diff options
Diffstat (limited to 'tensorflow/python/ops/array_ops.py')
| -rw-r--r-- | tensorflow/python/ops/array_ops.py | 152 |
1 files changed, 76 insertions, 76 deletions
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index 9df0ffce1a..cefd164a74 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -172,13 +172,13 @@ def shape(input, name=None, out_type=dtypes.int32): ``` Args: - input: An `Output` or `SparseTensor`. + input: A `Tensor` or `SparseTensor`. name: A name for the operation (optional). out_type: (Optional) The specified output type of the operation (`int32` or `int64`). Defaults to `tf.int32`. Returns: - An `Output` of type `out_type`. + A `Tensor` of type `out_type`. """ return shape_internal(input, name, optimize=True, out_type=out_type) @@ -188,14 +188,14 @@ def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32): """Returns the shape of a tensor. Args: - input: An `Output` or `SparseTensor`. + input: A `Tensor` or `SparseTensor`. name: A name for the operation (optional). optimize: if true, encode the shape as a constant when possible. out_type: (Optional) The specified output type of the operation (`int32` or `int64`). Defaults to tf.int32. Returns: - An `Output` of type `out_type`. + A `Tensor` of type `out_type`. """ with ops.name_scope(name, "Shape", [input]) as name: @@ -225,13 +225,13 @@ def size(input, name=None, out_type=dtypes.int32): ``` Args: - input: An `Output` or `SparseTensor`. + input: A `Tensor` or `SparseTensor`. name: A name for the operation (optional). out_type: (Optional) The specified output type of the operation (`int32` or `int64`). Defaults to tf.int32. Returns: - An `Output` of type `out_type`. Defaults to tf.int32. + A `Tensor` of type `out_type`. Defaults to tf.int32. """ return size_internal(input, name, optimize=True, out_type=out_type) @@ -241,14 +241,14 @@ def size_internal(input, name=None, optimize=True, out_type=dtypes.int32): """Returns the size of a tensor. Args: - input: An `Output` or `SparseTensor`. + input: A `Tensor` or `SparseTensor`. name: A name for the operation (optional). optimize: if true, encode the size as a constant when possible. out_type: (Optional) The specified output type of the operation (`int32` or `int64`). Defaults to tf.int32. Returns: - An `Output` of type `out_type`. + A `Tensor` of type `out_type`. """ with ops.name_scope(name, "Size", [input]) as name: if isinstance( @@ -282,11 +282,11 @@ def rank(input, name=None): element of the tensor. Rank is also known as "order", "degree", or "ndims." Args: - input: An `Output` or `SparseTensor`. + input: A `Tensor` or `SparseTensor`. name: A name for the operation (optional). Returns: - An `Output` of type `int32`. + A `Tensor` of type `int32`. @compatibility(numpy) Equivalent to np.ndim @@ -300,12 +300,12 @@ def rank_internal(input, name=None, optimize=True): """Returns the rank of a tensor. Args: - input: An `Output` or `SparseTensor`. + input: A `Tensor` or `SparseTensor`. name: A name for the operation (optional). optimize: if true, encode the rank as a constant when possible. Returns: - An `Output` of type `int32`. + A `Tensor` of type `int32`. """ with ops.name_scope(name, "Rank", [input]) as name: if isinstance( @@ -320,7 +320,7 @@ def rank_internal(input, name=None, optimize=True): def _SliceHelper(tensor, slice_spec, var=None): - """Overload for Output.__getitem__. + """Overload for Tensor.__getitem__. This operation extracts the specified region from the tensor. The notation is similar to NumPy with the restriction that @@ -357,7 +357,7 @@ def _SliceHelper(tensor, slice_spec, var=None): Args: tensor: An ops.Tensor object. - slice_spec: The arguments to Output.__getitem__. + slice_spec: The arguments to Tensor.