本文整理汇总了Python中tensorflow.compat.v1.stack方法的典型用法代码示例。如果您正苦于以下问题:Python v1.stack方法的具体用法?Python v1.stack怎么用?Python v1.stack使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.compat.v1
的用法示例。
在下文中一共展示了v1.stack方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: two_class_log_likelihood
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def two_class_log_likelihood(predictions, labels, weights_fn=None):
"""Log-likelihood for two class classification with 0/1 labels.
Args:
predictions: A float valued tensor of shape [`batch_size`]. Each
component should be between 0 and 1.
labels: An int valued tensor of shape [`batch_size`]. Each component
should either be 0 or 1.
weights_fn: unused.
Returns:
A pair, with the average log likelihood in the first component.
"""
del weights_fn
float_predictions = tf.cast(tf.squeeze(predictions), dtype=tf.float64)
batch_probs = tf.stack([1. - float_predictions, float_predictions], axis=-1)
int_labels = tf.cast(tf.squeeze(labels), dtype=tf.int32)
onehot_targets = tf.cast(tf.one_hot(int_labels, 2), dtype=tf.float64)
chosen_probs = tf.einsum(
"ij,ij->i", batch_probs, onehot_targets, name="chosen_probs")
avg_log_likelihood = tf.reduce_mean(tf.log(chosen_probs))
return avg_log_likelihood, tf.constant(1.0)
示例2: combine
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def combine(self, expert_out, multiply_by_gates=True):
"""Sum together the expert output, multiplied by the corresponding gates.
Args:
expert_out: a list of `num_experts` `Tensor`s, each with shape
`[expert_batch_size_i, <extra_output_dims>]`.
multiply_by_gates: a boolean.
Returns:
a list of num_datashards `Tensor`s with shapes
`[batch_size[d], <extra_output_dims>]`.
"""
expert_part_sizes = tf.unstack(
tf.stack([d.part_sizes for d in self._dispatchers]),
num=self._ep.n,
axis=1)
# list of lists of shape [num_experts][num_datashards]
expert_output_parts = self._ep(tf.split, expert_out, expert_part_sizes)
expert_output_parts_t = transpose_list_of_lists(expert_output_parts)
def my_combine(dispatcher, parts):
return dispatcher.combine(
common_layers.convert_gradient_to_tensor(tf.concat(parts, 0)),
multiply_by_gates=multiply_by_gates)
return self._dp(my_combine, self._dispatchers, expert_output_parts_t)
示例3: actnorm_3d
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def actnorm_3d(name, x, logscale_factor=3.):
"""Applies actnorm to each time-step independently.
There are a total of 2*n_channels*n_steps parameters learnt.
Args:
name: variable scope.
x: 5-D Tensor, (NTHWC)
logscale_factor: Increases the learning rate of the scale by
logscale_factor.
Returns:
x: 5-D Tensor, (NTHWC) with the per-timestep, per-channel normalization.
"""
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
x = tf.unstack(x, axis=1)
x_normed = []
for ind, x_step in enumerate(x):
x_step, _ = actnorm("actnorm_%d" % ind, x_step,
logscale_factor=logscale_factor)
x_normed.append(x_step)
return tf.stack(x_normed, axis=1), None
示例4: inject_latent
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def inject_latent(self, layer, inputs, target, action):
"""Inject a VAE-style latent."""
del action
# Latent for stochastic model
filters = 128
full_video = tf.stack(inputs + [target], axis=1)
latent_mean, latent_std = self.construct_latent_tower(
full_video, time_axis=1)
latent = common_video.get_gaussian_tensor(latent_mean, latent_std)
latent = tfl.flatten(latent)
latent = tf.expand_dims(latent, axis=1)
latent = tf.expand_dims(latent, axis=1)
latent_mask = tfl.dense(latent, filters, name="latent_mask")
zeros_mask = tf.zeros(
common_layers.shape_list(layer)[:-1] + [filters], dtype=tf.float32)
layer = tf.concat([layer, latent_mask + zeros_mask], axis=-1)
extra_loss = self.get_kl_loss([latent_mean], [latent_std])
return layer, extra_loss
示例5: reward_prediction_mid
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def reward_prediction_mid(
self, input_images, input_reward, action, latent, mid_outputs):
"""Builds a reward prediction network from intermediate layers."""
