
decrease=True):
"""Check if metric has plateaued.
A metric has plateaued if the value has not increased/decreased (depending on
`decrease`) by `delta` for at least `num_steps`.
Args:
steps: list<int> list of global ... |
SAVP model hparams. | def next_frame_savp():
"""SAVP model hparams."""
hparams = sv2p_params.next_frame_sv2p()
hparams.add_hparam("z_dim", 8)
hparams.add_hparam("num_discriminator_filters", 32)
hparams.add_hparam("use_vae", True)
hparams.add_hparam("use_gan", False)
hparams.add_hparam("use_spectral_norm", True)
hparams.add_h... |
SAVP - VAE only model. | def next_frame_savp_vae():
"""SAVP - VAE only model."""
hparams = next_frame_savp()
hparams.use_vae = True
hparams.use_gan = False
hparams.latent_loss_multiplier = 1e-3
hparams.latent_loss_multiplier_schedule = "linear_anneal"
return hparams |
Default hyperparameters for a DietAdamOptimizer.
Returns:
a hyperparameters object. | def diet_adam_optimizer_params():
"""Default hyperparameters for a DietAdamOptimizer.
Returns:
a hyperparameters object.
"""
return hparam.HParams(
quantize=True, # use 16-bit fixed-point
quantization_scale=10.0 / tf.int16.max,
optimizer="DietAdam",
learning_rate=1.0,
learnin... |
SAVP - GAN only model. | def next_frame_savp_gan():
"""SAVP - GAN only model."""
hparams = next_frame_savp()
hparams.use_gan = True
hparams.use_vae = False
hparams.gan_loss_multiplier = 0.001
hparams.optimizer_adam_beta1 = 0.5
hparams.learning_rate_constant = 2e-4
hparams.gan_loss = "cross_entropy"
hparams.learning_rate_decay... |
A two-layer feed-forward network with relu activation on hidden layer.
Uses diet variables.
Recomputes hidden layer on backprop to save activation memory.
Args:
x: a Tensor with shape [batch, io_size]
hidden_size: an integer
params: a diet variable HParams object.
Returns:
a Tensor with shape... | def diet_expert(x, hidden_size, params):
"""A two-layer feed-forward network with relu activation on hidden layer.
Uses diet variables.
Recomputes hidden layer on backprop to save activation memory.
Args:
x: a Tensor with shape [batch, io_size]
hidden_size: an integer
params: a diet variable HPara... |
Quantize x according to params, optionally randomizing the rounding. | def _quantize(x, params, randomize=True):
"""Quantize x according to params, optionally randomizing the rounding."""
if not params.quantize:
return x
if not randomize:
return tf.bitcast(
tf.cast(x / params.quantization_scale, tf.int16), tf.float16)
abs_x = tf.abs(x)
sign_x = tf.sign(x)
y =... |
Dequantize q according to params. | def _dequantize(q, params):
"""Dequantize q according to params."""
if not params.quantize:
return q
return tf.to_float(tf.bitcast(q, tf.int16)) * params.quantization_scale |
Create a custom variable getter for diet variables according to params. | def make_diet_var_getter(params):
"""Create a custom variable getter for diet variables according to params."""
def diet_var_initializer(shape, dtype, partition_info=None):
"""Initializer for a diet variable."""
del dtype
del partition_info
with common_layers.fn_device_dependency("diet_init") as o... |
Call function with args; use diet variables according to params. | def _fn_with_diet_vars(fn, args, params):
"""Call function with args; use diet variables according to params."""
vs_ctr = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Custom gradient function."""
del outputs # recomputing below
with common_layers.fn_device_dependency("diet_grad",
... |
Decorator for graph-building function to use diet variables. | def fn_with_diet_vars(params):
"""Decorator for graph-building function to use diet variables."""
params = copy.copy(params)
def dec(fn):
def wrapped(*args):
return _fn_with_diet_vars(fn, args, params)
return wrapped
return dec |
Create the factorized Adam accumulators for diet variables. | def create_slots(self, var):
"""Create the factorized Adam accumulators for diet variables."""
