Python tensorflow.python.ops.math_ops.logical_or() Examples

def next_inputs(self, time, sample_ids=None, prev_finished=None):
        if sample_ids is None or self.teacher_rate > 0.:
            finished = tf.greater_equal(time + 1, self.sequence_length)
        else:
            finished = math_ops.logical_or(
                tf.greater_equal(time + 1, self.max_step),
                tf.equal(self.eos_id, sample_ids))

        if self.teacher_rate == 1. or (sample_ids is None):
            next_input_ids = self._input_tas.read(time)
            return finished, self.lookup(next_input_ids)

        if self.teacher_rate > 0.:
            # scheduled
            teacher_rates = tf.less_equal(
                tf.random_uniform(tf.shape(sample_ids), minval=0., maxval=1.),
                self.teacher_rate)
            teacher_rates = tf.to_int32(teacher_rates)

            next_input_ids = (teacher_rates * self._input_tas.read(time)
                              + (1 - teacher_rates) * sample_ids)
        else:
            next_input_ids = sample_ids

        return finished, self.lookup(next_input_ids) 

def is_mask_update_iter(self, global_step, last_update_step):
    """Function for checking if the current step is a mask update step.

    It also creates the drop_fraction op and assigns it to the self object.

    Args:
      global_step: tf.Variable(int), current training step.
      last_update_step: tf.Variable(int), holding the last iteration the mask
        is updated. Used to determine whether current iteration is a mask
        update step.


    Returns:
      bool, whether the current iteration is a mask_update step.
    """
    gs_dtype = global_step.dtype
    self._begin_step = math_ops.cast(self._begin_step, gs_dtype)
    self._end_step = math_ops.cast(self._end_step, gs_dtype)
    self._frequency = math_ops.cast(self._frequency, gs_dtype)
    is_step_within_update_range = math_ops.logical_and(
        math_ops.greater_equal(global_step, self._begin_step),
        math_ops.logical_or(
            math_ops.less_equal(global_step, self._end_step),
            # If _end_step is negative, we never stop updating the mask.
            # In other words we update the mask with given frequency until the
            # training ends.
            math_ops.less(self._end_step, 0)))
    is_update_step = math_ops.less_equal(
        math_ops.add(last_update_step, self._frequency), global_step)
    is_mask_update_iter_op = math_ops.logical_and(
        is_step_within_update_range, is_update_step)
    self.drop_fraction = self.get_drop_fraction(global_step,
                                                is_mask_update_iter_op)
    return is_mask_update_iter_op 

def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
  """Boolean mask for `SparseTensor`s.

  Args:
    sparse_tensor: a `SparseTensor`.
    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
      of `sparse_tensor`.
    name: optional name for this operation.
  Returns:
    A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
    `True`.
  """
  # TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
  with ops.name_scope(name, values=[sparse_tensor, mask]):
    mask = ops.convert_to_tensor(mask)
    mask_rows = array_ops.where(mask)
    first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
                                                      [0, 0], [-1, 1]))

    # Identify indices corresponding to the rows identified by mask_rows.
    sparse_entry_matches = functional_ops.map_fn(
        lambda x: math_ops.equal(first_indices, x),
        mask_rows,
        dtype=dtypes.bool)
    # Combine the rows of index_matches to form a mask for the sparse indices
    # and values.
    to_retain = array_ops.reshape(
        functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])

    return sparse_ops.sparse_retain(sparse_tensor, to_retain) 

def _prob(self, x):
    broadcasted_x = x * array_ops.ones(self.batch_shape())
    return math_ops.select(
        math_ops.is_nan(broadcasted_x),
        broadcasted_x,
        math_ops.select(
            math_ops.logical_or(broadcasted_x < self.a,
                                broadcasted_x > self.b),
            array_ops.zeros_like(broadcasted_x),
            (1. / self.range()) * array_ops.ones_like(broadcasted_x))) 

def next_inputs(self, time, sample_ids, prev_finished):
        finished = math_ops.logical_or(
            tf.greater_equal(time + 1, tf.maximum(self.max_step,
                                                  self.max_sequence_length)),
            tf.equal(self.eos_id, sample_ids))
        next_finished = math_ops.logical_or(finished, prev_finished)
        return next_finished, self.lookup(sample_ids) 

def next_inputs(self, time, sample_ids):
        finished = math_ops.logical_or(
            tf.greater_equal(time + 1, self.max_step),
            tf.equal(self.eos_id, sample_ids))
        return finished, self.lookup(sample_ids) 

def slice_tensor_or_dict(tensor_or_dict, signals):
    """Slice the real Tensors according to padding mask in signals."""