encoded = []
for i, output in enumerate(mid_outputs):
enc = output
enc = tfl.conv2d(enc, 64, [3, 3], strides=(1, 1), activation=tf.nn.relu)
enc = tfl.conv2d(enc, 32, [3, 3], strides=(2, 2), activation=tf.nn.relu)
enc = tfl.conv2d(enc, 16, [3, 3], strides=(2, 2), activation=tf.nn.relu)
enc = tfl.flatten(enc)
enc = tfl.dense(enc, 64, activation=tf.nn.relu, name="rew_enc_%d" % i)
encoded.append(enc)
x = encoded
x = tf.stack(x, axis=1)
x = tfl.flatten(x)
x = tfl.dense(x, 256, activation=tf.nn.relu, name="rew_dense1")
x = tfl.dense(x, 128, activation=tf.nn.relu, name="rew_dense2")
return x
示例6: argmax_with_score
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def argmax_with_score(logits, axis=None):
"""Argmax along with the value."""
axis = axis or len(logits.get_shape()) - 1
predictions = tf.argmax(logits, axis=axis)
logits_shape = shape_list(logits)
prefix_shape, vocab_size = logits_shape[:-1], logits_shape[-1]
prefix_size = 1
for d in prefix_shape:
prefix_size *= d
# Flatten to extract scores
flat_logits = tf.reshape(logits, [prefix_size, vocab_size])
flat_predictions = tf.reshape(predictions, [prefix_size])
flat_indices = tf.stack(
[tf.range(tf.to_int64(prefix_size)),
tf.to_int64(flat_predictions)],
axis=1)
flat_scores = tf.gather_nd(flat_logits, flat_indices)
# Unflatten
scores = tf.reshape(flat_scores, prefix_shape)
return predictions, scores
示例7: _scanning_pack
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def _scanning_pack(self, dataset):
"""Apply scan based pack to a dataset."""
if self._chop_long_sequences:
dataset = dataset.map(lambda x: (x[:self._packed_length],))
else:
dataset = dataset.filter(lambda *x: tf.reduce_max( # pylint: disable=g-long-lambda
tf.stack([tf.shape(i)[0] for i in x]), axis=0) <= self._packed_length)
# In order to retrieve the sequences which are still in the queue when the
# dataset is exhausted, we feed dummy sequences which are guaranteed to
# displace the remaining elements.
dataset = dataset.concatenate(
tf.data.Dataset.range(self._queue_size).map(self._eviction_fn))
initial_state = self._scan_initial_state()
step_fn = functools.partial(
tf.autograph.to_graph(_scan_step_fn), packed_length=self._packed_length,
queue_size=self._queue_size, spacing=self._spacing,
num_sequences=self._num_sequences, token_dtype=self._token_dtype)
dataset = dataset.apply(tf.data.experimental.scan(initial_state, step_fn))
is_real_sample = lambda valid_sample, _: valid_sample
return dataset.filter(is_real_sample)
示例8: _merge_decode_results
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def _merge_decode_results(self, decode_results):
"""Merge across time."""
assert decode_results
time_axis = 1
zipped_results = lstm_utils.LstmDecodeResults(*list(zip(*decode_results)))
if zipped_results.rnn_output[0] is None:
rnn_output = None
rnn_input = None
else:
rnn_output = tf.concat(zipped_results.rnn_output, axis=time_axis)
rnn_input = tf.concat(zipped_results.rnn_input, axis=time_axis)
return lstm_utils.LstmDecodeResults(
rnn_output=rnn_output,
rnn_input=rnn_input,
samples=tf.concat(zipped_results.samples, axis=time_axis),
final_state=zipped_results.final_state[-1],
final_sequence_lengths=tf.stack(
zipped_results.final_sequence_lengths, axis=time_axis))
示例9: resize_and_crop_boxes
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def resize_and_crop_boxes(self):
"""Resize boxes and crop it to the self._output dimension."""
boxlist = preprocessor.box_list.BoxList(self._boxes)
boxes = preprocessor.box_list_scale(
boxlist, self._scaled_height, self._scaled_width).get()
# Adjust box coordinates based on the offset.
box_offset = tf.stack([self._crop_offset_y, self._crop_offset_x,
self._crop_offset_y, self._crop_offset_x,])
boxes -= tf.cast(tf.reshape(box_offset, [1, 4]), tf.float32)
# Clip the boxes.
boxes = self.clip_boxes(boxes)
# Filter out ground truth boxes that are all zeros.
indices = tf.where(tf.not_equal(tf.reduce_sum(boxes, axis=1), 0))
boxes = tf.gather_nd(boxes, indices)
classes = tf.gather_nd(self._classes, indices)
return boxes, classes
示例10: _build_train_op
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def _build_train_op(self):
"""Builds a training op.
Returns:
train_op: An op performing one step of training from replay data.