params = self.params
shape = var.get_shape().as_list()
if not hasattr(params, "slots"):
params.slots = defaultdict(dict)
name = var.op.name
slots = params.slots[name]
if params.factored_se... |
Update the variable and its slots. | def update_variable(self, var, grad_var):
"""Update the variable and its slots."""
params = self.params
global_step = tf.to_float(self.global_step) + 1
# compute learning rate
lrate = params.learning_rate
if params.learning_rate_decay_scheme == "noam":
lrate *= tf.minimum(global_step * pa... |
Construct EstimatorSpec for EVAL mode. | def estimator_spec_eval(
self, features, logits, labels, loss, restore_hook, use_tpu):
"""Construct EstimatorSpec for EVAL mode."""
hparams = self.hparams
problem = hparams.problem
if logits.get_shape().ndims == 3:
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
# Support for mult... |
Generator for the dataset samples.
If not present, download and extract the dataset.
Args:
tmp_dir: path to the directory where to download the dataset.
pb_cst: CodingPbConstants object defining paths
Yields:
A CodingPbInfo object containing the next challenge informations. | def generator_samples(tmp_dir, pb_cst):
"""Generator for the dataset samples.
If not present, download and extract the dataset.
Args:
tmp_dir: path to the directory where to download the dataset.
pb_cst: CodingPbConstants object defining paths
Yields:
A CodingPbInfo object containing the next cha... |
Adds a stack of LSTM layers on top of input.
Args:
inputs: The input `Tensor`, shaped `[batch_size, time_steps, hidden_size]`.
sequence_length: Lengths of the actual input sequence, excluding padding; a
`Tensor` shaped `[batch_size]`.
hparams: HParams; hyperparameters.
train: bool; `True` whe... | def lstm(inputs, sequence_length, hparams, train, name, initial_state=None):
"""Adds a stack of LSTM layers on top of input.
Args:
inputs: The input `Tensor`, shaped `[batch_size, time_steps, hidden_size]`.
sequence_length: Lengths of the actual input sequence, excluding padding; a
`Tensor` shaped ... |
Run LSTM cell with attention on inputs of shape [batch x time x size].
Args:
inputs: The decoder input `Tensor`, shaped `[batch_size, decoder_steps,
hidden_size]`.
hparams: HParams; hyperparameters.
train: bool; `True` when constructing training graph to enable dropout.
name: string; Create v... | def lstm_attention_decoder(inputs, hparams, train, name, initial_state,
encoder_outputs, encoder_output_length,
decoder_input_length):
"""Run LSTM cell with attention on inputs of shape [batch x time x size].
Args:
inputs: The decoder input `Tensor`, shaped... |
The basic LSTM seq2seq model, main step used for training. | def lstm_seq2seq_internal(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model, main step used for training."""
with tf.variable_scope("lstm_seq2seq"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs = common_layers.flatt... |
LSTM seq2seq model with attention, main step used for training. | def lstm_seq2seq_internal_attention(inputs, targets, hparams, train,
inputs_length, targets_length):
"""LSTM seq2seq model with attention, main step used for training."""
with tf.variable_scope("lstm_seq2seq_attention"):
# Flatten inputs.
inputs = common_layers.flatten4d3... |
Bidirectional LSTM for encoding inputs that are [batch x time x size]. | def lstm_bid_encoder(inputs, sequence_length, hparams, train, name):
"""Bidirectional LSTM for encoding inputs that are [batch x time x size]."""
with tf.variable_scope(name):
cell_fw = tf.nn.rnn_cell.MultiRNNCell(
[_dropout_lstm_cell(hparams, train)
for _ in range(hparams.num_hidden_layers)])... |
The basic LSTM seq2seq model with bidirectional encoder. | def lstm_seq2seq_internal_bid_encoder(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model with bidirectional encoder."""
with tf.variable_scope("lstm_seq2seq_bid_encoder"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs... |
LSTM seq2seq model with attention, main step used for training. | def lstm_seq2seq_internal_attention_bid_encoder(inputs, targets, hparams,
train):
"""LSTM seq2seq model with attention, main step used for training."""
with tf.variable_scope("lstm_seq2seq_attention_bid_encoder"):
inputs_length = common_layers.length_from_embeddin... |
hparams for LSTM. | def lstm_seq2seq():
"""hparams for LSTM."""