    padding_mask = signals['padding_mask']
    batch_size = array_ops.shape(padding_mask)[0]

    def verify_batch_size(tensor):
      check_batch_size = math_ops.equal(batch_size, tensor.shape[0])
      with ops.control_dependencies([check_batch_size]):
        return array_ops.identity(tensor)

    def slice_single_tensor(tensor):
      rank = len(tensor.shape)
      assert rank > 0
      real_batch_size = batch_size - math_ops.reduce_sum(padding_mask)
      return verify_batch_size(tensor)[0:real_batch_size]

    # As we split the Tensors to all TPU cores and concat them back, it is
    # important to ensure the real data is placed before padded ones, i.e.,
    # order is preserved. By that, the sliced padding mask should have all 0's.
    # If this assertion failed, # the slice logic here would not hold.
    sliced_padding_mask = slice_single_tensor(padding_mask)
    assert_padding_mask = math_ops.equal(
        math_ops.reduce_sum(sliced_padding_mask), 0)

    with ops.control_dependencies([assert_padding_mask]):
      should_stop = _StopSignals.should_stop(
          _StopSignals.as_scalar_stopping_signal(signals))

    is_full_batch = math_ops.equal(math_ops.reduce_sum(padding_mask), 0)

    def slice_fn(tensor):
      # If the current batch is full batch or part of stopping signals, we do
      # not need to slice to save performance.
      return control_flow_ops.cond(
          math_ops.logical_or(should_stop, is_full_batch),
          (lambda: verify_batch_size(tensor)),
          (lambda: slice_single_tensor(tensor)))

    return nest.map_structure(slice_fn, tensor_or_dict) 

def slice_tensor_or_dict(tensor_or_dict, signals):
    """Slice the real Tensors according to padding mask in signals."""

    padding_mask = signals['padding_mask']
    batch_size = array_ops.shape(padding_mask)[0]

    def verify_batch_size(tensor):
      check_batch_size = math_ops.equal(batch_size, tensor.shape[0])
      with ops.control_dependencies([check_batch_size]):
        return array_ops.identity(tensor)

    def slice_single_tensor(tensor):
      rank = len(tensor.shape)
      assert rank > 0
      real_batch_size = batch_size - math_ops.reduce_sum(padding_mask)
      return verify_batch_size(tensor)[0:real_batch_size]

    # As we split the Tensors to all TPU cores and concat them back, it is
    # important to ensure the real data is placed before padded ones, i.e.,
    # order is preserved. By that, the sliced padding mask should have all 0's.
    # If this assertion failed, # the slice logic here would not hold.
    sliced_padding_mask = slice_single_tensor(padding_mask)
    assert_padding_mask = math_ops.equal(
        math_ops.reduce_sum(sliced_padding_mask), 0)

    with ops.control_dependencies([assert_padding_mask]):
      should_stop = _StopSignals.should_stop(
          _StopSignals.as_scalar_stopping_signal(signals))

    is_full_batch = math_ops.equal(math_ops.reduce_sum(padding_mask), 0)

    def slice_fn(tensor):
      # If the current batch is full batch or part of stopping signals, we do
      # not need to slice to save performance.
      return control_flow_ops.cond(
          math_ops.logical_or(should_stop, is_full_batch),
          (lambda: verify_batch_size(tensor)),
          (lambda: slice_single_tensor(tensor)))

    return nest.map_structure(slice_fn, tensor_or_dict) 

def _upper_bound_grad(op, grad):
  """Gradient for `upper_bound` if `gradient == 'identity_if_towards'`.

  Args:
    op: The op for which to calculate a gradient.
    grad: Gradient with respect to the output of the op.

  Returns:
    Gradient with respect to the inputs of the op.
  """
  inputs, bound = op.inputs
  pass_through_if = math_ops.logical_or(inputs <= bound, grad > 0)
  return [math_ops.cast(pass_through_if, grad.dtype) * grad, None] 

def _lower_bound_grad(op, grad):
  """Gradient for `lower_bound` if `gradient == 'identity_if_towards'`.

  Args:
    op: The op for which to calculate a gradient.
    grad: Gradient with respect to the output of the op.

