Python tensorflow.size方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def build_cross_entropy_loss(logits, gold):
  """Constructs a cross entropy from logits and one-hot encoded gold labels.

  Supports skipping rows where the gold label is the magic -1 value.

  Args:
    logits: float Tensor of scores.
    gold: int Tensor of one-hot labels.

  Returns:
    cost, correct, total: the total cost, the total number of correctly
        predicted labels, and the total number of valid labels.
  """
  valid = tf.reshape(tf.where(tf.greater(gold, -1)), [-1])
  gold = tf.gather(gold, valid)
  logits = tf.gather(logits, valid)
  correct = tf.reduce_sum(tf.to_int32(tf.nn.in_top_k(logits, gold, 1)))
  total = tf.size(gold)
  cost = tf.reduce_sum(
      tf.contrib.nn.deprecated_flipped_sparse_softmax_cross_entropy_with_logits(
          logits, tf.cast(gold, tf.int64))) / tf.cast(total, tf.float32)
  return cost, correct, total 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def update_tensor_arrays(network_tensors, arrays):
  """Updates a list of tensor arrays from the network's output tensors.

  Arguments:
    network_tensors: Output tensors from the underlying NN unit.
    arrays: TensorArrays to be updated.

  Returns:
    New list of TensorArrays after writing activations.
  """
  # TODO(googleuser): Only store activations that will be used later in linked
  # feature specifications.
  next_arrays = []
  for index, network_tensor in enumerate(network_tensors):
    array = arrays[index]
    size = array.size()
    array = array.write(size, network_tensor)
    next_arrays.append(array)
  return next_arrays 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def expanded_shape(orig_shape, start_dim, num_dims):
  """Inserts multiple ones into a shape vector.

  Inserts an all-1 vector of length num_dims at position start_dim into a shape.
  Can be combined with tf.reshape to generalize tf.expand_dims.

  Args:
    orig_shape: the shape into which the all-1 vector is added (int32 vector)
    start_dim: insertion position (int scalar)
    num_dims: length of the inserted all-1 vector (int scalar)
  Returns:
    An int32 vector of length tf.size(orig_shape) + num_dims.
  """
  with tf.name_scope('ExpandedShape'):
    start_dim = tf.expand_dims(start_dim, 0)  # scalar to rank-1
    before = tf.slice(orig_shape, [0], start_dim)
    add_shape = tf.ones(tf.reshape(num_dims, [1]), dtype=tf.int32)
    after = tf.slice(orig_shape, start_dim, [-1])
    new_shape = tf.concat([before, add_shape, after], 0)
    return new_shape 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def _padded_batched_proposals_indicator(self,
                                          num_proposals,
                                          max_num_proposals):
    """Creates indicator matrix of non-pad elements of padded batch proposals.

    Args:
      num_proposals: Tensor of type tf.int32 with shape [batch_size].
      max_num_proposals: Maximum number of proposals per image (integer).

    Returns:
      A Tensor of type tf.bool with shape [batch_size, max_num_proposals].
    """
    batch_size = tf.size(num_proposals)
    tiled_num_proposals = tf.tile(
        tf.expand_dims(num_proposals, 1), [1, max_num_proposals])
    tiled_proposal_index = tf.tile(
        tf.expand_dims(tf.range(max_num_proposals), 0), [batch_size, 1])
    return tf.greater(tiled_num_proposals, tiled_proposal_index) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def _grad_sparsity(self):
    """Gradient sparsity."""
    # If the sparse minibatch gradient has 10 percent of its entries
    # non-zero, its sparsity is 0.1.
    # The norm of dense gradient averaged from full dataset
    # are roughly estimated norm of minibatch
    # sparse gradient norm * sqrt(sparsity)
    # An extension maybe only correct the sparse blob.
    non_zero_cnt = tf.add_n([tf.count_nonzero(g) for g in self._grad])
    all_entry_cnt = tf.add_n([tf.size(g) for g in self._grad])
    self._sparsity = tf.cast(non_zero_cnt, self._grad[0].dtype)
    self._sparsity /= tf.cast(all_entry_cnt, self._grad[0].dtype)
    avg_op = self._moving_averager.apply([self._sparsity,])
    with tf.control_dependencies([avg_op]):
      self._sparsity_avg = self._moving_averager.average(self._sparsity)
    return avg_op 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def add_positional_embedding(x, max_length, name, positions=None):
  """Add positional embedding.