hparams = common_hparams.basic_params1()
hparams.daisy_chain_variables = False
hparams.batch_size = 1024
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_decay ... |
Base attention params. | def lstm_attention_base():
"""Base attention params."""
hparams = lstm_seq2seq()
hparams.add_hparam("attention_layer_size", hparams.hidden_size)
hparams.add_hparam("output_attention", True)
hparams.add_hparam("num_heads", 1)
return hparams |
Basic LSTM Params. | def lstm_asr_v1():
"""Basic LSTM Params."""
hparams = lstm_bahdanau_attention()
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.batch_size = 36
hparams.max_input_seq_length = 600000
hparams.max_target_seq_length = 350
hparams.max_length = hparams.max_input_seq_length
hparams.min_length... |
Hparams for LSTM with area attention. | def lstm_area_attention_base():
"""Hparams for LSTM with area attention."""
hparams = lstm_luong_attention()
hparams.batch_size = 16384
hparams.num_hidden_layers = 2
hparams.hidden_size = 1024
hparams.num_heads = 4
hparams.dropout = 0.2
hparams.learning_rate = 0.1
hparams.max_area_width = 2
hparams.... |
Create a run config.
Args:
hp: model hyperparameters
Returns:
a run config | def create_surrogate_run_config(hp):
"""Create a run config.
Args:
hp: model hyperparameters
Returns:
a run config
"""
save_ckpt_steps = max(FLAGS.iterations_per_loop, FLAGS.local_eval_frequency)
save_ckpt_secs = FLAGS.save_checkpoints_secs or None
if save_ckpt_secs:
save_ckpt_steps = None
... |
Construct input pipeline. | def prepare_data(problem, hparams, params, config):
"""Construct input pipeline."""
input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams, force_repeat=True)
dataset = input_fn(params, config)
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["... |
Transform a string with a filename into a list of float32.
Args:
s: path to the file with a waveform.
Returns:
samples: list of int16s | def encode(self, s):
"""Transform a string with a filename into a list of float32.
Args:
s: path to the file with a waveform.
Returns:
samples: list of int16s
"""
# Make sure that the data is a single channel, 16bit, 16kHz wave.
# TODO(chorowski): the directory may not be writable,... |
Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the waveform was saved.
Raises:
ValueError: if the ids are not of the appropriate size. | def decode(self, ids):
"""Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the waveform was saved.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
_, tmp_file_path = te... |
Creates and returns a new vertex.
Returns:
A new Vertex instance with a unique index. | def new_vertex(self):
"""Creates and returns a new vertex.
Returns:
A new Vertex instance with a unique index.
"""
vertex = Vertex(len(self.vertices))
self.vertices.append(vertex)
return vertex |
Returns or Creates a Vertex mapped by key.
Args:
key: A string reference for a vertex. May refer to a new Vertex in which
case it will be created.
Returns:
A the Vertex mapped to by key. | def get_vertex(self, key):
"""Returns or Creates a Vertex mapped by key.
Args:
key: A string reference for a vertex. May refer to a new Vertex in which
case it will be created.
Returns:
A the Vertex mapped to by key.
"""
if key in self.vertex_map:
return self.vertex_map[ke... |
Returns a new edge connecting source and target vertices.
Args:
source: The source Vertex.
target: The target Vertex.
Returns:
A new Edge linking source to target. | def add_edge(self, source, target):
"""Returns a new edge connecting source and target vertices.
Args:
source: The source Vertex.
target: The target Vertex.
Returns:
A new Edge linking source to target.
"""
edge = Edge(len(self.edges))
self.edges.append(edge)
source.out_e... |
Returns a simplified dictionary representing the Graph.
Returns:
A dictionary that can easily be serialized to JSON. | def to_dict(self):
"""Returns a simplified dictionary representing the Graph.
Returns:
A dictionary that can easily be serialized to JSON.
