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Python v1.bool方法代碼示例

本文整理匯總了Python中tensorflow.compat.v1.bool方法的典型用法代碼示例。如果您正苦於以下問題:Python v1.bool方法的具體用法?Python v1.bool怎麽用?Python v1.bool使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在tensorflow.compat.v1的用法示例。


在下文中一共展示了v1.bool方法的15個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: _rollout_metadata

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def _rollout_metadata(batch_env, distributional_size=1):
  """Metadata for rollouts."""
  batch_env_shape = batch_env.observ.get_shape().as_list()
  batch_size = [batch_env_shape[0]]
  value_size = batch_size
  if distributional_size > 1:
    value_size = batch_size + [distributional_size]
  shapes_types_names = [
      # TODO(piotrmilos): possibly retrieve the observation type for batch_env
      (batch_size + batch_env_shape[1:], batch_env.observ_dtype, "observation"),
      (batch_size, tf.float32, "reward"),
      (batch_size, tf.bool, "done"),
      (batch_size + list(batch_env.action_shape), batch_env.action_dtype,
       "action"),
      (batch_size, tf.float32, "pdf"),
      (value_size, tf.float32, "value_function"),
  ]
  return shapes_types_names 
開發者ID:tensorflow,項目名稱:tensor2tensor,代碼行數:20,代碼來源:ppo_learner.py

示例2: set_recall

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def set_recall(predictions, labels, weights_fn=common_layers.weights_nonzero):
  """Recall of set predictions.

  Args:
    predictions : A Tensor of scores of shape [batch, nlabels].
    labels: A Tensor of int32s giving true set elements,
      of shape [batch, seq_length].
    weights_fn: A function to weight the elements.

  Returns:
    hits: A Tensor of shape [batch, nlabels].
    weights: A Tensor of shape [batch, nlabels].
  """
  with tf.variable_scope("set_recall", values=[predictions, labels]):
    labels = tf.squeeze(labels, [2, 3])
    weights = weights_fn(labels)
    labels = tf.one_hot(labels, predictions.shape[-1])
    labels = tf.reduce_max(labels, axis=1)
    labels = tf.cast(labels, tf.bool)
    return tf.to_float(tf.equal(labels, predictions)), weights 
開發者ID:tensorflow,項目名稱:tensor2tensor,代碼行數:22,代碼來源:metrics.py

示例3: pad_batch

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def pad_batch(features, batch_multiple):
  """Pad batch dim of features to nearest multiple of batch_multiple."""
  feature = list(features.items())[0][1]
  batch_size = tf.shape(feature)[0]
  mod = batch_size % batch_multiple
  has_mod = tf.cast(tf.cast(mod, tf.bool), tf.int32)
  batch_padding = batch_multiple * has_mod - mod

  padded_features = {}
  for k, feature in features.items():
    rank = len(feature.shape)
    paddings = [[0, 0] for _ in range(rank)]
    paddings[0][1] = batch_padding
    padded_feature = tf.pad(feature, paddings)
    padded_features[k] = padded_feature
  return padded_features


# TODO(lukaszkaiser): refactor the API to not be just a list of self params
#   but make sense for other uses too. 
開發者ID:tensorflow,項目名稱:tensor2tensor,代碼行數:22,代碼來源:data_reader.py

示例4: revnet

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def revnet(name, x, hparams, reverse=True):
  """'hparams.depth' steps of generative flow.

  Args:
    name: variable scope for the revnet block.
    x: 4-D Tensor, shape=(NHWC).
    hparams: HParams.
    reverse: bool, forward or backward pass.
  Returns:
    x: 4-D Tensor, shape=(NHWC).
    objective: float.
  """
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    steps = np.arange(hparams.depth)
    if reverse:
      steps = steps[::-1]

    objective = 0.0
    for step in steps:
      x, curr_obj = revnet_step(
          "revnet_step_%d" % step, x, hparams, reverse=reverse)
      objective += curr_obj
    return x, objective 
開發者ID:tensorflow,項目名稱:tensor2tensor,代碼行數:25,代碼來源:glow_ops.py

示例5: lengths_to_area_mask

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def lengths_to_area_mask(feature_length, length, max_area_size):
  """Generates a non-padding mask for areas based on lengths.

  Args:
    feature_length: a tensor of [batch_size]
    length: the length of the batch
    max_area_size: the maximum area size considered
  Returns:
    mask: a tensor in shape of [batch_size, num_areas]
  """

  paddings = tf.cast(tf.expand_dims(
      tf.logical_not(
          tf.sequence_mask(feature_length, maxlen=length)), 2), tf.float32)
  _, _, area_sum, _, _ = compute_area_features(paddings,
                                               max_area_width=max_area_size)
  mask = tf.squeeze(tf.logical_not(tf.cast(area_sum, tf.bool)), [2])
  return mask 
開發者ID:tensorflow,項目名稱:tensor2tensor,代碼行數:20,代碼來源:area_attention.py

示例6: __init__

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def __init__(self, regularizers_to_group):
    """Creates an instance.