__getitem__. var: In the case of variable slice assignment, the Variable object to slice (i.e. tensor is the read-only view of this variable). @@ -472,13 +472,13 @@ def slice(input_, begin, size, name=None): ``` Args: - input_: An `Output`. - begin: An `int32` or `int64` `Output`. - size: An `int32` or `int64` `Output`. + input_: A `Tensor`. + begin: An `int32` or `int64` `Tensor`. + size: An `int32` or `int64` `Tensor`. name: A name for the operation (optional). Returns: - An `Output` the same type as `input`. + A `Tensor` the same type as `input`. """ return gen_array_ops._slice(input_, begin, size, name=name) @@ -561,10 +561,10 @@ def strided_slice(input_, ``` Args: - input_: An `Output`. - begin: An `int32` or `int64` `Output`. - end: An `int32` or `int64` `Output`. - strides: An `int32` or `int64` `Output`. + input_: A `Tensor`. + begin: An `int32` or `int64` `Tensor`. + end: An `int32` or `int64` `Tensor`. + strides: An `int32` or `int64` `Tensor`. begin_mask: An `int32` mask. end_mask: An `int32` mask. ellipsis_mask: An `int32` mask. @@ -574,7 +574,7 @@ def strided_slice(input_, name: A name for the operation (optional). Returns: - An `Output` the same type as `input`. + A `Tensor` the same type as `input`. """ op = gen_array_ops.strided_slice( input=input_, @@ -617,7 +617,7 @@ def _SliceHelperVar(var, slice_spec): This allows creating a sub-tensor from part of the current contents of a variable. See - [`Output.__getitem__`](../../api_docs/python/framework.md#Output.__getitem__) + [`Tensor.__getitem__`](../../api_docs/python/framework.md#Tensor.__getitem__) for detailed examples of slicing. This function in addition also allows assignment to a sliced range. @@ -642,7 +642,7 @@ def _SliceHelperVar(var, slice_spec): Args: var: An `ops.Variable` object. - slice_spec: The arguments to `Output.__getitem__`. + slice_spec: The arguments to `Tensor.__getitem__`. Returns: The appropriate slice of "tensor", based on "slice_spec". @@ -686,13 +686,13 @@ def stack(values, axis=0, name="stack"): tf.stack([x, y, z]) = np.asarray([x, y, z]) Args: - values: A list of `Output` objects with the same shape and type. + values: A list of `Tensor` objects with the same shape and type. axis: An `int`. The axis to stack along. Defaults to the first dimension. Supports negative indexes. name: A name for this operation (optional). Returns: - output: A stacked `Output` with the same type as `values`. + output: A stacked `Tensor` with the same type as `values`. Raises: ValueError: If `axis` is out of the range [-(R+1), R+1). @@ -740,13 +740,13 @@ def pack(values, axis=0, name="pack"): tf.pack([x, y, z]) = np.asarray([x, y, z]) Args: - values: A list of `Output` objects with the same shape and type. + values: A list of `Tensor` objects with the same shape and type. axis: An `int`. The axis to pack along. Defaults to the first dimension. Supports negative indexes. name: A name for this operation (optional). Returns: - output: A packed `Output` with the same type as `values`. + output: A packed `Tensor` with the same type as `values`. Raises: ValueError: If `axis` is out of the range [-(R+1), R+1). @@ -764,7 +764,7 @@ def _autopacking_helper(list_or_tuple, dtype, name): name: A name for the returned tensor. Returns: - A `tf.Output` with value equivalent to `list_or_tuple`. + A `tf.Tensor` with value equivalent to `list_or_tuple`. """ must_pack = False converted_elems = [] @@ -805,7 +805,7 @@ def _get_dtype_from_nested_lists(list_or_tuple): Args: list_or_tuple: A list or tuple representing an object that can be - converted to a `tf.Output`. + converted to a `tf.Tensor`. Returns: The dtype of any tensor-like object in `list_or_tuple`, or `None` if no @@ -864,7 +864,7 @@ def