"""
return {
"node": [v.to_dict() for v in self.vertices],
"edge": [e.to_dict() for e in self.edges]
} |
Self-attention layer with source as memory antecedent. | def attend(x, source, hparams, name):
"""Self-attention layer with source as memory antecedent."""
with tf.variable_scope(name):
x = tf.squeeze(x, axis=2)
if len(source.get_shape()) > 3:
source = tf.squeeze(source, axis=2)
source = common_attention.add_timing_signal_1d(source)
y = common_atten... |
Calculate softmax(x), select top-k and rescale to sum to 1. | def top_k_softmax(x, k):
"""Calculate softmax(x), select top-k and rescale to sum to 1."""
x = tf.nn.softmax(x)
top_x, _ = tf.nn.top_k(x, k=k+1)
min_top = tf.reduce_min(top_x, axis=-1, keepdims=True)
x = tf.nn.relu((x - min_top) + 1e-12)
x /= tf.reduce_sum(x, axis=-1, keepdims=True)
return x, tf.reduce_ma... |
Compress. | def compress(x, c, is_2d, hparams, name):
"""Compress."""
with tf.variable_scope(name):
# Run compression by strided convs.
cur = x
k1 = (3, 3) if is_2d else (3, 1)
k2 = (2, 2) if is_2d else (2, 1)
cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams, "rc")
if c is not None and h... |
Original Transformer decoder. | def decode_transformer(encoder_output,
encoder_decoder_attention_bias,
targets,
hparams,
name,
task=None,
causal=True):
"""Original Transformer decoder."""
orig_hparams = hparams... |
Latent prediction and loss. | def ae_latent_softmax(latents_pred, latents_discrete, hparams):
"""Latent prediction and loss."""
vocab_size = 2 ** hparams.z_size
if hparams.num_decode_blocks < 2:
latents_logits = tf.layers.dense(latents_pred, vocab_size,
name="extra_logits")
if hparams.logit_normali... |
Sample from the latent space in the autoencoder. | def ae_latent_sample(latents_dense, inputs, ed, embed, iters, hparams):
"""Sample from the latent space in the autoencoder."""
if hparams.num_decode_blocks < 2 and hparams.sampling_temp == 0.0:
# TODO(lukaszkaiser): beam-search only works in non-blocked mode for now.
tf.logging.info("Running beam-search for... |
AE Transformer, main step used for training. | def ae_transformer_internal(inputs,
targets,
target_space,
hparams,
cache=None,
predict_mask=1.0):
"""AE Transformer, main step used for training."""
# Summaries break with the... |
Set of hyperparameters. | def transformer_ae_small():
"""Set of hyperparameters."""
hparams = transformer.transformer_small()
hparams.batch_size = 2048
hparams.learning_rate = 0.2
hparams.learning_rate_warmup_steps = 4000
hparams.num_hidden_layers = 3
hparams.hidden_size = 384
hparams.filter_size = 2048
hparams.add_hparam("com... |
Hyperparameters for CIFAR-10 experiments. | def imagetransformer_ae_cifar():
"""Hyperparameters for CIFAR-10 experiments."""
hparams = transformer_ae_small()
hparams.filter_size = 512
hparams.num_compress_steps = 3
hparams.startup_steps = 10000
hparams.is_2d = 0
hparams.learning_rate_warmup_steps = 8000
hparams.learning_rate = 0.2
hparams.hidde... |
For 64x64 ImageNet. ~56M trainable variables. | def imagetransformer_ae_imagenet():
"""For 64x64 ImageNet. ~56M trainable variables."""
hparams = imagetransformer_ae_cifar()
hparams.max_length = int(64 * 64 * 3)
hparams.img_len = 64
hparams.num_heads = 4 # Heads are expensive on TPUs.
# Reduce architecture from 32x32 CIFAR-10 in order to fit in memory.
... |
Set of hyperparameters. | def transformer_ae_base():
"""Set of hyperparameters."""
hparams = transformer_ae_small()
hparams.batch_size = 2048
hparams.hidden_size = 512
hparams.filter_size = 4096
hparams.num_hidden_layers = 6
return hparams |
Set of hyperparameters. | def transformer_ae_a3():
"""Set of hyperparameters."""
hparams = transformer_ae_base()
hparams.batch_size = 4096
hparams.layer_prepostprocess_dropout = 0.3
hparams.optimizer = "Adafactor"
hparams.learning_rate = 0.25
hparams.learning_rate_warmup_steps = 10000
return hparams |
Set of hyperparameters. | def transformer_ae_base_noatt():
"""Set of hyperparameters."""