  Args:
    x: a Tensor with shape [batch, length, depth]
    max_length: an integer.  static maximum size of any dimension.
    name: a name for this layer.
    positions: an optional tensor with shape [batch, length]

  Returns:
    a Tensor the same shape as x.
  """
  _, length, depth = common_layers.shape_list(x)
  var = tf.cast(tf.get_variable(name, [max_length, depth]), x.dtype)
  if positions is None:
    sliced = tf.cond(
        tf.less(length, max_length),
        lambda: tf.slice(var, [0, 0], [length, -1]),
        lambda: tf.pad(var, [[0, length - max_length], [0, 0]]))
    return x + tf.expand_dims(sliced, 0)
  else:
    return x + tf.gather(var, tf.to_int32(positions)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def coordinate_tensor(shape, axis):
  """Return a tensor with given shape containing coordinate along given axis.

  Args:
    shape: a Tensor representing the shape of the output Tensor
    axis: an integer

  Returns:
    A tensor with shape shape and type tf.int32, where each elements its
    coordinate along the given axis.
  """
  if axis < 0:
    axis = tf.size(shape) + axis  # Convert to positive for the one_hot indice

  r = tf.range(shape[axis])
  r_shape = tf.one_hot(
      axis, tf.size(shape), on_value=-1, off_value=1, dtype=tf.int32)
  return tf.zeros(shape, dtype=tf.int32) + tf.reshape(r, r_shape) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def select_dim_value(x, indices, name=None):
    with tf.name_scope(name, "select-dim-value", values=[x, indices]):
        # x.shape = (rest..., dims)
        rest = tf.shape(x)[:-1]
        dims = tf.shape(x)[-1]
        size = tf.size(indices, out_type=indices.dtype)

        # reshape to (size, dims)
        t = tf.reshape(x, shape=[-1, dims])
        # then index as ([1,2,3,...,size], indices.ravel())
        nd_indices = tf.stack([
            tf.range(0, size, dtype=indices.dtype),
            tf.reshape(indices, shape=[-1])
        ], axis=1)
        t = tf.gather_nd(t, indices=nd_indices)

        # reshape back to (rest...)
        t = tf.reshape(t, rest)
        t.set_shape(x.get_shape()[:-1])
        return t 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def fixed_padding(inputs, kernel_size, rate=1):
  """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, height_in, width_in, channels].
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                 Should be a positive integer.
    rate: An integer, rate for atrous convolution.

  Returns:
    output: A tensor of size [batch, height_out, width_out, channels] with the
      input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
  pad_total = kernel_size_effective - 1
  pad_beg = pad_total // 2
  pad_end = pad_total - pad_beg
  padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
                                  [pad_beg, pad_end], [0, 0]])
  return padded_inputs 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def __init__(self,
               is_training,
               first_stage_features_stride,
               batch_norm_trainable=False,
               reuse_weights=None,
               weight_decay=0.0):
    """Constructor.