unstack(value, num=None, axis=0, name="unstack"): tf.unstack(x, n) = list(x) Args: - value: A rank `R > 0` `Output` to be unstacked. + value: A rank `R > 0` `Tensor` to be unstacked. num: An `int`. The length of the dimension `axis`. Automatically inferred if `None` (the default). axis: An `int`. The axis to unstack along. Defaults to the first @@ -872,7 +872,7 @@ def unstack(value, num=None, axis=0, name="unstack"): name: A name for the operation (optional). Returns: - The list of `Output` objects unstacked from `value`. + The list of `Tensor` objects unstacked from `value`. Raises: ValueError: If `num` is unspecified and cannot be inferred. @@ -915,7 +915,7 @@ def unpack(value, num=None, axis=0, name="unpack"): tf.unpack(x, n) = list(x) Args: - value: A rank `R > 0` `Output` to be unpacked. + value: A rank `R > 0` `Tensor` to be unpacked. num: An `int`. The length of the dimension `axis`. Automatically inferred if `None` (the default). axis: An `int`. The axis to unpack along. Defaults to the first @@ -923,7 +923,7 @@ def unpack(value, num=None, axis=0, name="unpack"): name: A name for the operation (optional). Returns: - The list of `Output` objects unpacked from `value`. + The list of `Tensor` objects unpacked from `value`. Raises: ValueError: If `num` is unspecified and cannot be inferred. @@ -979,12 +979,12 @@ def concat(concat_dim, values, name="concat"): ``` Args: - concat_dim: 0-D `int32` `Output`. Dimension along which to concatenate. - values: A list of `Output` objects or a single `Output`. + concat_dim: 0-D `int32` `Tensor`. Dimension along which to concatenate. + values: A list of `Tensor` objects or a single `Tensor`. name: A name for the operation (optional). Returns: - An `Output` resulting from concatenation of the input tensors. + A `Tensor` resulting from concatenation of the input tensors. """ if not isinstance(values, (list, tuple)): values = [values] @@ -1144,14 +1144,14 @@ def split(split_dim, num_split, value, name="split"): ``` Args: - split_dim: A 0-D `int32` `Output`. The dimension along which to split. + split_dim: A 0-D `int32` `Tensor`. The dimension along which to split. Must be in the range `[0, rank(value))`. num_split: A Python integer. The number of ways to split. - value: The `Output` to split. + value: The `Tensor` to split. name: A name for the operation (optional). Returns: - `num_split` `Output` objects resulting from splitting `value`. + `num_split` `Tensor` objects resulting from splitting `value`. """ return gen_array_ops._split(split_dim=split_dim, num_split=num_split, @@ -1185,20 +1185,20 @@ def split_v(value, size_splits, split_dim=0, num=None, name="split_v"): ``` Args: - value: The `Output` to split. + value: The `Tensor` to split. size_splits: Either an integer indicating the number of splits along split_dim or a 1-D Tensor containing the sizes of each output tensor along split_dim. If an integer then it must evenly divide value.shape[split_dim]; otherwise the sum of sizes along the split dimension must match that of the input. - split_dim: A 0-D `int32` `Output`. The dimension along which to split. + split_dim: A 0-D `int32` `Tensor`. The dimension along which to split. Must be in the range `[0, rank(value))`. Defaults to 0. num: Optional, used to specify the number of outputs when it cannot be inferred from the shape of size_splits. name: A name for the operation (optional). Returns: - `len(size_splits)` `Output` objects resulting from splitting `value`. + `len(size_splits)` `Tensor` objects resulting from splitting `value`. Raises: ValueError: If `num` is unspecified and cannot be inferred. @@ -1259,12 +1259,12 @@ def transpose(a, perm=None, name="transpose"): ``` Args: - a: An `Output`. + a: A `Tensor`. perm: A permutation of the dimensions of `a`. name: A name for the operation (optional). Returns: - A transposed `Output`. + A transposed `Tensor`. """ with ops.name_scope(name, "transpose", [a]) as name: if perm is None: @@ -1301,11 +1301,11 @@ def matrix_transpose(a, name="matrix_transpose"): ``` Args: - a: An `Output` with `rank >= 2`. + a: A `Tensor` with `rank >= 2`. name: A name for the operation (optional). Returns: - A transposed batch matrix `Output`. + A transposed batch matrix `Tensor`. Raises: ValueError: If `a` is determined statically to have `rank < 2`. @@ -1347,12 +1347,12 @@ def zeros(shape, dtype=dtypes.float32, name=None): ``` Args: - shape: Either a list of integers, or a 1-D `Output` of type `int32`. - dtype: The type of an element in the resulting `Output`. + shape: Either a list of integers, or a 1-D `Tensor` of type `int32`. + dtype: The type of an element in the resulting `Tensor`. name: A name for the operation (optional). Returns: - An `Output` with all elements set to zero. + A `Tensor` with all elements set to zero. """ dtype = dtypes.as_dtype(dtype).base_dtype with ops.name_scope(name, "zeros", [shape]) as name: @@ -1382,15 +1382,15 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True): ``` Args: - tensor: An `Output`. - dtype: A type for the returned `Output`. Must be `float32`, `float64`, + tensor: A `Tensor`. + dtype: A type for the returned `Tensor`. Must be `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`. name: A name for the operation (optional). optimize: if true, attempt to statically determine the shape of 'tensor' and encode it as a constant. Returns: - An `Output` with all elements set to zero. + A `Tensor` with all elements set to zero. """ with ops.name_scope(name, "zeros_like", [tensor]) as name: tensor = ops.convert_to_tensor(tensor, name="tensor") @@ -1417,8 +1417,8 @@ def ones_like(tensor, dtype=None, name=None, optimize=True): ``` Args: - tensor: An `Output`. - dtype: A type for the returned `Output`. Must be `float32`, `float64`, + tensor: A `Tensor`. + dtype: A type for the returned `Tensor`. Must be `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, `complex128` or `bool`. name: A name for the operation (optional). @@ -1426,7 +1426,7 @@ def ones_like(tensor, dtype=None, name=None, optimize=True): and encode it as a constant. Returns: - An `Output` with all elements set to 1. + A `Tensor` with all elements set to 1. """ with ops.name_scope(name, "ones_like", [tensor]) as name: tensor = ops.convert_to_tensor(tensor, name="tensor") @@ -1451,12 +1451,12 @@ def ones(shape, dtype=dtypes.float32, name=None): ``` Args: - shape: Either a list of integers, or a 1-D `Output` of type `int32`. - dtype: The type of an element in the resulting `Output`. + shape: Either a list of integers, or a 1-D `Tensor` of type `int32`. + dtype: The type of an element in the resulting `Tensor`. name: A name for the operation (optional). Returns: - An `Output` with all elements set to 1. + A `Tensor` with all elements set to 1. """ dtype = dtypes.as_dtype(dtype).base_dtype with ops.name_scope(name, "ones", [shape]) as name: @@ -1476,7 +1476,7 @@ def placeholder(dtype, shape=None, name=None): **Important**: This tensor will produce an error if evaluated. Its value must be fed using the `feed_dict` optional argument to `Session.run()`, - `Output.eval()`, or `Operation.run()`. + `Tensor.eval()`, or `Operation.run()`. For example: @@ -1498,7 +1498,7 @@ def placeholder(dtype, shape=None, name=None): name: A name for the operation (optional). Returns: - An `Output` that may be used as a handle for feeding a value, but not + A `Tensor` that may be used as a handle for feeding a value, but not evaluated directly. """ shape = tensor_shape.as_shape(shape) @@ -1516,7 +1516,7 @@ def placeholder(dtype, shape=None, name=None): # pylint: disable=redefined-outer-name def _normalize_sparse_shape(shape, name): - """Takes numpy array or Tensor or None and returns either None or Output.""" + """Takes numpy array or Tensor or None and returns either None or Tensor.""" if shape is None: return None if not isinstance(shape, ops.Tensor): for el in shape: @@ -1530,7 +1530,7 @@ def sparse_placeholder(dtype, shape=None, name=None): **Important**: This sparse tensor will produce an error if evaluated. Its value must be fed using the `feed_dict` optional argument to - `Session.run()`, `Output.eval()`, or `Operation.run()`. + `Session.run()`, `Tensor.eval()`, or `Operation.run()`. For example: @@ -1620,13 +1620,13 @@ def pad(tensor, paddings, mode="CONSTANT", name=None): # pylint: disable=invali ``` Args: - tensor: An `Output`. - paddings: An `Output` of type `int32`. + tensor: A `Tensor`. + paddings: A `Tensor` of type `int32`. mode: One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive) name: A name for the operation (optional). Returns: - An `Output`. Has the same type as `tensor`. + A `Tensor`. Has the same type as `tensor`. Raises: ValueError: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC". @@ -1683,12 +1683,12 @@ def meshgrid(*args, **kwargs): ``` Args: - *args: `Output`s with rank 1 + *args: `Tensor`s with rank 1 indexing: Either 'xy' or 'ij' (optional, default: 'xy') name: A name for the operation (optional). Returns: - outputs: A list of N `Output`s with rank N + outputs: A list of N `Tensor`s with rank N """ indexing = kwargs.pop("indexing", "xy") @@ -1841,7 +1841,7 @@ def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"): name: A name for the operation (optional). Returns: - A dense `Output` with rank `R - 1`, where R is the rank of the + A dense `Tensor` with rank `R - 1`, where R is the rank of the `SparseTensor` inputs `hypothesis` and `truth`. Raises: @@ -2103,7 +2103,7 @@ def one_hot(indices, depth, on_value=None, off_value=None, ``` Args: - indices: An `Output` of indices. + indices: A `Tensor` of indices. depth: A scalar defining the depth of the one hot dimension. on_value: A scalar defining the value to fill in output when `indices[j] = i`. (default: 1) @@ -2234,7 +2234,7 @@ def squeeze(input, axis=None, name=None, squeeze_dims=None): ``` Args: - input: An `Output`. The `input` to squeeze. + input: A `Tensor`. The `input` to squeeze. axis: An optional list of `ints`. Defaults to `[]`. If specified, only squeezes the dimensions listed. The dimension index starts at 0. It is an error to squeeze a dimension that is not 1. @@ -2242,7 +2242,7 @@ def squeeze(input, axis=None, name=None, squeeze_dims=None): squeeze_dims: Deprecated keyword argument that is now axis. Returns: - An `Output`. Has the same type as `input`. + A `Tensor`. Has the same type as `input`. Contains the same data as `input`, but has one or more dimensions of size 1 removed. @@ -2285,7 +2285,7 @@ def where(condition, x=None, y=None, name=None): `x` and `y`. Args: - condition: An `Output` of type `bool` + condition: A `Tensor` of type `bool` x: A Tensor which may have the same shape as `condition`. If `condition` is rank 1, `x` may have higher rank, but its first dimension must match the size of `condition`. @@ -2293,8 +2293,8 @@ def where(condition, x=None, y=None, name=None): name: A name of the operation (optional) Returns: - An `Output` with the same type and shape as `x`, `y` if they are non-None. - An `Output` with shape `(num_true, dim_size(condition))`. + A `Tensor` with the same type and shape as `x`, `y` if they are non-None. + A `Tensor` with shape `(num_true, dim_size(condition))`. Raises: ValueError: When exactly one of `x` or `y` is non-None. |