  Returns:
    Gradient with respect to the inputs of the op.
  """
  inputs, bound = op.inputs
  pass_through_if = math_ops.logical_or(inputs >= bound, grad < 0)
  return [math_ops.cast(pass_through_if, grad.dtype) * grad, None] 

def setUp(self):
    super(LogicalBinaryOpsTest, self).setUp()

    self.ops = [
        ('logical_and', operator.and_, math_ops.logical_and, core.logical_and),
        ('logical_or', operator.or_, math_ops.logical_or, core.logical_or),
        ('logical_xor', operator.xor, math_ops.logical_xor, core.logical_xor),
    ]
    self.test_lt_1 = self.original_lt < 10
    self.test_lt_2 = self.original_lt < 5
    self.test_lt_1_broadcast = self.test_lt_1.tensor
    self.test_lt_2_broadcast = self.test_lt_2.tensor
    self.broadcast_axes = self.test_lt_1.axes 

def slice_tensor_or_dict(tensor_or_dict, signals):
    """Slice the real Tensors according to padding mask in signals."""

    padding_mask = signals['padding_mask']
    batch_size = array_ops.shape(padding_mask)[0]

    def verify_batch_size(tensor):
      check_batch_size = math_ops.equal(batch_size, tensor.shape[0])
      with ops.control_dependencies([check_batch_size]):
        return array_ops.identity(tensor)

    def slice_single_tensor(tensor):
      rank = len(tensor.shape)
      assert rank > 0
      real_batch_size = batch_size - math_ops.reduce_sum(padding_mask)
      return verify_batch_size(tensor)[0:real_batch_size]

    # As we split the Tensors to all TPU cores and concat them back, it is
    # important to ensure the real data is placed before padded ones, i.e.,
    # order is preserved. By that, the sliced padding mask should have all 0's.
    # If this assertion failed, # the slice logic here would not hold.
    sliced_padding_mask = slice_single_tensor(padding_mask)
    assert_padding_mask = math_ops.equal(
        math_ops.reduce_sum(sliced_padding_mask), 0)

    with ops.control_dependencies([assert_padding_mask]):
      should_stop = _StopSignals.should_stop(
          _StopSignals.as_scalar_stopping_signal(signals))

    is_full_batch = math_ops.equal(math_ops.reduce_sum(padding_mask), 0)

    def slice_fn(tensor):
      # If the current batch is full batch or part of stopping signals, we do
      # not need to slice to save performance.
      return control_flow_ops.cond(
          math_ops.logical_or(should_stop, is_full_batch),
          (lambda: verify_batch_size(tensor)),
          (lambda: slice_single_tensor(tensor)))

    return nest.map_structure(slice_fn, tensor_or_dict) 

def _test_logical_binary(logical_bin_op, data):

    with tf.Graph().as_default():
        in_data = [array_ops.placeholder(shape=data[0].shape, dtype='bool', name='in_0'),
                   array_ops.placeholder(shape=data[1].shape, dtype='bool', name='in_1')]
        if logical_bin_op == math_ops.logical_not:
            out = math_ops.logical_or(in_data[0], in_data[1], name='out1')
            out = logical_bin_op(out, name='out')
        else:
            out = logical_bin_op(in_data[0], in_data[1], name='out')

        compare_tflite_with_tvm(data, ['in_0:0', 'in_1:0'], in_data, [out]) 

def _test_forward_logical_or(data):
    """ One iteration of logical or """
    return _test_logical_binary(math_ops.logical_or, data) 

def _dynamic_rank_in(actual_rank, given_ranks):
  if len(given_ranks) < 1:
    return ops.convert_to_tensor(False)
  result = math_ops.equal(given_ranks[0], actual_rank)
  for given_rank in given_ranks[1:]:
    result = math_ops.logical_or(
        result, math_ops.equal(given_rank, actual_rank))
  return result 

def _prob(self, x):
    broadcasted_x = x * array_ops.ones(self.batch_shape_tensor())
    return array_ops.where(
        math_ops.is_nan(broadcasted_x),
        broadcasted_x,
        array_ops.where(
            math_ops.logical_or(broadcasted_x < self.low,
                                broadcasted_x >= self.high),
            array_ops.zeros_like(broadcasted_x),
            array_ops.ones_like(broadcasted_x) / self.range())) 

def _transform_feature(self, inputs):
    input_tensor = _to_sparse_input(inputs.get(self.key))

    if not input_tensor.dtype.is_integer:
      raise ValueError(
          'Invalid input, not integer. key: {} dtype: {}'.format(
              self.key, input_tensor.dtype))

