本文整理汇总了Python中tensorflow.python.framework.dtypes.uint16方法的典型用法代码示例。如果您正苦于以下问题:Python dtypes.uint16方法的具体用法?Python dtypes.uint16怎么用?Python dtypes.uint16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
的用法示例。
在下文中一共展示了dtypes.uint16方法的13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: _convert_string_dtype
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def _convert_string_dtype(dtype):
if dtype == 'float16':
return dtypes_module.float16
if dtype == 'float32':
return dtypes_module.float32
elif dtype == 'float64':
return dtypes_module.float64
elif dtype == 'int16':
return dtypes_module.int16
elif dtype == 'int32':
return dtypes_module.int32
elif dtype == 'int64':
return dtypes_module.int64
elif dtype == 'uint8':
return dtypes_module.int8
elif dtype == 'uint16':
return dtypes_module.uint16
else:
raise ValueError('Unsupported dtype:', dtype)
示例2: testConvertBetweenInt16AndInt8
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def testConvertBetweenInt16AndInt8(self):
with self.test_session(use_gpu=True):
# uint8, uint16
self._convert([0, 255 * 256], dtypes.uint16, dtypes.uint8,
[0, 255])
self._convert([0, 255], dtypes.uint8, dtypes.uint16,
[0, 255 * 256])
# int8, uint16
self._convert([0, 127 * 2 * 256], dtypes.uint16, dtypes.int8,
[0, 127])
self._convert([0, 127], dtypes.int8, dtypes.uint16,
[0, 127 * 2 * 256])
# int16, uint16
self._convert([0, 255 * 256], dtypes.uint16, dtypes.int16,
[0, 255 * 128])
self._convert([0, 255 * 128], dtypes.int16, dtypes.uint16,
[0, 255 * 256])
示例3: ExtractBitsFromFloat16
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def ExtractBitsFromFloat16(x):
return np.asscalar(np.asarray(x, dtype=np.float16).view(np.uint16))
示例4: testSyntheticUint16
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def testSyntheticUint16(self):
with self.test_session(use_gpu=True) as sess:
# Encode it, then decode it
image0 = constant_op.constant(_SimpleColorRamp(), dtype=dtypes.uint16)
png0 = image_ops.encode_png(image0, compression=7)
image1 = image_ops.decode_png(png0, dtype=dtypes.uint16)
png0, image0, image1 = sess.run([png0, image0, image1])
# PNG is lossless
self.assertAllEqual(image0, image1)
# Smooth ramps compress well, but not too well
self.assertGreaterEqual(len(png0), 800)
self.assertLessEqual(len(png0), 1500)
示例5: testSyntheticTwoChannelUint16
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def testSyntheticTwoChannelUint16(self):
with self.test_session(use_gpu=True) as sess:
# Strip the b channel from an rgb image to get a two-channel image.
gray_alpha = _SimpleColorRamp()[:, :, 0:2]
image0 = constant_op.constant(gray_alpha, dtype=dtypes.uint16)
png0 = image_ops.encode_png(image0, compression=7)
image1 = image_ops.decode_png(png0, dtype=dtypes.uint16)
png0, image0, image1 = sess.run([png0, image0, image1])
self.assertEqual(2, image0.shape[-1])
self.assertAllEqual(image0, image1)
示例6: _convert_string_dtype
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def _convert_string_dtype(dtype):
"""Get the type from a string.
Arguments:
dtype: A string representation of a type.
Returns:
The type requested.
Raises:
ValueError: if `dtype` is not supported.
"""
if dtype == 'float16':
return dtypes_module.float16
if dtype == 'float32':
return dtypes_module.float32
elif dtype == 'float64':
return dtypes_module.float64
elif dtype == 'int16':
return dtypes_module.int16
elif dtype == 'int32':
return dtypes_module.int32
elif dtype == 'int64':
return dtypes_module.int64
elif dtype == 'uint8':
return dtypes_module.int8
elif dtype == 'uint16':
return dtypes_module.uint16
else:
raise ValueError('Unsupported dtype:', dtype)
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:32,代码来源:backend.py
示例7: _is_known_unsigned_by_dtype
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def _is_known_unsigned_by_dtype(dt):
"""Helper returning True if dtype is known to be unsigned."""
return {
dtypes.bool: True,
dtypes.uint8: True,
dtypes.uint16: True,
}.get(dt.base_dtype, False)
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:9,代码来源:util.py
示例8: cumsum
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
"""Compute the cumulative sum of the tensor `x` along `axis`.
By default, this op performs an inclusive cumsum, which means that the first
element of the input is identical to the first element of the output:
```prettyprint
tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c]
```
By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
instead:
```prettyprint
tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b]
```
By setting the `reverse` kwarg to `True`, the cumsum is performed in the
opposite direction:
```prettyprint
tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c]
```
This is more efficient than using separate `tf.reverse` ops.
The `reverse` and `exclusive` kwargs can also be combined:
```prettyprint
tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0]
```
Args:
x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
`complex128`, `qint8`, `quint8`, `qint32`, `half`.
axis: A `Tensor` of type `int32` (default: 0).
exclusive: If `True`, perform exclusive cumsum.
reverse: A `bool` (default: False).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Cumsum", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops.cumsum(
x, axis, exclusive=exclusive, reverse=reverse, name=name)
示例9: cumprod
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def cumprod(x, axis=0, exclusive=False, reverse=False, name=None):
"""Compute the cumulative product of the tensor `x` along `axis`.