    Args:
      regularizers_to_group: A list of generic_regularizers.OpRegularizer
        objects.Their regularization_vector (alive_vector) are expected to be of
        the same length.

    Raises:
      ValueError: regularizers_to_group is not of length at least 2.
    """
    if len(regularizers_to_group) < 2:
      raise ValueError('Groups must be of at least size 2.')
    self._regularization_vector = tf.add_n(
        [r.regularization_vector for r in regularizers_to_group])
    self._alive_vector = tf.cast(
        tf.ones(self._regularization_vector.get_shape()[-1]), tf.bool) 
開發者ID:google-research,項目名稱:morph-net,代碼行數:19,代碼來源:op_regularizer_manager_test.py

示例7: _flat_reconstruction_loss

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def _flat_reconstruction_loss(self, flat_x_target, flat_rnn_output):
    b_enc, b_dec = tf.split(
        flat_rnn_output,
        [self._nade.num_hidden, self._output_depth], axis=1)
    ll, cond_probs = self._nade.log_prob(
        flat_x_target, b_enc=b_enc, b_dec=b_dec)
    r_loss = -ll
    flat_truth = tf.cast(flat_x_target, tf.bool)
    flat_predictions = tf.greater_equal(cond_probs, 0.5)

    metric_map = {
        'metrics/accuracy':
            tf.metrics.mean(
                tf.reduce_all(tf.equal(flat_truth, flat_predictions), axis=-1)),
        'metrics/recall':
            tf.metrics.recall(flat_truth, flat_predictions),
        'metrics/precision':
            tf.metrics.precision(flat_truth, flat_predictions),
    }

    return r_loss, metric_map 
開發者ID:magenta,項目名稱:magenta,代碼行數:23,代碼來源:lstm_models.py

示例8: get_placeholders

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def get_placeholders(self):
    hparams = self.hparams
    return dict(
        pianorolls=tf.placeholder(
            tf.bool,
            [None, None, hparams.num_pitches, hparams.num_instruments],
            "pianorolls"),
        # The default value is only used for checking if completion masker
        # should be evoked.  It can't be used directly as the batch size
        # and length of pianorolls are unknown during static time.
        outer_masks=tf.placeholder_with_default(
            np.zeros(
                (1, 1, hparams.num_pitches, hparams.num_instruments),
                dtype=np.float32),
            [None, None, hparams.num_pitches, hparams.num_instruments],
            "outer_masks"),
        sample_steps=tf.placeholder_with_default(0, (), "sample_steps"),
        total_gibbs_steps=tf.placeholder_with_default(
            0, (), "total_gibbs_steps"),
        current_step=tf.placeholder_with_default(0, (), "current_step"),
        temperature=tf.placeholder_with_default(0.99, (), "temperature")) 
開發者ID:magenta,項目名稱:magenta,代碼行數:23,代碼來源:lib_tfsampling.py

示例9: get_signature_def

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def get_signature_def(model, use_tf_sampling):
  """Creates a signature def for the SavedModel."""
  if use_tf_sampling:
    return tf.saved_model.signature_def_utils.predict_signature_def(
        inputs={
            'pianorolls': model.inputs['pianorolls'],
        }, outputs={
            'predictions': tf.cast(model.samples, tf.bool),
        })
  return tf.saved_model.signature_def_utils.predict_signature_def(
      inputs={
          'pianorolls': model.model.pianorolls,
          'masks': model.model.masks,
          'lengths': model.model.lengths,
      }, outputs={
          'predictions': model.model.predictions
      }) 
開發者ID:magenta,項目名稱:magenta,代碼行數:19,代碼來源:lib_saved_model.py

示例10: tracks_own_finished

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def tracks_own_finished(self):
    """Describes whether the Decoder keeps track of finished states.

    Most decoders will emit a true/false `finished` value independently
    at each time step.  In this case, the `dynamic_decode` function keeps track
    of which batch entries are already finished, and performs a logical OR to
    insert new batches to the finished set.

    Some decoders, however, shuffle batches / beams between time steps and
    `dynamic_decode` will mix up the finished state across these entries because
    it does not track the reshuffle across time steps.  In this case, it is
    up to the decoder to declare that it will keep track of its own finished
    state by setting this property to `True`.

    Returns:
      Python bool.
    """
    return False

  # TODO(scottzhu): Add build/get_config/from_config and other layer methods. 
開發者ID:magenta,項目名稱:magenta,代碼行數:22,代碼來源:seq2seq.py

示例11: __init__

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def __init__(self, sample_fn, sample_shape, sample_dtype,
               start_inputs, end_fn, next_inputs_fn=None):
    """Initializer.