"""
actions = self._replay.actions
indices = tf.stack([tf.range(actions.shape[0]), actions], axis=-1)
replay_chosen_q = tf.gather_nd(
self._replay_net_outputs.q_heads, indices=indices)
target = tf.stop_gradient(self._build_target_q_op())
loss = tf.losses.huber_loss(
target, replay_chosen_q, reduction=tf.losses.Reduction.NONE)
q_head_losses = tf.reduce_mean(loss, axis=0)
final_loss = tf.reduce_mean(q_head_losses)
if self.summary_writer is not None:
with tf.variable_scope('Losses'):
tf.summary.scalar('HuberLoss', final_loss)
return self.optimizer.minimize(final_loss)
示例11: call
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def call(self, state):
"""Creates the output tensor/op given the input state tensor.
See https://www.tensorflow.org/api_docs/python/tf/keras/Model for more
information on this. Note that tf.keras.Model implements `call` which is
wrapped by `__call__` function by tf.keras.Model.
Args:
state: Tensor, input tensor.
Returns:
collections.namedtuple, output ops (graph mode) or output tensors (eager).
"""
unordered_q_networks = [
network(state).q_values for network in self._q_networks]
unordered_q_networks = tf.stack(unordered_q_networks, axis=-1)
q_networks, q_values = combine_q_functions(unordered_q_networks,
self._transform_strategy,
**self._kwargs)
return MultiNetworkNetworkType(q_networks, unordered_q_networks, q_values)
示例12: apply_piecewise_monotonic_fn
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def apply_piecewise_monotonic_fn(self, wrapper, fn, boundaries, *args):
valid_values = []
for a in [self] + list(args):
vs = []
vs.append(a.lower)
vs.append(a.upper)
for b in boundaries:
vs.append(
tf.maximum(a.lower, tf.minimum(a.upper, b * tf.ones_like(a.lower))))
valid_values.append(vs)
outputs = []
for inputs in itertools.product(*valid_values):
outputs.append(fn(*inputs))
outputs = tf.stack(outputs, axis=-1)
return IntervalBounds(tf.reduce_min(outputs, axis=-1),
tf.reduce_max(outputs, axis=-1))
示例13: stack_intra_task_episodes
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def stack_intra_task_episodes(
in_tensors,
num_samples_per_task,
):
"""Stacks together tensors from different episodes of the same task.
Args:
in_tensors: The input tensors, stored with key names of the form
"<name>/i", where i is an int in [0, (num_samples_per_task - 1)].
num_samples_per_task: Number of episodes in the task.
Returns:
A structure of tensors that matches out_tensor_spec.
"""
out_tensors = TSpecStructure()
# Strip the "/i" postfix from all keys, then get the set of unique keys.
key_set = set(['/'.join(key.split('/')[:-1]) for key in in_tensors.keys()])
for key in key_set:
data = []
for i in range(num_samples_per_task):
data.append(in_tensors['{:s}/{:d}'.format(key, i)])
out_tensors[key] = tf.stack(data, axis=1)
return out_tensors
示例14: ApplyPhotometricImageDistortionsCheap
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def ApplyPhotometricImageDistortionsCheap(
images):
"""Apply photometric distortions to the input images.
Args:
images: Tensor of shape [batch_size, h, w, 3] containing a batch of images
to apply the random photometric distortions to. Assumed to be normalized
to range (0, 1), float32 encoding.
Returns:
images: Tensor of shape [batch_size, h, w, 3] containing a batch of images
resulting from applying random photometric distortions to the inputs.
"""
with tf.name_scope('photometric_distortion'):
channels = tf.unstack(images, axis=-1)
# Per-channel random gamma correction.
# Lower gamma = brighter image, decreased contrast.
# Higher gamma = dark image, increased contrast.
gamma_corrected = [c**tf.random_uniform([], 0.5, 1.5) for c in channels]
images = tf.stack(gamma_corrected, axis=-1)
return images
示例15: _decode_and_center_crop
# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import stack [as 别名]
def _decode_and_center_crop(image_bytes, image_size):
"""Crops to center of image with padding then scales image_size."""
shape = tf.image.extract_jpeg_shape(image_bytes)
image_height = shape[0]
image_width = shape[1]
padded_center_crop_size = tf.cast(
((image_size / (image_size + CROP_PADDING)) *
tf.cast(tf.minimum(image_height, image_width), tf.float32)),
tf.int32)
offset_height = ((image_height - padded_center_crop_size) + 1) // 2
offset_width = ((image_width - padded_center_crop_size) + 1) // 2
crop_window = tf.stack([offset_height, offset_width,
padded_center_crop_size, padded_center_crop_size])
image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
image = tf.image.resize_bicubic([image], [image_size, image_size])[0]
return image