    Args:
      is_training: A boolean indicating whether the training version of the
        computation graph should be constructed.
      first_stage_features_stride: Output stride of extracted RPN feature map.
      batch_norm_trainable: Whether to update batch norm parameters during
        training or not. When training with a relative large batch size
        (e.g. 8), it could be desirable to enable batch norm update.
      reuse_weights: Whether to reuse variables. Default is None.
      weight_decay: float weight decay for feature extractor (default: 0.0).
    """
    self._is_training = is_training
    self._first_stage_features_stride = first_stage_features_stride
    self._train_batch_norm = (batch_norm_trainable and is_training)
    self._reuse_weights = reuse_weights
    self._weight_decay = weight_decay 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def get_anchors(self, image_shape):
        """Returns anchor pyramid for the given image size."""
        backbone_shapes = compute_backbone_shapes(self.config, image_shape)
        # Cache anchors and reuse if image shape is the same
        if not hasattr(self, "_anchor_cache"):
            self._anchor_cache = {}
        if not tuple(image_shape) in self._anchor_cache:
            # Generate Anchors
            a = utils.generate_pyramid_anchors(
                self.config.RPN_ANCHOR_SCALES,
                self.config.RPN_ANCHOR_RATIOS,
                backbone_shapes,
                self.config.BACKBONE_STRIDES,
                self.config.RPN_ANCHOR_STRIDE)
            # Keep a copy of the latest anchors in pixel coordinates because
            # it's used in inspect_model notebooks.
            # TODO: Remove this after the notebook are refactored to not use it
            self.anchors = a
            # Normalize coordinates
            self._anchor_cache[tuple(image_shape)] = utils.norm_boxes(a, image_shape[:2])
        return self._anchor_cache[tuple(image_shape)] 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def compose_image_meta(image_id, original_image_shape, image_shape,
                       window, scale, active_class_ids):
    """Takes attributes of an image and puts them in one 1D array.
    image_id: An int ID of the image. Useful for debugging.
    original_image_shape: [H, W, C] before resizing or padding.
    image_shape: [H, W, C] after resizing and padding
    window: (y1, x1, y2, x2) in pixels. The area of the image where the real
            image is (excluding the padding)
    scale: The scaling factor applied to the original image (float32)
    active_class_ids: List of class_ids available in the dataset from which
        the image came. Useful if training on images from multiple datasets
        where not all classes are present in all datasets.
    """
    meta = np.array(
        [image_id] +                  # size=1
        list(original_image_shape) +  # size=3
        list(image_shape) +           # size=3
        list(window) +                # size=4 (y1, x1, y2, x2) in image cooredinates
        [scale] +                     # size=1
        list(active_class_ids)        # size=num_classes
    )
    return meta 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def fetch_differentiable_fixed_embeddings(comp, state, stride):
  """Looks up fixed features with separate, differentiable, embedding lookup.

  Args:
    comp: Component whose fixed features we wish to look up.
    state: live MasterState object for the component.
    stride: Tensor containing current batch * beam size.

  Returns:
    state handle: updated state handle to be used after this call
    fixed_embeddings: list of NamedTensor objects
  """
  _validate_embedded_fixed_features(comp)
  num_channels = len(comp.spec.fixed_feature)
  if not num_channels:
    return state.handle, []

  state.handle, indices, ids, weights, num_steps = (
      dragnn_ops.bulk_fixed_features(
          state.handle, component=comp.name, num_channels=num_channels))
  fixed_embeddings = []
  for channel, feature_spec in enumerate(comp.spec.fixed_feature):
    differentiable_or_constant = ('constant' if feature_spec.is_constant else
                                  'differentiable')
    tf.logging.info('[%s] Adding %s fixed feature "%s"', comp.name,
                    differentiable_or_constant, feature_spec.name)
    size = stride * num_steps * feature_spec.size
    fixed_embedding = network_units.embedding_lookup(
        comp.get_variable(network_units.fixed_embeddings_name(channel)),
        indices[channel], ids[channel], weights[channel], size)
    if feature_spec.is_constant:
      fixed_embedding = tf.stop_gradient(fixed_embedding)
    fixed_embeddings.append(
        network_units.NamedTensor(fixed_embedding, feature_spec.name))

  return state.handle, fixed_embeddings 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def extract_fixed_feature_ids(comp, state, stride):
  """Extracts fixed feature IDs.

  Args:
    comp: Component whose fixed feature IDs we wish to extract.
    state: Live MasterState object for the component.
    stride: Tensor containing current batch * beam size.

  Returns:
    state handle: Updated state handle to be used after this call.
    ids: List of [stride * num_steps, 1] feature IDs per channel.  Missing IDs
         (e.g., due to batch padding) are set to -1.
  """
  num_channels = len(comp.spec.fixed_feature)
  if not num_channels:
    return state.handle, []

  for feature_spec in comp.spec.fixed_feature:
    check.Eq(feature_spec.size, 1, 'All features must have size=1')
    check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')

  state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
      state.handle, component=comp.name, num_channels=num_channels)
  size = stride * num_steps

  fixed_ids = []
  for channel, feature_spec in enumerate(comp.spec.fixed_feature):
    tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
                    feature_spec.name)

    # The +1 and -1 increments ensure that missing IDs default to -1.
    #
    # TODO(googleuser): This formula breaks if multiple IDs are extracted at some
    # step.  Try using tf.unique() to enforce the unique-IDS precondition.
    sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
    sums = tf.expand_dims(sums, axis=1)
    fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
  return state.handle, fixed_ids 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import size [as 别名]
def embedding_size(self):
    size = 0
    for i in range(self._feature_size):
      size += self._num_features[i] * self._embedding_sizes[i]
    return size