Python v1.reduce_max方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def mask_from_lengths(lengths, max_length=None, dtype=None, name=None):
  """Convert a length scalar to a vector of binary masks.

  This function will convert a vector of lengths to a matrix of binary masks.
  E.g. [2, 4, 3] will become [[1, 1, 0, 0], [1, 1, 1, 1], [1, 1, 1, 0]]

  Args:
    lengths: a d-dimensional vector of integers corresponding to lengths.
    max_length: an optional (default: None) scalar-like or 0-dimensional tensor
      indicating the maximum length of the masks. If not provided, the maximum
      length will be inferred from the lengths vector.
    dtype: the dtype of the returned mask, if specified. If None, the dtype of
      the lengths will be used.
    name: a name for the operation (optional).

  Returns:
    A d x max_length tensor of binary masks (int32).
  """
  with tf.name_scope(name, 'mask_from_lengths'):
    dtype = lengths.dtype if dtype is None else dtype
    max_length = tf.reduce_max(lengths) if max_length is None else max_length
    indexes = tf.range(max_length, dtype=lengths.dtype)
    mask = tf.less(tf.expand_dims(indexes, 0), tf.expand_dims(lengths, 1))
    cast_mask = tf.cast(mask, dtype)
  return tf.stop_gradient(cast_mask) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def set_precision(predictions, labels,
                  weights_fn=common_layers.weights_nonzero):
  """Precision 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_precision", 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 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [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 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def compute_max_pool_embedding(input_embeddings, input_lengths):
  """Computes max pool embedding.

  Args:
    input_embeddings: <tf.float32>[bs, max_seq_len, emb_dim]
    input_lengths: <tf.int64>[bs, 1]

  Returns:
    max_pool_embedding: <tf.float32>[bs, emb_dim]
  """
  max_seq_len = tf.shape(input_embeddings)[1]
  # <tf.float32>[bs, max_seq_len]
  mask = 1.0 - tf.sequence_mask(input_lengths, max_seq_len, dtype=tf.float32)
  mask = tf.squeeze(mask * (-1e-6), 1)
  mask = tf.expand_dims(mask, 2)
  # <tf.float32>[bs, emb_dim]
  max_pool_embedding = tf.reduce_max(input_embeddings + mask, 1)
  # <tf.float32>[bs, dim]
  return max_pool_embedding 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def top_1_tpu(inputs):
  """find max and argmax over the last dimension.

  Works well on TPU

  Args:
    inputs: A tensor with shape [..., depth]

  Returns:
    values: a Tensor with shape [...]
    indices: a Tensor with shape [...]
  """
  inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
  mask = tf.to_int32(tf.equal(inputs_max, inputs))
  index = tf.range(tf.shape(inputs)[-1]) * mask
  return tf.squeeze(inputs_max, -1), tf.reduce_max(index, axis=-1) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def top_k_softmax(x, k):
  """Calculate softmax(x), select top-k and rescale to sum to 1.

  Args:
    x: Input to softmax over.
    k: Number of top-k to select.

  Returns:
    softmax(x) and maximum item.
  """
  x = tf.nn.softmax(x)
  top_x, _ = tf.nn.top_k(x, k=k + 1)
  min_top = tf.reduce_min(top_x, axis=-1, keep_dims=True)
  x = tf.nn.relu((x - min_top) + 1e-12)
  x /= tf.reduce_sum(x, axis=-1, keep_dims=True)
  return x, tf.reduce_max(top_x, axis=-1) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
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) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
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)) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def _simplex_bounds(mapped_vertices, mapped_centres, r, axis):
  """Calculates naive bounds on the given layer-mapped vertices.

  Args:
    mapped_vertices: Tensor of shape (num_vertices, *output_shape)
      or of shape (batch_size, num_vertices, *output_shape)
      containing the vertices in the layer's output space.
    mapped_centres: Tensor of shape (batch_size, *output_shape)
      containing the layer's nominal outputs.
    r: Scalar in [0, 1) specifying the radius (in vocab space) of the simplex.
    axis: Index of the `num_vertices` dimension of `mapped_vertices`.

