def reduce_max(input_tensor, reduction_indices=None, keep_dims=False,
name=None):
"""Computes the maximum of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `reduction_indices`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
If `reduction_indices` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
reduction_indices: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
The reduced tensor.
"""
return gen_math_ops._max(input_tensor, _ReductionDims(input_tensor,
reduction_indices),
keep_dims, name=name)
def sequence_mask_mid(lengths, maxlen=None, dtype=dtypes.bool, name=None):
"""Return a mask tensor representing the first N positions of each row.
Example:
```python
tf.sequence_mask([1, 3, 2], 5) =
[[True, False, False, False, False],
[False,False, False, False, False],
[False, True, False, False, False]]
```
Args:
lengths: 1D integer tensor, all its values < maxlen.
maxlen: scalar integer tensor, maximum length of each row. Default: use
maximum over lengths.
dtype: output type of the resulting tensor.
name: name of the op.
Returns:
A 2D mask tensor, as shown in the example above, cast to specified dtype.
Raises:
ValueError: if the arguments have invalid rank.
"""
with ops.name_scope(name, "SequenceMask", [lengths, maxlen]):
lengths = (np.array(lengths)) / 2
lengths = ops.convert_to_tensor(lengths, dtype=tf.int32)
# lengths = ops.convert_to_tensor(lengths)
if lengths.get_shape().ndims != 1:
raise ValueError("lengths must be 1D for sequence_mask")
if maxlen is None:
maxlen = gen_math_ops._max(lengths, [0])
else:
maxlen = ops.convert_to_tensor(maxlen)
if maxlen.get_shape().ndims != 0:
raise ValueError("maxlen must be scalar for sequence_mask")
# The basic idea is to compare a range row vector of size maxlen:
# [0, 1, 2, 3, 4]
# to length as a matrix with 1 column: [[1], [3], [2]].
# Because of broadcasting on both arguments this comparison results
# in a matrix of size (len(lengths), maxlen)
row_vector = gen_math_ops._range(constant(0, maxlen.dtype), maxlen,
constant(1, maxlen.dtype))
# Since maxlen >= max(lengths), it is safe to use maxlen as a cast
# authoritative type. Whenever maxlen fits into tf.int32, so do the lengths.
matrix_0 = gen_math_ops.cast(expand_dims(lengths, 1), maxlen.dtype)
matrix_1 = gen_math_ops.cast(expand_dims(lengths - 1, 1), maxlen.dtype)
result_0 = (row_vector < matrix_0)
result_1 = (row_vector >= matrix_1)
result = tf.logical_and(result_0, result_1)
if dtype is None or result.dtype.base_dtype == dtype.base_dtype:
return result
else:
return gen_math_ops.cast(result, dtype)
def sequence_mask(lengths, maxlen=None, dtype=dtypes.bool, name=None):
"""Returns a mask tensor representing the first N positions of each cell.
If `lengths` has shape `[d_1, d_2, ..., d_n]` the resulting tensor `mask` has
dtype `dtype` and shape `[d_1, d_2, ..., d_n, maxlen]`, with
```
mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])
```
Examples:
```python
tf.sequence_mask([1, 3, 2], 5) # [[True, False, False, False, False],
# [True, True, True, False, False],
# [True, True, False, False, False]]
tf.sequence_mask([[1, 3],[2,0]]) # [[[True, False, False],
# [True, True, True]],
# [[True, True, False],
# [False, False, False]]]
```
Args:
lengths: integer tensor, all its values <= maxlen.
maxlen: scalar integer tensor, size of last dimension of returned tensor.
Default is the maximum value in `lengths`.
dtype: output type of the resulting tensor.
name: name of the op.
Returns:
A mask tensor of shape `lengths.shape + (maxlen,)`, cast to specified dtype.
Raises:
ValueError: if `maxlen` is not a scalar.
