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- #! /usr/bin/python
- # -*- coding: utf-8 -*-
- """A file containing functions related to array manipulation."""
-
- from tensorflow.python.eager import context
- from tensorflow.python.framework import constant_op, dtypes, ops, tensor_shape
- from tensorflow.python.framework.constant_op import constant
- from tensorflow.python.framework.ops import convert_to_tensor
- from tensorflow.python.ops.array_ops import shape_internal
- from tensorflow.python.ops.gen_array_ops import fill, reshape
-
- __all__ = ['alphas', 'alphas_like']
-
-
- def alphas(shape, alpha_value, name=None):
- """Creates a tensor with all elements set to `alpha_value`.
- This operation returns a tensor of type `dtype` with shape `shape` and all
- elements set to alpha.
-
- Parameters
- ----------
- shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type `int32`.
- The shape of the desired tensor
- alpha_value: `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, int32`, `int64`
- The value used to fill the resulting `Tensor`.
- name: str
- A name for the operation (optional).
-
- Returns
- -------
- A `Tensor` with all elements set to alpha.
-
- Examples
- --------
- >>> tl.alphas([2, 3], tf.int32) # [[alpha, alpha, alpha], [alpha, alpha, alpha]]
- """
- with ops.name_scope(name, "alphas", [shape]) as name:
-
- alpha_tensor = convert_to_tensor(alpha_value)
- alpha_dtype = dtypes.as_dtype(alpha_tensor.dtype).base_dtype
-
- if not isinstance(shape, ops.Tensor):
- try:
- shape = constant_op._tensor_shape_tensor_conversion_function(tensor_shape.TensorShape(shape))
- except (TypeError, ValueError):
- shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
-
- if not shape._shape_tuple():
- shape = reshape(shape, [-1]) # Ensure it's a vector
-
- try:
- output = constant(alpha_value, shape=shape, dtype=alpha_dtype, name=name)
-
- except (TypeError, ValueError):
- output = fill(shape, constant(alpha_value, dtype=alpha_dtype), name=name)
-
- if output.dtype.base_dtype != alpha_dtype:
- raise AssertionError("Dtypes do not corresponds: %s and %s" % (output.dtype.base_dtype, alpha_dtype))
-
- return output
-
-
- def alphas_like(tensor, alpha_value, name=None, optimize=True):
- """Creates a tensor with all elements set to `alpha_value`.
- Given a single tensor (`tensor`), this operation returns a tensor of the same
- type and shape as `tensor` with all elements set to `alpha_value`.
-
- Parameters
- ----------
- tensor: tf.Tensor
- The Tensorflow Tensor that will be used as a template.
- alpha_value: `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, int32`, `int64`
- The value used to fill the resulting `Tensor`.
- name: str
- A name for the operation (optional).
- optimize: bool
- if true, attempt to statically determine the shape of 'tensor' and encode it as a constant.
-
- Returns
- -------
- A `Tensor` with all elements set to `alpha_value`.
-
- Examples
- --------
- >>> tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
- >>> tl.alphas_like(tensor, 0.5) # [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]
- """
- with ops.name_scope(name, "alphas_like", [tensor]) as name:
- tensor = ops.convert_to_tensor(tensor, name="tensor")
-
- if context.in_eager_mode(): # and dtype is not None and dtype != tensor.dtype:
- ret = alphas(shape_internal(tensor, optimize=optimize), alpha_value=alpha_value, name=name)
-
- else: # if context.in_graph_mode():
-
- # For now, variant types must be created via zeros_like; as we need to
- # pass the input variant object to the proper zeros callback.
-
- if (optimize and tensor.shape.is_fully_defined()):
- # We can produce a zeros tensor independent of the value of 'tensor',
- # since the shape is known statically.
- ret = alphas(tensor.shape, alpha_value=alpha_value, name=name)
-
- # elif dtype is not None and dtype != tensor.dtype and dtype != dtypes.variant:
- else:
- ret = alphas(shape_internal(tensor, optimize=optimize), alpha_value=alpha_value, name=name)
-
- ret.set_shape(tensor.get_shape())
-
- return ret
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