    values = math_ops.to_int64(input_tensor.values, name='values')
    num_buckets = math_ops.to_int64(self.num_buckets, name='num_buckets')
    zero = math_ops.to_int64(0, name='zero')
    if self.default_value is None:
      # Fail if values are out-of-range.
      assert_less = check_ops.assert_less(
          values, num_buckets, data=(values, num_buckets),
          name='assert_less_than_num_buckets')
      assert_greater = check_ops.assert_greater_equal(
          values, zero, data=(values,),
          name='assert_greater_or_equal_0')
      with ops.control_dependencies((assert_less, assert_greater)):
        values = array_ops.identity(values)
    else:
      # Assign default for out-of-range values.
      values = array_ops.where(
          math_ops.logical_or(
              values < zero, values >= num_buckets, name='out_of_range'),
          array_ops.fill(
              dims=array_ops.shape(values),
              value=math_ops.to_int64(self.default_value),
              name='default_values'),
          values)

    return sparse_tensor_lib.SparseTensor(
        indices=input_tensor.indices,
        values=values,
        dense_shape=input_tensor.dense_shape) 

def _dynamic_rank_in(actual_rank, given_ranks):
  if len(given_ranks) < 1:
    return ops.convert_to_tensor(False)
  result = math_ops.equal(given_ranks[0], actual_rank)
  for given_rank in given_ranks[1:]:
    result = math_ops.logical_or(
        result, math_ops.equal(given_rank, actual_rank))
  return result 

def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
  """Boolean mask for `SparseTensor`s.

  Args:
    sparse_tensor: a `SparseTensor`.
    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
      of `sparse_tensor`.
    name: optional name for this operation.
  Returns:
    A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
    `True`.
  """
  # TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
  with ops.name_scope(name, values=[sparse_tensor, mask]):
    mask = ops.convert_to_tensor(mask)
    mask_rows = array_ops.where(mask)
    first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
                                                      [0, 0], [-1, 1]))

    # Identify indices corresponding to the rows identified by mask_rows.
    sparse_entry_matches = functional_ops.map_fn(
        lambda x: math_ops.equal(first_indices, x),
        mask_rows,
        dtype=dtypes.bool)
    # Combine the rows of index_matches to form a mask for the sparse indices
    # and values.
    to_retain = array_ops.reshape(
        functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])

    return sparse_ops.sparse_retain(sparse_tensor, to_retain) 

def _prob(self, x):
    broadcasted_x = x * array_ops.ones(self.batch_shape())
    return array_ops.where(
        math_ops.is_nan(broadcasted_x),
        broadcasted_x,
        array_ops.where(
            math_ops.logical_or(broadcasted_x < self.a,
                                broadcasted_x > self.b),
            array_ops.zeros_like(broadcasted_x),
            (1. / self.range()) * array_ops.ones_like(broadcasted_x))) 

def _lower_bound_grad(op, grad):
    """Gradient for `_lower_bound`.

    Args:
      op: the tensorflow op for which to calculate a gradient
      grad: gradient with respect to the output of the op

    Returns:
      gradients with respect to the inputs of the op
    """
    inputs = op.inputs[0]
    bound = op.inputs[1]
    pass_through_if = math_ops.logical_or(inputs >= bound, grad < 0)
    return [math_ops.cast(pass_through_if, grad.dtype) * grad, None] 

def next_inputs(self, time, outputs, state, sample_ids, name=None,
                    reach_max_time=None):
        """Gets the inputs for next step."""
        finished = math_ops.equal(sample_ids, self._end_token)
        all_finished = math_ops.reduce_all(finished)
        if reach_max_time is not None:
            all_finished = tf.logical_or(all_finished, reach_max_time)

        if self._embedding_args_cnt == 1:
            del time, outputs  # unused by next_inputs_fn
            next_inputs = control_flow_ops.cond(
                all_finished,
                # If we're finished, the next_inputs value doesn't matter
                lambda: self._start_inputs,
                lambda: self._embedding_fn(sample_ids))
        elif self._embedding_args_cnt == 2:
            del outputs
            # Prepare the position embedding of the next step
            times = tf.ones(self._batch_size, dtype=tf.int32) * (time+1)
            next_inputs = control_flow_ops.cond(
                all_finished,
                # If we're finished, the next_inputs value doesn't matter
                lambda: self._start_inputs,
                lambda: self._embedding_fn(sample_ids, times))

        return finished, next_inputs, state 

def _lower_bound_grad(op, grad):
    """Gradient for `_lower_bound`.