"""
with ops.name_scope(name, "SequenceMask", [lengths, maxlen]):
lengths = ops.convert_to_tensor(lengths)
if maxlen is None:
maxlen = gen_math_ops._max(lengths, _all_dimensions(lengths))
maxlen = gen_math_ops.maximum(constant(0, maxlen.dtype), maxlen)
else:
maxlen = ops.convert_to_tensor(maxlen)
if maxlen.get_shape(
).ndims is not None and maxlen.get_shape().ndims != 0:
raise ValueError("maxlen must be scalar for sequence_mask")
# The basic idea is to compare a range row vector of size maxlen:
# [0, 1, 2, 3, 4]
# to length as a matrix with 1 column: [[1], [3], [2]].
# Because of broadcasting on both arguments this comparison results
# in a matrix of size (len(lengths), maxlen)
row_vector = gen_math_ops._range(constant(0, maxlen.dtype), maxlen,
constant(1, maxlen.dtype))
# Since maxlen >= max(lengths), it is safe to use maxlen as a cast
# authoritative type. Whenever maxlen fits into tf.int32, so do the lengths.
matrix = gen_math_ops.cast(expand_dims(lengths, -1), maxlen.dtype)
result = row_vector < matrix
if dtype is None or result.dtype.base_dtype == dtype.base_dtype:
return result
else:
return gen_math_ops.cast(result, dtype)
def repeat_with_axis(data, repeats, axis, name=None):
"""Repeats elements of `data`.
Args:
data: An `N`-dimensional tensor.
repeats: A 1-D integer tensor specifying how many times each element in
`axis` should be repeated. `len(repeats)` must equal `data.shape[axis]`.
Supports broadcasting from a scalar value.
axis: `int`. The axis along which to repeat values. Must be less than
`max(N, 1)`.
name: A name for the operation.
Returns:
A tensor with `max(N, 1)` dimensions. Has the same shape as `data`,
except that dimension `axis` has size `sum(repeats)`.
#### Examples:
```python
>>> repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0)
['a', 'a', 'a', 'c', 'c']
>>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0)
[[1, 2], [1, 2], [3, 4], [3, 4], [3, 4]]
>>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1)
[[1, 1, 2, 2, 2], [3, 3, 4, 4, 4]]
```
"""
if not isinstance(axis, int):
raise TypeError("axis must be an int; got %s" % type(axis).__name__)
with ops.name_scope(name, "Repeat", [data, repeats]):
data = ops.convert_to_tensor(data, name="data")
repeats = convert_to_int_tensor(repeats, name="repeats")
repeats.shape.with_rank_at_most(1)
# If `data` is a scalar, then upgrade it to a vector.
data = _with_nonzero_rank(data)
data_shape = shape(data)
# If `axis` is negative, then convert it to a positive value.
axis = get_positive_axis(axis, data.shape.ndims)
# Check data Tensor shapes.
if repeats.shape.ndims == 1:
data.shape.dims[axis].assert_is_compatible_with(repeats.shape[0])
# If we know that `repeats` is a scalar, then we can just tile & reshape.
if repeats.shape.ndims == 0:
expanded = expand_dims(data, axis + 1)
tiled = tile_one_dimension(expanded, axis + 1, repeats)
result_shape = concat([data_shape[:axis], [-1], data_shape[axis + 1:]],
axis=0)
return tf.reshape(tiled, result_shape)
# Broadcast the `repeats` tensor so rank(repeats) == axis + 1.
if repeats.shape.ndims != axis + 1:
repeats_shape = shape(repeats)
repeats_ndims = rank(repeats)
broadcast_shape = concat(
[data_shape[:axis + 1 - repeats_ndims], repeats_shape], axis=0)
repeats = broadcast_to(repeats, broadcast_shape)
repeats.set_shape([None] * (axis + 1))
# Create a "sequence mask" based on `repeats`, where slices across `axis`
# contain one `True` value for each repetition. E.g., if
# `repeats = [3, 1, 2]`, then `mask = [[1, 1, 1], [1, 0, 0], [1, 1, 0]]`.
max_repeat = gen_math_ops.maximum(
0, gen_math_ops._max(repeats, _all_dimensions(repeats)))
mask = tf.sequence_mask(repeats, max_repeat)
# Add a new dimension around each value that needs to be repeated, and
# then tile that new dimension to match the maximum number of repetitions.
expanded = expand_dims(data, axis + 1)
tiled = tile_one_dimension(expanded, axis + 1, max_repeat)
# Use `boolean_mask` to discard the extra repeated values. This also
# flattens all dimensions up through `axis`.
masked = tf.boolean_mask(tiled, mask)
# Reshape the output tensor to add the outer dimensions back.
if axis == 0:
result = masked
else:
result_shape = concat([data_shape[:axis], [-1], data_shape[axis + 1:]],
axis=0)
result = tf.reshape(masked, result_shape)
# Preserve shape information.
if data.shape.ndims is not None:
new_axis_size = 0 if repeats.shape[0] == 0 else None
result.set_shape(data.shape[:axis].concatenate(
[new_axis_size]).concatenate(data.shape[axis + 1:]))
return result