hparams = transformer_ae_base()
hparams.reshape_method = "slice"
hparams.bottleneck_kind = "dvq"
hparams.hidden_size = 512
hparams.num_blocks = 1
hparams.num_decode_blocks = 1
hparams.z_size = 12
hparams.do_attend_decompress = False
retu... |
Set of hyperparameters. | def transformer_ae_small_noatt():
"""Set of hyperparameters."""
hparams = transformer_ae_small()
hparams.reshape_method = "slice"
hparams.bottleneck_kind = "dvq"
hparams.hidden_size = 512
hparams.num_blocks = 1
hparams.num_decode_blocks = 1
hparams.z_size = 12
hparams.do_attend_decompress = False
re... |
Basic transformer_sketch hparams. | def transformer_sketch():
"""Basic transformer_sketch hparams."""
hparams = transformer.transformer_small()
hparams.num_compress_steps = 4
hparams.batch_size = 32
hparams.clip_grad_norm = 2.
hparams.sampling_method = "random"
return hparams |
Get the layers module good for TF 1 and TF 2 work for now. | def layers():
"""Get the layers module good for TF 1 and TF 2 work for now."""
global _cached_layers
if _cached_layers is not None:
return _cached_layers
layers_module = tf.layers
try:
from tensorflow.python import tf2 # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
if tf2.enable... |
Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
a list of dimension numbers in which noise_shape should be 1. The random
keep/drop tensor has dimensionality 1 along these dimensions.
Args:
x: a floating point tens... | def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
"""Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
a list of dimension numbers in which noise_shape should be 1. The random
keep/drop tensor... |
Saturating sigmoid: 1.2 * sigmoid(x) - 0.1 cut to [0, 1]. | def saturating_sigmoid(x):
"""Saturating sigmoid: 1.2 * sigmoid(x) - 0.1 cut to [0, 1]."""
with tf.name_scope("saturating_sigmoid", values=[x]):
y = tf.sigmoid(x)
return tf.minimum(1.0, tf.maximum(0.0, 1.2 * y - 0.1)) |
Inverse-decay exponentially from 0.01 to 1.0 reached at max_step. | def inverse_exp_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay exponentially from 0.01 to 1.0 reached at max_step."""
inv_base = tf.exp(tf.log(min_value) / float(max_step))
if step is None:
step = tf.train.get_global_step()
if step is None:
return 1.0
step = to_float(step)
return inv_b... |
Inverse-decay linearly from 0.01 to 1.0 reached at max_step. | def inverse_lin_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay linearly from 0.01 to 1.0 reached at max_step."""
if step is None:
step = tf.train.get_global_step()
if step is None:
return 1.0
step = to_float(step)
progress = tf.minimum(step / float(max_step), 1.0)
return progress * (1.... |
The shake-shake sum of 2 tensors, python version. | def shakeshake2_py(x, y, equal=False, individual=False):
"""The shake-shake sum of 2 tensors, python version."""
if equal:
alpha = 0.5
elif individual:
alpha = tf.random_uniform(tf.get_shape(x)[:1])
else:
alpha = tf.random_uniform([])
return alpha * x + (1.0 - alpha) * y |
Overriding gradient for shake-shake of 2 tensors. | def shakeshake2_grad(x1, x2, dy):
"""Overriding gradient for shake-shake of 2 tensors."""
y = shakeshake2_py(x1, x2)
dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy])
return dx |
Overriding gradient for shake-shake of 2 tensors. | def shakeshake2_indiv_grad(x1, x2, dy):
"""Overriding gradient for shake-shake of 2 tensors."""
y = shakeshake2_py(x1, x2, individual=True)
dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy])
return dx |
Overriding gradient for shake-shake of 2 tensors. | def shakeshake2_equal_grad(x1, x2, dy):
"""Overriding gradient for shake-shake of 2 tensors."""
y = shakeshake2_py(x1, x2, equal=True)
dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy])
return dx |
Multi-argument shake-shake, currently approximated by sums of 2. | def shakeshake(xs, equal_grad=False):
"""Multi-argument shake-shake, currently approximated by sums of 2."""