By default, this op performs an inclusive cumprod, which means that the
first
element of the input is identical to the first element of the output:
```prettyprint
tf.cumprod([a, b, c]) ==> [a, a * b, a * b * c]
```
By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
performed
instead:
```prettyprint
tf.cumprod([a, b, c], exclusive=True) ==> [1, a, a * b]
```
By setting the `reverse` kwarg to `True`, the cumprod is performed in the
opposite direction:
```prettyprint
tf.cumprod([a, b, c], reverse=True) ==> [a * b * c, b * c, c]
```
This is more efficient than using separate `tf.reverse` ops.
The `reverse` and `exclusive` kwargs can also be combined:
```prettyprint
tf.cumprod([a, b, c], exclusive=True, reverse=True) ==> [b * c, c, 1]
```
Args:
x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
`complex128`, `qint8`, `quint8`, `qint32`, `half`.
axis: A `Tensor` of type `int32` (default: 0).
exclusive: If `True`, perform exclusive cumprod.
reverse: A `bool` (default: False).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Cumprod", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops.cumprod(
x, axis, exclusive=exclusive, reverse=reverse, name=name)
示例10: cumsum
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
"""Compute the cumulative sum of the tensor `x` along `axis`.
By default, this op performs an inclusive cumsum, which means that the first
element of the input is identical to the first element of the output:
```prettyprint
tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c]
```
By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
instead:
```prettyprint
tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b]
```
By setting the `reverse` kwarg to `True`, the cumsum is performed in the
opposite direction:
```prettyprint
tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c]
```
This is more efficient than using separate `tf.reverse` ops.
The `reverse` and `exclusive` kwargs can also be combined:
```prettyprint
tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0]
```
Args:
x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
`complex128`, `qint8`, `quint8`, `qint32`, `half`.
axis: A `Tensor` of type `int32` (default: 0).
reverse: A `bool` (default: False).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Cumsum", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops.cumsum(
x, axis, exclusive=exclusive, reverse=reverse, name=name)
示例11: cumprod
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def cumprod(x, axis=0, exclusive=False, reverse=False, name=None):
"""Compute the cumulative product of the tensor `x` along `axis`.
By default, this op performs an inclusive cumprod, which means that the
first
element of the input is identical to the first element of the output:
```prettyprint
tf.cumprod([a, b, c]) ==> [a, a * b, a * b * c]
```
By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
performed
instead:
```prettyprint
tf.cumprod([a, b, c], exclusive=True) ==> [1, a, a * b]
```
By setting the `reverse` kwarg to `True`, the cumprod is performed in the
opposite direction:
```prettyprint
tf.cumprod([a, b, c], reverse=True) ==> [a * b * c, b * c, c]
```
This is more efficient than using separate `tf.reverse` ops.
The `reverse` and `exclusive` kwargs can also be combined:
```prettyprint
tf.cumprod([a, b, c], exclusive=True, reverse=True) ==> [b * c, c, 1]
```
Args:
x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
`complex128`, `qint8`, `quint8`, `qint32`, `half`.
axis: A `Tensor` of type `int32` (default: 0).
reverse: A `bool` (default: False).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Cumprod", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops.cumprod(
x, axis, exclusive=exclusive, reverse=reverse, name=name)
示例12: cumsum
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
"""Compute the cumulative sum of the tensor `x` along `axis`.
By default, this op performs an inclusive cumsum, which means that the first
element of the input is identical to the first element of the output:
```python
tf.cumsum([a, b, c]) # [a, a + b, a + b + c]
```
By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
instead:
```python
tf.cumsum([a, b, c], exclusive=True) # [0, a, a + b]
```
By setting the `reverse` kwarg to `True`, the cumsum is performed in the
opposite direction:
```python
tf.cumsum([a, b, c], reverse=True) # [a + b + c, b + c, c]
```
This is more efficient than using separate `tf.reverse` ops.
The `reverse` and `exclusive` kwargs can also be combined:
```python
tf.cumsum([a, b, c], exclusive=True, reverse=True) # [b + c, c, 0]
```
Args:
x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
`complex128`, `qint8`, `quint8`, `qint32`, `half`.
axis: A `Tensor` of type `int32` (default: 0). Must be in the range
`[-rank(x), rank(x))`.
exclusive: If `True`, perform exclusive cumsum.
reverse: A `bool` (default: False).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Cumsum", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops.cumsum(
x, axis, exclusive=exclusive, reverse=reverse, name=name)
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:51,代码来源:math_ops.py
示例13: cumprod
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import uint16 [as 别名]
def cumprod(x, axis=0, exclusive=False, reverse=False, name=None):
"""Compute the cumulative product of the tensor `x` along `axis`.
By default, this op performs an inclusive cumprod, which means that the
first element of the input is identical to the first element of the output:
```python
tf.cumprod([a, b, c]) # [a, a * b, a * b * c]
```
By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
performed
instead:
```python
tf.cumprod([a, b, c], exclusive=True) # [1, a, a * b]
```
By setting the `reverse` kwarg to `True`, the cumprod is performed in the
opposite direction:
```python
tf.cumprod([a, b, c], reverse=True) # [a * b * c, b * c, c]
```
This is more efficient than using separate `tf.reverse` ops.
The `reverse` and `exclusive` kwargs can also be combined:
```python
tf.cumprod([a, b, c], exclusive=True, reverse=True) # [b * c, c, 1]
```
Args:
x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
`complex128`, `qint8`, `quint8`, `qint32`, `half`.
axis: A `Tensor` of type `int32` (default: 0). Must be in the range
`[-rank(x), rank(x))`.
exclusive: If `True`, perform exclusive cumprod.
reverse: A `bool` (default: False).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Cumprod", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops.cumprod(
x, axis, exclusive=exclusive, reverse=reverse, name=name)
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:51,代码来源:math_ops.py