本文整理汇总了Python中tensorflow.python.framework.dtypes.int64方法的典型用法代码示例。如果您正苦于以下问题:Python dtypes.int64方法的具体用法?Python dtypes.int64怎么用?Python dtypes.int64使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.framework.dtypes
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在下文中一共展示了dtypes.int64方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: dense_to_sparse
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def dense_to_sparse(tensor, eos_token=0, outputs_collections=None, scope=None):
"""Converts a dense tensor into a sparse tensor.
An example use would be to convert dense labels to sparse ones
so that they can be fed to the ctc_loss.
Args:
tensor: An `int` `Tensor` to be converted to a `Sparse`.
eos_token: An integer. It is part of the target label that signifies the
end of a sentence.
outputs_collections: Collection to add the outputs.
scope: Optional scope for name_scope.
"""
with variable_scope.variable_scope(scope, 'dense_to_sparse', [tensor]) as sc:
tensor = ops.convert_to_tensor(tensor)
indices = array_ops.where(
math_ops.not_equal(tensor, constant_op.constant(eos_token,
tensor.dtype)))
values = array_ops.gather_nd(tensor, indices)
shape = array_ops.shape(tensor, out_type=dtypes.int64)
outputs = sparse_tensor.SparseTensor(indices, values, shape)
return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
示例2: _maybe_select_class_id
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def _maybe_select_class_id(labels, predictions_idx, selected_id=None):
"""If class ID is specified, filter all other classes.
Args:
labels: `int64` `Tensor` or `SparseTensor` with shape
[D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
target classes for the associated prediction. Commonly, N=1 and `labels`
has shape [batch_size, num_labels]. [D1, ... DN] must match
`predictions_idx`.
predictions_idx: `int64` `Tensor` of class IDs, with shape [D1, ... DN, k]
where N >= 1. Commonly, N=1 and `predictions_idx` has shape
[batch size, k].
selected_id: Int id to select.
Returns:
Tuple of `labels` and `predictions_idx`, possibly with classes removed.
"""
if selected_id is None:
return labels, predictions_idx
return (_select_class_id(labels, selected_id),
_select_class_id(predictions_idx, selected_id))
示例3: shape_internal
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
# pylint: disable=redefined-builtin
"""Returns the shape of a tensor.
Args:
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:
A `Tensor` of type `out_type`.
"""
with ops.name_scope(name, "Shape", [input]) as name:
if isinstance(
input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
return gen_math_ops.cast(input.dense_shape, out_type)
else:
input_tensor = ops.convert_to_tensor(input)
input_shape = input_tensor.get_shape()
if optimize and input_shape.is_fully_defined():
return constant(input_shape.as_list(), out_type, name=name)
return gen_array_ops.shape(input, name=name, out_type=out_type)
示例4: size
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def size(input, name=None, out_type=dtypes.int32):
# pylint: disable=redefined-builtin
"""Returns the size of a tensor.
This operation returns an integer representing the number of elements in
`input`.
For example:
```python
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
size(t) ==> 12
```
Args:
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:
A `Tensor` of type `out_type`. Defaults to tf.int32.
"""
return size_internal(input, name, optimize=True, out_type=out_type)
示例5: size_internal
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
# pylint: disable=redefined-builtin,protected-access
"""Returns the size of a tensor.
Args:
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:
A `Tensor` of type `out_type`.
"""
with ops.name_scope(name, "Size", [input]) as name:
if isinstance(
input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
return gen_math_ops._prod(
gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
else:
input_tensor = ops.convert_to_tensor(input)
input_shape = input_tensor.get_shape()
if optimize and input_shape.is_fully_defined():
return constant(input_shape.num_elements(), out_type, name=name)
return gen_array_ops.size(input, name=name, out_type=out_type)
示例6: _neg
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def _neg(x, name=None):
"""Computes numerical negative value element-wise.
I.e., \\(y = -x\\).
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
"""
return negative(x, name)
# pylint: enable=g-docstring-has-escape
示例7: square
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def square(x, name=None):
r"""Computes square of x element-wise.
I.e., \\(y = x * x = x^2\\).
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Square", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
x_square = gen_math_ops.square(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_square, dense_shape=x.dense_shape)
else:
return gen_math_ops.square(x, name=name)
示例8: sigmoid
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def sigmoid(x, name=None):
"""Computes sigmoid of `x` element-wise.
Specifically, `y = 1 / (1 + exp(-x))`.
Args:
x: A Tensor with type `float32`, `float64`, `int32`, `complex64`, `int64`,
or `qint32`.
name: A name for the operation (optional).
Returns:
A Tensor with the same type as `x` if `x.dtype != qint32`
otherwise the return type is `quint8`.
