/
constant_op.py
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/
constant_op.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Operations that generate constants.
See the @{$python/constant_op$constants guide}.
@@zeros
@@zeros_like
@@ones
@@ones_like
@@fill
@@constant
@@linspace
@@range
@@random_normal
@@truncated_normal
@@random_uniform
@@random_shuffle
@@random_crop
@@multinomial
@@random_gamma
@@random_poisson
@@set_random_seed
"""
# Must be separate from array_ops to avoid a cyclic dependency.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.core.framework import attr_value_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
def constant(value, dtype=None, shape=None, name="Const", verify_shape=False):
"""Creates a constant tensor.
The resulting tensor is populated with values of type `dtype`, as
specified by arguments `value` and (optionally) `shape` (see examples
below).
The argument `value` can be a constant value, or a list of values of type
`dtype`. If `value` is a list, then the length of the list must be less
than or equal to the number of elements implied by the `shape` argument (if
specified). In the case where the list length is less than the number of
elements specified by `shape`, the last element in the list will be used
to fill the remaining entries.
The argument `shape` is optional. If present, it specifies the dimensions of
the resulting tensor. If not present, the shape of `value` is used.
If the argument `dtype` is not specified, then the type is inferred from
the type of `value`.
For example:
```python
# Constant 1-D Tensor populated with value list.
tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
# Constant 2-D tensor populated with scalar value -1.
tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
[-1. -1. -1.]]
```
Args:
value: A constant value (or list) of output type `dtype`.
dtype: The type of the elements of the resulting tensor.
shape: Optional dimensions of resulting tensor.
name: Optional name for the tensor.
verify_shape: Boolean that enables verification of a shape of values.
Returns:
A Constant Tensor.
"""
g = ops.get_default_graph()
tensor_value = attr_value_pb2.AttrValue()
tensor_value.tensor.CopyFrom(
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
const_tensor = g.create_op(
"Const", [], [dtype_value.type],
attrs={"value": tensor_value, "dtype": dtype_value}, name=name).outputs[0]
return const_tensor
def _constant_tensor_conversion_function(v, dtype=None, name=None,
as_ref=False):
_ = as_ref
return constant(v, dtype=dtype, name=name)
ops.register_tensor_conversion_function(
(list, tuple), _constant_tensor_conversion_function, 100)
ops.register_tensor_conversion_function(
np.ndarray, _constant_tensor_conversion_function, 100)
ops.register_tensor_conversion_function(
np.generic, _constant_tensor_conversion_function, 100)
ops.register_tensor_conversion_function(
object, _constant_tensor_conversion_function, 200)
def _tensor_shape_tensor_conversion_function(s, dtype=None, name=None,
as_ref=False):
_ = as_ref
if not s.is_fully_defined():
raise ValueError(
"Cannot convert a partially known TensorShape to a Tensor: %s" % s)
s_list = s.as_list()
int64_value = 0
for dim in s_list:
if dim >= 2**31:
int64_value = dim
break
if dtype is not None:
if dtype not in (dtypes.int32, dtypes.int64):
raise TypeError("Cannot convert a TensorShape to dtype: %s" % dtype)
if dtype == dtypes.int32 and int64_value:
raise ValueError("Cannot convert a TensorShape to dtype int32; "
"a dimension is too large (%s)" % int64_value)
else:
dtype = dtypes.int64 if int64_value else dtypes.int32
if name is None:
name = "shape_as_tensor"
return constant(s_list, dtype=dtype, name=name)
ops.register_tensor_conversion_function(
tensor_shape.TensorShape, _tensor_shape_tensor_conversion_function, 100)
def _dimension_tensor_conversion_function(d, dtype=None, name=None,
as_ref=False):
_ = as_ref
if d.value is None:
raise ValueError("Cannot convert an unknown Dimension to a Tensor: %s" % d)
if dtype is not None:
if dtype not in (dtypes.int32, dtypes.int64):
raise TypeError("Cannot convert a TensorShape to dtype: %s" % dtype)
else:
dtype = dtypes.int32
if name is None:
name = "shape_as_tensor"
return constant(d.value, dtype=dtype, name=name)
ops.register_tensor_conversion_function(
tensor_shape.Dimension, _dimension_tensor_conversion_function, 100)