    Args:
      sample_fn: A callable that takes `outputs` and emits tensor `sample_ids`.
      sample_shape: Either a list of integers, or a 1-D Tensor of type `int32`,
        the shape of the each sample in the batch returned by `sample_fn`.
      sample_dtype: the dtype of the sample returned by `sample_fn`.
      start_inputs: The initial batch of inputs.
      end_fn: A callable that takes `sample_ids` and emits a `bool` vector
        shaped `[batch_size]` indicating whether each sample is an end token.
      next_inputs_fn: (Optional) A callable that takes `sample_ids` and returns
        the next batch of inputs. If not provided, `sample_ids` is used as the
        next batch of inputs.
    """
    self._sample_fn = sample_fn
    self._end_fn = end_fn
    self._sample_shape = tf.TensorShape(sample_shape)
    self._sample_dtype = sample_dtype
    self._next_inputs_fn = next_inputs_fn
    self._batch_size = tf.shape(start_inputs)[0]
    self._start_inputs = tf.convert_to_tensor(
        start_inputs, name="start_inputs") 
開發者ID:magenta,項目名稱:magenta,代碼行數:26,代碼來源:seq2seq.py

示例12: _build

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def _build(self, inputs, labels):

    def cond(i, unused_attack, success):
      # If we are already successful, we break.
      return tf.logical_and(i < self._num_restarts,
                            tf.logical_not(tf.reduce_all(success)))

    def body(i, attack, success):
      new_attack = self._inner_attack(inputs, labels)
      new_success = self._inner_attack.success
      # The first iteration always sets the attack.
      use_new_values = tf.logical_or(tf.equal(i, 0), new_success)
      return (i + 1,
              tf.where(use_new_values, new_attack, attack),
              tf.logical_or(success, new_success))

    _, self._attack, self._success = tf.while_loop(
        cond, body, back_prop=False, parallel_iterations=1,
        loop_vars=[
            tf.constant(0, dtype=tf.int32),
            inputs,
            tf.zeros([tf.shape(inputs)[0]], dtype=tf.bool),
        ])
    self._logits = self._eval_fn(self._attack, mode='final')
    return self._attack 
開發者ID:deepmind,項目名稱:interval-bound-propagation,代碼行數:27,代碼來源:attacks.py

示例13: sparse_reduce

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def sparse_reduce(sp_tensor, rank, agg_fn="sum", axis=-1):
  """Reduce SparseTensor along the given axis.

  Args:
    sp_tensor: SparseTensor of arbitrary rank.
    rank: Integer rank of the sparse tensor.
    agg_fn: Reduce function for aggregation.
    axis: Integer specifying axis to sum over.

  Returns:
    sp_tensor: SparseTensor of one less rank.
  """
  if axis < 0:
    axis = rank + axis
  axes_to_keep = tf.one_hot(
      axis, rank, on_value=False, off_value=True, dtype=tf.bool)
  indices_to_keep = tf.boolean_mask(sp_tensor.indices, axes_to_keep, axis=1)
  new_shape = tf.boolean_mask(sp_tensor.dense_shape, axes_to_keep)
  indices_to_keep.set_shape([None, rank - 1])
  indices, values = aggregate_sparse_indices(
      indices_to_keep, sp_tensor.values, new_shape, agg_fn=agg_fn)
  return tf.sparse.reorder(tf.SparseTensor(indices, values, new_shape)) 
開發者ID:google-research,項目名稱:language,代碼行數:24,代碼來源:model_fns.py

示例14: batch_boolean_mask

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def batch_boolean_mask(mask):
  """Get indices of true values.

  Args:
    mask: [batch_size, num_values]

  Returns:
    true_indices: [batch_size, max_true]
    gathered_mask: [batch_size, max_true]
  """
  # [batch_size, num_values]
  mask = tf.to_int32(mask)

  # [batch_size]
  num_true = tf.reduce_sum(mask, 1)

  # []
  max_true = tf.reduce_max(num_true)

  # [batch_size, max_true]
  gathered_mask, true_indices = tf.nn.top_k(mask, max_true)
  gathered_mask = tf.cast(gathered_mask, tf.bool)

  return gathered_mask, true_indices 
開發者ID:google-research,項目名稱:language,代碼行數:26,代碼來源:tensor_utils.py

示例15: load_boolq_file

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import bool [as 別名]
def load_boolq_file(filename, num_par=2):
  """Build a tf.data.Data from a file of boolq examples."""
  tokenizer = tokenization.NltkTokenizer()
  examples = []
  with tf.gfile.Open(filename) as f:
    for line in f:
      obj = json.loads(line)
      context = tokenizer.tokenize(obj["passage"])
      if FLAGS.max_passage_len:
        context = context[:FLAGS.max_passage_len]
      question = tokenizer.tokenize(obj["question"])
      examples.append((question, context, obj["answer"]))

  def get_data():
    out = list(examples)
    np.random.shuffle(out)
    return out

  ds = tf.data.Dataset.from_generator(get_data, (tf.string, tf.string, tf.bool),
                                      ([None], [None], []))

  def to_dict(p, h, label):
    return {"hypothesis": p, "premise": h, "label": label}

  return ds.map(to_dict, num_parallel_calls=num_par) 
開發者ID:google-research,項目名稱:language,代碼行數:27,代碼來源:run_recurrent_model_boolq.py


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