@compatibility(numpy)
Equivalent to np.scipy.special.expit
@end_compatibility
"""
with ops.name_scope(name, "Sigmoid", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops._sigmoid(x, name=name)
示例9: tanh
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def tanh(x, name=None):
"""Computes hyperbolic tangent of `x` element-wise.
Args:
x: A Tensor or SparseTensor with type `float`, `double`, `int32`,
`complex64`, `int64`, or `qint32`.
name: A name for the operation (optional).
Returns:
A Tensor or SparseTensor respectively with the same type as `x` if
`x.dtype != qint32` otherwise the return type is `quint8`.
"""
with ops.name_scope(name, "Tanh", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
x_tanh = gen_math_ops._tanh(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_tanh, dense_shape=x.dense_shape)
else:
return gen_math_ops._tanh(x, name=name)
示例10: _transform_feature
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def _transform_feature(self, inputs):
input_tensor = _to_sparse_input(inputs.get(self.key))
if self.dtype.is_integer != input_tensor.dtype.is_integer:
raise ValueError(
'Column dtype and SparseTensors dtype must be compatible. '
'key: {}, column dtype: {}, tensor dtype: {}'.format(
self.key, self.dtype, input_tensor.dtype))
_assert_string_or_int(
input_tensor.dtype,
prefix='column_name: {} input_tensor'.format(self.key))
key_dtype = self.dtype
if input_tensor.dtype.is_integer:
# `index_table_from_tensor` requires 64-bit integer keys.
key_dtype = dtypes.int64
input_tensor = math_ops.to_int64(input_tensor)
return lookup_ops.index_table_from_tensor(
vocabulary_list=tuple(self.vocabulary_list),
default_value=self.default_value,
dtype=key_dtype,
name='{}_lookup'.format(self.key)).lookup(input_tensor)
示例11: _get_or_create_eval_step
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def _get_or_create_eval_step():
"""Gets or creates the eval step `Tensor`.
Returns:
A `Tensor` representing a counter for the evaluation step.
Raises:
ValueError: If multiple `Tensors` have been added to the
`tf.GraphKeys.EVAL_STEP` collection.
"""
graph = ops.get_default_graph()
eval_steps = graph.get_collection(ops.GraphKeys.EVAL_STEP)
if len(eval_steps) == 1:
return eval_steps[0]
elif len(eval_steps) > 1:
raise ValueError('Multiple tensors added to tf.GraphKeys.EVAL_STEP')
else:
counter = variable_scope.get_variable(
'eval_step',
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
collections=[ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.EVAL_STEP])
return counter
示例12: create_global_step
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def create_global_step(graph=None):
"""Create global step tensor in graph.
Args:
graph: The graph in which to create the global step tensor. If missing,
use default graph.
Returns:
Global step tensor.
Raises:
ValueError: if global step tensor is already defined.
"""
graph = graph or ops.get_default_graph()
if get_global_step(graph) is not None:
raise ValueError('"global_step" already exists.')
# Create in proper graph and base name_scope.
with graph.as_default() as g, g.name_scope(None):
return variable_scope.get_variable(
ops.GraphKeys.GLOBAL_STEP,
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP])
示例13: _tensor_shape_tensor_conversion_function
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
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)
示例14: _init_clusters_random
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def _init_clusters_random(self):
"""Does random initialization of clusters.
Returns:
Tensor of randomly initialized clusters.
"""
num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in self._inputs])
# Note that for mini-batch k-means, we should ensure that the batch size of
# data used during initialization is sufficiently large to avoid duplicated
# clusters.
with ops.control_dependencies(
[check_ops.assert_less_equal(self._num_clusters, num_data)]):
indices = random_ops.random_uniform(
array_ops.reshape(self._num_clusters, [-1]),
minval=0,
maxval=math_ops.cast(num_data, dtypes.int64),
seed=self._random_seed,
dtype=dtypes.int64)
clusters_init = embedding_lookup(
self._inputs, indices, partition_strategy='div')
return clusters_init
示例15: _init_clusters_random
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int64 [as 别名]
def _init_clusters_random(data, num_clusters, random_seed):
"""Does random initialization of clusters.
Args:
data: a list of Tensors with a matrix of data, each row is an example.
num_clusters: an integer with the number of clusters.
random_seed: Seed for PRNG used to initialize seeds.
Returns:
A Tensor with num_clusters random rows of data.
"""
assert isinstance(data, list)
num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in data])
with ops.control_dependencies(
[check_ops.assert_less_equal(num_clusters, num_data)]):
indices = random_ops.random_uniform(
[num_clusters],
minval=0,
maxval=math_ops.cast(num_data, dtypes.int64),
seed=random_seed,
dtype=dtypes.int64)
indices %= math_ops.cast(num_data, dtypes.int64)
clusters_init = embedding_lookup(data, indices, partition_strategy='div')
return clusters_init