  Returns:
    lb_out: Tensor of shape (batch_size, *output_shape) with lower bounds
      on the outputs of the affine layer.
    ub_out: Tensor of shape (batch_size, *output_shape) with upper bounds
      on the outputs of the affine layer.
  """
  # Use the negative of r, instead of the complement of r, as
  # we're shifting the input domain to be centred at the origin.
  lb_out = -r * mapped_centres + r * tf.reduce_min(mapped_vertices, axis=axis)
  ub_out = -r * mapped_centres + r * tf.reduce_max(mapped_vertices, axis=axis)
  return lb_out, ub_out 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def extract_relation_representations(input_layer, input_ids, tokenizer):
  """Extracts relation representation from sentence sequence layer."""
  entity_representations = []
  entity_marker_ids = tokenizer.convert_tokens_to_ids(["[E1]", "[E2]"])
  for entity_marker_id in entity_marker_ids:
    mask = tf.to_float(tf.equal(input_ids, entity_marker_id))
    mask = tf.broadcast_to(tf.expand_dims(mask, -1), tf.shape(input_layer))
    entity_representation = tf.reduce_max(
        mask * input_layer, axis=1, keepdims=True)
    entity_representations.append(entity_representation)

  output_layer = tf.concat(entity_representations, axis=2)
  output_layer = tf.squeeze(output_layer, [1])
  tf.logging.info("entity marker pooling AFTER output shape %s",
                  output_layer.shape)

  return output_layer 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [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 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def assert_box_normalized(boxes, maximum_normalized_coordinate=1.1):
  """Asserts the input box tensor is normalized.

  Args:
    boxes: a tensor of shape [N, 4] where N is the number of boxes.
    maximum_normalized_coordinate: Maximum coordinate value to be considered
      as normalized, default to 1.1.

  Returns:
    a tf.Assert op which fails when the input box tensor is not normalized.

  Raises:
    ValueError: When the input box tensor is not normalized.
  """
  box_minimum = tf.reduce_min(boxes)
  box_maximum = tf.reduce_max(boxes)
  return tf.Assert(
      tf.logical_and(
          tf.less_equal(box_maximum, maximum_normalized_coordinate),
          tf.greater_equal(box_minimum, 0)),
      [boxes]) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def keypoints_to_enclosing_bounding_boxes(keypoints):
  """Creates enclosing bounding boxes from keypoints.

  Args:
    keypoints: a [num_instances, num_keypoints, 2] float32 tensor with keypoints
      in [y, x] format.

  Returns:
    A [num_instances, 4] float32 tensor that tightly covers all the keypoints
    for each instance.
  """
  ymin = tf.math.reduce_min(keypoints[:, :, 0], axis=1)
  xmin = tf.math.reduce_min(keypoints[:, :, 1], axis=1)
  ymax = tf.math.reduce_max(keypoints[:, :, 0], axis=1)
  xmax = tf.math.reduce_max(keypoints[:, :, 1], axis=1)
  return tf.stack([ymin, xmin, ymax, xmax], axis=1) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def one_hot_encoding(labels, num_classes=None):
  """One-hot encodes the multiclass labels.

  Example usage:
    labels = tf.constant([1, 4], dtype=tf.int32)
    one_hot = OneHotEncoding(labels, num_classes=5)
    one_hot.eval()    # evaluates to [0, 1, 0, 0, 1]

  Args:
    labels: A tensor of shape [None] corresponding to the labels.
    num_classes: Number of classes in the dataset.
  Returns:
    onehot_labels: a tensor of shape [num_classes] corresponding to the one hot
      encoding of the labels.
  Raises:
    ValueError: if num_classes is not specified.
  """
  with tf.name_scope('OneHotEncoding', values=[labels]):
    if num_classes is None:
      raise ValueError('num_classes must be specified')

    labels = tf.one_hot(labels, num_classes, 1, 0)
    return tf.reduce_max(labels, 0) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import reduce_max [as 別名]
def compute_lengths(symbols_list, eos_symbol, name=None,
                    dtype=tf.int64):
  """Computes sequence lengths given end-of-sequence symbol.

  Args:
    symbols_list: list of [batch_size] tensors of symbols (e.g. integers).
    eos_symbol: end of sequence symbol (e.g. integer).
    name: name for the name scope of this op.
    dtype: type of symbols, default: tf.int64.

  Returns:
    Tensor [batch_size] of lengths of sequences.
  """
  with tf.name_scope(name, 'compute_lengths'):
    max_len = len(symbols_list)
    eos_symbol_ = tf.constant(eos_symbol, dtype=dtype)
    # Array with max_len-time where we have EOS, 0 otherwise. Maximum of this is
    # the first EOS in that example.
    ends = [tf.constant(max_len - i, dtype=tf.int64)
            * tf.to_int64(tf.equal(s, eos_symbol_))
            for i, s in enumerate(symbols_list)]
    # Lengths of sequences, or max_len for sequences that didn't have EOS.
    # Note: examples that don't have EOS will have max value of 0 and value of
    # max_len+1 in lens_.
    lens_ = max_len + 1 - tf.reduce_max(tf.stack(ends, 1), axis=1)
    # For examples that didn't have EOS decrease max_len+1 to max_len as the
    # length.
    lens = tf.subtract(lens_, tf.to_int64(tf.equal(lens_, max_len + 1)))
    return tf.stop_gradient(tf.reshape(lens, [-1]))