Python tensorflow.python.ops.check_ops.assert_type() Examples

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: An optional `Tensor` whose shape is broadcastable to `values`.
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A tensor representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    values = math_ops.mul(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions
      must be either `1`, or the same as the corresponding `values`
      dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    with ops.control_dependencies((_assert_weights_rank(weights, values),)):
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _concatenate_context_input(sequence_input, context_input):
  """Replicates `context_input` across all timesteps of `sequence_input`.

  Expands dimension 1 of `context_input` then tiles it `sequence_length` times.
  This value is appended to `sequence_input` on dimension 2 and the result is
  returned.

  Args:
    sequence_input: A `Tensor` of dtype `float32` and shape `[batch_size,
      padded_length, d0]`.
    context_input: A `Tensor` of dtype `float32` and shape `[batch_size, d1]`.

  Returns:
    A `Tensor` of dtype `float32` and shape `[batch_size, padded_length,
    d0 + d1]`.

  Raises:
    ValueError: If `sequence_input` does not have rank 3 or `context_input` does
      not have rank 2.
  """
  seq_rank_check = check_ops.assert_rank(
      sequence_input,
      3,
      message='sequence_input must have rank 3',
      data=[array_ops.shape(sequence_input)])
  seq_type_check = check_ops.assert_type(
      sequence_input,
      dtypes.float32,
      message='sequence_input must have dtype float32; got {}.'.format(
          sequence_input.dtype))
  ctx_rank_check = check_ops.assert_rank(
      context_input,
      2,
      message='context_input must have rank 2',
      data=[array_ops.shape(context_input)])
  ctx_type_check = check_ops.assert_type(
      context_input,
      dtypes.float32,
      message='context_input must have dtype float32; got {}.'.format(
          context_input.dtype))
  with ops.control_dependencies(
      [seq_rank_check, seq_type_check, ctx_rank_check, ctx_type_check]):
    padded_length = array_ops.shape(sequence_input)[1]
    tiled_context_input = array_ops.tile(
        array_ops.expand_dims(context_input, 1),
        array_ops.concat([[1], [padded_length], [1]], 0))
  return array_ops.concat([sequence_input, tiled_context_input], 2) 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions
      must be either `1`, or the same as the corresponding `values`
      dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    with ops.control_dependencies((_assert_weights_rank(weights, values),)):
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _concatenate_context_input(sequence_input, context_input):
  """Replicates `context_input` accross all timesteps of `sequence_input`.

  Expands dimension 1 of `context_input` then tiles it `sequence_length` times.
  This value is appended to `sequence_input` on dimension 2 and the result is
  returned.

  Args:
    sequence_input: A `Tensor` of dtype `float32` and shape `[batch_size,
      padded_length, d0]`.
    context_input: A `Tensor` of dtype `float32` and shape `[batch_size, d1]`.

  Returns:
    A `Tensor` of dtype `float32` and shape `[batch_size, padded_length,
    d0 + d1]`.

  Raises:
    ValueError: If `sequence_input` does not have rank 3 or `context_input` does
      not have rank 2.
  """
  seq_rank_check = check_ops.assert_rank(
      sequence_input,
      3,
      message='sequence_input must have rank 3',
      data=[array_ops.shape(sequence_input)])
  seq_type_check = check_ops.assert_type(
      sequence_input,
      dtypes.float32,
      message='sequence_input must have dtype float32; got {}.'.format(
          sequence_input.dtype))
  ctx_rank_check = check_ops.assert_rank(
      context_input,
      2,
      message='context_input must have rank 2',
      data=[array_ops.shape(context_input)])
  ctx_type_check = check_ops.assert_type(
      context_input,
      dtypes.float32,
      message='context_input must have dtype float32; got {}.'.format(
          context_input.dtype))
  with ops.control_dependencies(
      [seq_rank_check, seq_type_check, ctx_rank_check, ctx_type_check]):
    padded_length = array_ops.shape(sequence_input)[1]
    tiled_context_input = array_ops.tile(
        array_ops.expand_dims(context_input, 1),
        array_ops.concat([[1], [padded_length], [1]], 0))
  return array_ops.concat([sequence_input, tiled_context_input], 2) 

def _concatenate_context_input(sequence_input, context_input):
  """Replicates `context_input` accross all timesteps of `sequence_input`.

  Expands dimension 1 of `context_input` then tiles it `sequence_length` times.
  This value is appended to `sequence_input` on dimension 2 and the result is
  returned.

  Args:
    sequence_input: a `Tensor` of dtype `float32` and shape `[batch_size,
      padded_length, d0]`.
    context_input: a `Tensor` of dtype `float32` and shape `[batch_size, d1]`.