if len(xs) == 1:
return xs[0]
div = (len(xs) + 1) // 2
arg1 = shakeshake(xs[:div], equal_grad=equal_grad)
arg2 = shakeshake(xs[div:], equal_grad=equal_grad)
if equal_grad:
return shakeshake2_... |
Conversion of pixel values to real numbers. | def convert_rgb_to_real(x):
"""Conversion of pixel values to real numbers."""
with tf.name_scope("rgb_to_real", values=[x]):
x = to_float(x)
x /= 255.0
return x |
Conversion of pixel values to real numbers. | def convert_rgb_to_symmetric_real(x):
"""Conversion of pixel values to real numbers."""
with tf.name_scope("rgb_to_real", values=[x]):
x = to_float(x)
# Convert each pixel intensity in [0, 1, 2, ..., 255] into a real number in
# the range [-1, 1].
x = (x / 127.5) - 1
return x |
Make x n-d with squeeze and expand_dims. | def expand_squeeze_to_nd(x, n, squeeze_dim=2, expand_dim=-1):
"""Make x n-d with squeeze and expand_dims."""
if len(x.shape) > n:
while len(x.shape) != n:
x = tf.squeeze(x, [squeeze_dim])
else:
while len(x.shape) != n:
x = tf.expand_dims(x, expand_dim)
return x |
Image standardization on batches and videos. | def standardize_images(x):
"""Image standardization on batches and videos."""
with tf.name_scope("standardize_images", values=[x]):
x_shape = shape_list(x)
x = to_float(tf.reshape(x, [-1] + x_shape[-3:]))
x_mean = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
x_variance = tf.reduce_mean(
tf.... |
Flatten a 4d-tensor into a 3d-tensor by joining width and height. | def flatten4d3d(x):
"""Flatten a 4d-tensor into a 3d-tensor by joining width and height."""
xshape = shape_list(x)
result = tf.reshape(x, [xshape[0], xshape[1] * xshape[2], xshape[3]])
return result |
Version of tf.gather that works faster on tpu. | def gather(params, indices, dtype=tf.float32):
"""Version of tf.gather that works faster on tpu."""
if not is_xla_compiled():
return tf.gather(params, indices)
vocab_size = params.get_shape().as_list()[0]
indices_flat = tf.reshape(indices, [-1])
out = tf.matmul(tf.one_hot(indices_flat, vocab_size, dtype=d... |
TPU hack for tf.cumsum.
This is equivalent to tf.cumsum and is faster on TPU as of 04/2018 unless
the axis dimension is very large.
Args:
x: a Tensor
axis: an integer
exclusive: a boolean
Returns:
Tensor of the same shape as x. | def cumsum(x, axis=0, exclusive=False):
"""TPU hack for tf.cumsum.
This is equivalent to tf.cumsum and is faster on TPU as of 04/2018 unless
the axis dimension is very large.
Args:
x: a Tensor
axis: an integer
exclusive: a boolean
Returns:
Tensor of the same shape as x.
"""
if not is_xl... |
Like tf.nn.dropout, but does not scale up. Works on integers also.
Args:
x: a Tensor
keep_prob: a floating point number
Returns:
Tensor of the same shape as x. | def dropout_no_scaling(x, keep_prob):
"""Like tf.nn.dropout, but does not scale up. Works on integers also.
Args:
x: a Tensor
keep_prob: a floating point number
Returns:
Tensor of the same shape as x.
"""
if keep_prob == 1.0:
return x
mask = tf.less(tf.random_uniform(tf.shape(x)), keep_pr... |
Embed x of type int64 into dense vectors, reducing to max 4 dimensions. | def embedding(x,
vocab_size,
dense_size,
name=None,
reuse=None,
multiplier=1.0,
symbol_dropout_rate=0.0,
embedding_var=None,
dtype=tf.float32):
"""Embed x of type int64 into dense vectors, reducing to max 4... |
Shift the second dimension of x right by one. | def shift_right(x, pad_value=None):
"""Shift the second dimension of x right by one."""