    Args:
      op: the tensorflow op for which to calculate a gradient
      grad: gradient with respect to the output of the op

    Returns:
      gradients with respect to the inputs of the op
    """
    inputs = op.inputs[0]
    bound = op.inputs[1]
    pass_through_if = math_ops.logical_or(inputs >= bound, grad < 0)
    return [math_ops.cast(pass_through_if, grad.dtype) * grad, None] 

def _dynamic_rank_in(actual_rank, given_ranks):
  if len(given_ranks) < 1:
    return ops.convert_to_tensor(False)
  result = math_ops.equal(given_ranks[0], actual_rank)
  for given_rank in given_ranks[1:]:
    result = math_ops.logical_or(
        result, math_ops.equal(given_rank, actual_rank))
  return result 

def _prob(self, x):
    broadcasted_x = x * array_ops.ones(self.batch_shape_tensor())
    return array_ops.where(
        math_ops.is_nan(broadcasted_x),
        broadcasted_x,
        array_ops.where(
            math_ops.logical_or(broadcasted_x < self.low,
                                broadcasted_x >= self.high),
            array_ops.zeros_like(broadcasted_x),
            array_ops.ones_like(broadcasted_x) / self.range())) 

def _transform_feature(self, inputs):
    input_tensor = _to_sparse_input(inputs.get(self.key))

    if not input_tensor.dtype.is_integer:
      raise ValueError(
          'Invalid input, not integer. key: {} dtype: {}'.format(
              self.key, input_tensor.dtype))

    values = math_ops.to_int64(input_tensor.values, name='values')
    num_buckets = math_ops.to_int64(self.num_buckets, name='num_buckets')
    zero = math_ops.to_int64(0, name='zero')
    if self.default_value is None:
      # Fail if values are out-of-range.
      assert_less = check_ops.assert_less(
          values, num_buckets, data=(values, num_buckets),
          name='assert_less_than_num_buckets')
      assert_greater = check_ops.assert_greater_equal(
          values, zero, data=(values,),
          name='assert_greater_or_equal_0')
      with ops.control_dependencies((assert_less, assert_greater)):
        values = array_ops.identity(values)
    else:
      # Assign default for out-of-range values.
      values = array_ops.where(
          math_ops.logical_or(
              values < zero, values >= num_buckets, name='out_of_range'),
          array_ops.fill(
              dims=array_ops.shape(values),
              value=math_ops.to_int64(self.default_value),
              name='default_values'),
          values)

    return sparse_tensor_lib.SparseTensor(
        indices=input_tensor.indices,
        values=values,
        dense_shape=input_tensor.dense_shape) 

def _decode(self, image_buffer, image_format):
    """Decodes the image buffer.

    Args:
      image_buffer: The tensor representing the encoded image tensor.
      image_format: The image format for the image in `image_buffer`. If image
        format is `raw`, all images are expected to be in this format, otherwise
        this op can decode a mix of `jpg` and `png` formats.

    Returns:
      A tensor that represents decoded image of self._shape, or
      (?, ?, self._channels) if self._shape is not specified.
    """
    def decode_image():
      """Decodes a png or jpg based on the headers."""
      return image_ops.decode_image(image_buffer, self._channels)

    def decode_raw():
      """Decodes a raw image."""
      return parsing_ops.decode_raw(image_buffer, out_type=self._dtype)

    pred_fn_pairs = {
        math_ops.logical_or(
            math_ops.equal(image_format, 'raw'),
            math_ops.equal(image_format, 'RAW')): decode_raw,
    }
    image = control_flow_ops.case(
        pred_fn_pairs, default=decode_image, exclusive=True)

    image.set_shape([None, None, self._channels])
    if self._shape is not None:
      image = array_ops.reshape(image, self._shape)

    return image 

def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
  """Boolean mask for `SparseTensor`s.

  Args:
    sparse_tensor: a `SparseTensor`.
    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
      of `sparse_tensor`.
    name: optional name for this operation.
  Returns:
    A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
    `True`.
  """
  # TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
  with ops.name_scope(name, values=[sparse_tensor, mask]):
    mask = ops.convert_to_tensor(mask)
    mask_rows = array_ops.where(mask)
    first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
                                                      [0, 0], [-1, 1]))

    # Identify indices corresponding to the rows identified by mask_rows.
    sparse_entry_matches = functional_ops.map_fn(
        lambda x: math_ops.equal(first_indices, x),
        mask_rows,
        dtype=dtypes.bool)
    # Combine the rows of index_matches to form a mask for the sparse indices
    # and values.
    to_retain = array_ops.reshape(
        functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])

    return sparse_ops.sparse_retain(sparse_tensor, to_retain) 

def __or__(self, other):
    return logical_or(self, other) 

def _dynamic_rank_in(actual_rank, given_ranks):
  if len(given_ranks) < 1:
    return ops.convert_to_tensor(False)
  result = math_ops.equal(given_ranks[0], actual_rank)
  for given_rank in given_ranks[1:]:
    result = math_ops.logical_or(
        result, math_ops.equal(given_rank, actual_rank))
  return result