  Returns:
    A `Tensor` of dtype `float32` and shape `[batch_size, padded_length,
    d0 + d1]`.

  Raises:
    ValueError: if `sequence_input` does not have rank 3 or `context_input` does
      not have rank 2.
  """
  seq_rank_check = check_ops.assert_rank(
      sequence_input,
      3,
      message='sequence_input must have rank 3',
      data=[array_ops.shape(sequence_input)])
  seq_type_check = check_ops.assert_type(
      sequence_input,
      dtypes.float32,
      message='sequence_input must have dtype float32; got {}.'.format(
          sequence_input.dtype))
  ctx_rank_check = check_ops.assert_rank(
      context_input,
      2,
      message='context_input must have rank 2',
      data=[array_ops.shape(context_input)])
  ctx_type_check = check_ops.assert_type(
      context_input,
      dtypes.float32,
      message='context_input must have dtype float32; got {}.'.format(
          context_input.dtype))
  with ops.control_dependencies(
      [seq_rank_check, seq_type_check, ctx_rank_check, ctx_type_check]):
    padded_length = array_ops.shape(sequence_input)[1]
    tiled_context_input = array_ops.tile(
        array_ops.expand_dims(context_input, 1),
        array_ops.concat(0, [[1], [padded_length], [1]]))
  return array_ops.concat(2, [sequence_input, tiled_context_input]) 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `values` dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    with ops.control_dependencies((
        check_ops.assert_rank_in(weights, (0, array_ops.rank(values))),)):
      weights = math_ops.to_float(weights)
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _count_condition(values, weights=None, metrics_collections=None,
                     updates_collections=None):
  """Sums the weights of cases where the given values are True.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    values: A `bool` `Tensor` of arbitrary size.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `values`, and must be broadcastable to `values` (i.e., all dimensions
      must be either `1`, or the same as the corresponding `values`
      dimension).
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.

  Returns:
    value_tensor: A `Tensor` representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match `values`,
      or if either `metrics_collections` or `updates_collections` are not a list
      or tuple.
  """
  check_ops.assert_type(values, dtypes.bool)
  count = _create_local('count', shape=[])

  values = math_ops.to_float(values)
  if weights is not None:
    weights = math_ops.to_float(weights)
    with ops.control_dependencies((_assert_weights_rank(weights, values),)):
      values = math_ops.multiply(values, weights)

  value_tensor = array_ops.identity(count)
  update_op = state_ops.assign_add(count, math_ops.reduce_sum(values))

  if metrics_collections:
    ops.add_to_collections(metrics_collections, value_tensor)

  if updates_collections:
    ops.add_to_collections(updates_collections, update_op)

  return value_tensor, update_op 

def _concatenate_context_input(sequence_input, context_input):
  """Replicates `context_input` accross all timesteps of `sequence_input`.

  Expands dimension 1 of `context_input` then tiles it `sequence_length` times.
  This value is appended to `sequence_input` on dimension 2 and the result is
  returned.

  Args:
    sequence_input: A `Tensor` of dtype `float32` and shape `[batch_size,
      padded_length, d0]`.
    context_input: A `Tensor` of dtype `float32` and shape `[batch_size, d1]`.

  Returns:
    A `Tensor` of dtype `float32` and shape `[batch_size, padded_length,
    d0 + d1]`.

  Raises:
    ValueError: If `sequence_input` does not have rank 3 or `context_input` does
      not have rank 2.
  """
  seq_rank_check = check_ops.assert_rank(
      sequence_input,
      3,
      message='sequence_input must have rank 3',
      data=[array_ops.shape(sequence_input)])
  seq_type_check = check_ops.assert_type(
      sequence_input,
      dtypes.float32,
      message='sequence_input must have dtype float32; got {}.'.format(
          sequence_input.dtype))
  ctx_rank_check = check_ops.assert_rank(
      context_input,
      2,
      message='context_input must have rank 2',
      data=[array_ops.shape(context_input)])
  ctx_type_check = check_ops.assert_type(
      context_input,
      dtypes.float32,
      message='context_input must have dtype float32; got {}.'.format(
          context_input.dtype))
  with ops.control_dependencies(
      [seq_rank_check, seq_type_check, ctx_rank_check, ctx_type_check]):
    padded_length = array_ops.shape(sequence_input)[1]
    tiled_context_input = array_ops.tile(
        array_ops.expand_dims(context_input, 1),
        array_ops.concat([[1], [padded_length], [1]], 0))
  return array_ops.concat([sequence_input, tiled_context_input], 2)