if pad_value is None:
shifted_targets = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]])[:, :-1, :, :]
else:
shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1, :, :]
return shifted_targets |
Shift the second dimension of x right by one. | def shift_right_3d(x, pad_value=None):
"""Shift the second dimension of x right by one."""
if pad_value is None:
shifted_targets = tf.pad(x, [[0, 0], [1, 0], [0, 0]])[:, :-1, :]
else:
shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1, :]
return shifted_targets |
Shift the second dimension of x right by one. | def shift_right_2d(x, pad_value=None):
"""Shift the second dimension of x right by one."""
if pad_value is None:
shifted_targets = tf.pad(x, [[0, 0], [1, 0]])[:, :-1]
else:
shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1]
return shifted_targets |
Use a strided convolution to downsample x by 2, `nbr_steps` times.
We use stride and filter size 2 to avoid the checkerboard problem of deconvs.
As detailed in http://distill.pub/2016/deconv-checkerboard/.
Args:
x: a `Tensor` with shape `[batch, spatial, depth]` or
`[batch, spatial_1, spatial_2, depth]... | def conv_stride2_multistep(x, nbr_steps, output_filters, name=None, reuse=None):
"""Use a strided convolution to downsample x by 2, `nbr_steps` times.
We use stride and filter size 2 to avoid the checkerboard problem of deconvs.
As detailed in http://distill.pub/2016/deconv-checkerboard/.
Args:
x: a `Tens... |
Use a deconvolution to upsample x by 2**`nbr_steps`.
Args:
x: a `Tensor` with shape `[batch, spatial, depth]` or
`[batch, spatial_1, spatial_2, depth]`
nbr_steps: an int specifying the number of doubling upsample rounds to
apply.
output_filters: an int specifying the filter count for the deconv... | def deconv_stride2_multistep(x,
nbr_steps,
output_filters,
name=None,
reuse=None):
"""Use a deconvolution to upsample x by 2**`nbr_steps`.
Args:
x: a `Tensor` with shape `[batch, spatial, depth]`... |
Conditional conv_fn making kernel 1d or 2d depending on inputs shape. | def conv_internal(conv_fn, inputs, filters, kernel_size, **kwargs):
"""Conditional conv_fn making kernel 1d or 2d depending on inputs shape."""
static_shape = inputs.get_shape()
if not static_shape or len(static_shape) != 4:
raise ValueError("Inputs to conv must have statically known rank 4. "
... |
Sub-separable convolution. If separability == 0 it's a separable_conv. | def subseparable_conv(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution. If separability == 0 it's a separable_conv."""
def conv_fn(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution, splits into separability-many blocks."""
separability = None
if "separability" i... |
Version of conv1d that works on TPU (as of 11/2017).
Args:
inputs: a Tensor with shape [batch, length, input_depth].
filters: an integer.
kernel_size: an integer.
padding: a string - "SAME" or "LEFT".
name: a string.
Returns:
a Tensor with shape [batch, length, filters]. | def tpu_conv1d(inputs, filters, kernel_size, padding="SAME", name="tpu_conv1d"):
"""Version of conv1d that works on TPU (as of 11/2017).
Args:
inputs: a Tensor with shape [batch, length, input_depth].
filters: an integer.
kernel_size: an integer.
padding: a string - "SAME" or "LEFT".
name: a st... |
Create Variables for layer norm. | def layer_norm_vars(filters):
"""Create Variables for layer norm."""
scale = tf.get_variable(
"layer_norm_scale", [filters], initializer=tf.ones_initializer())
bias = tf.get_variable(
"layer_norm_bias", [filters], initializer=tf.zeros_initializer())
return scale, bias |
Layer norm raw computation. | def layer_norm_compute(x, epsilon, scale, bias, layer_collection=None):
"""Layer norm raw computation."""
# Save these before they get converted to tensors by the casting below
params = (scale, bias)
epsilon, scale, bias = [cast_like(t, x) for t in [epsilon, scale, bias]]
mean = tf.reduce_mean(x, axis=[-1],... |
Layer normalize the tensor x, averaging over the last dimension. | def layer_norm(x,
filters=None,
epsilon=1e-6,
name=None,
reuse=None,
layer_collection=None):
"""Layer normalize the tensor x, averaging over the last dimension."""
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.