本文整理汇总了Python中tensorflow.python.framework.dtypes.int16方法的典型用法代码示例。如果您正苦于以下问题:Python dtypes.int16方法的具体用法?Python dtypes.int16怎么用?Python dtypes.int16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.int16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: _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 int16 [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 int16 [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: truediv
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [as 别名]
def truediv(x, y, name=None):
"""Divides x / y elementwise (using Python 3 division operator semantics).
NOTE: Prefer using the Tensor operator or tf.divide which obey Python
division operator semantics.
This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first. This op is generated by normal
`x / y` division in Python 3 and in Python 2.7 with
`from __future__ import division`. If you want integer division that rounds
down, use `x // y` or `tf.floordiv`.
`x` and `y` must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
and `int64` (matching the behavior of Numpy).
Args:
x: `Tensor` numerator of numeric type.
y: `Tensor` denominator of numeric type.
name: A name for the operation (optional).
Returns:
`x / y` evaluated in floating point.
Raises:
TypeError: If `x` and `y` have different dtypes.
"""
return _truediv_python3(x, y, name)
示例4: test_int16_to_sparse_ids_2d
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [as 别名]
def test_int16_to_sparse_ids_2d(self):
indicators = (
(0, 0, 1, 0),
(1, 0, 0, 1),
)
sparse_ids = sparse_ops.indicators_to_sparse_ids(
indicators, dtype=dtypes.int16)
with self.cached_session():
_assert_sparse_tensor_value(self, sparse_tensor.SparseTensorValue(
indices=((0, 0), (1, 0), (1, 1)),
values=np.array((2, 0, 3), dtype=np.int16),
dense_shape=(2, 2),
), sparse_ids.eval())
示例5: testConvertBetweenInteger
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [as 别名]
def testConvertBetweenInteger(self):
# Make sure converting to between integer types scales appropriately
with self.test_session(use_gpu=True):
self._convert([0, 255], dtypes.uint8, dtypes.int16, [0, 255 * 128])
self._convert([0, 32767], dtypes.int16, dtypes.uint8, [0, 255])
示例6: testFromCSVWithFeatureSpec
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [as 别名]
def testFromCSVWithFeatureSpec(self):
if not HAS_PANDAS:
return
num_batches = 100
batch_size = 8
data_path = _make_test_csv_sparse()
feature_spec = {
"int": tf.FixedLenFeature(None, dtypes.int16, np.nan),
"float": tf.VarLenFeature(dtypes.float16),
"bool": tf.VarLenFeature(dtypes.bool),
"string": tf.FixedLenFeature(None, dtypes.string, "")
}
pandas_df = pd.read_csv(data_path, dtype={"string": object})
# Pandas insanely uses NaN for empty cells in a string column.
# And, we can't use Pandas replace() to fix them because nan != nan
s = pandas_df["string"]
for i in range(0, len(s)):
if isinstance(s[i], float) and math.isnan(s[i]):
pandas_df.set_value(i, "string", "")
tensorflow_df = df.TensorFlowDataFrame.from_csv_with_feature_spec(
[data_path],
batch_size=batch_size,
shuffle=False,
feature_spec=feature_spec)
# These columns were sparse; re-densify them for comparison
tensorflow_df["float"] = densify.Densify(np.nan)(tensorflow_df["float"])
tensorflow_df["bool"] = densify.Densify(np.nan)(tensorflow_df["bool"])
self._assert_pandas_equals_tensorflow(pandas_df,
tensorflow_df,
num_batches=num_batches,
batch_size=batch_size)
示例7: _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 int16 [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8: assert_integer_form
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [as 别名]
def assert_integer_form(
x, data=None, summarize=None, message=None,
int_dtype=None, name="assert_integer_form"):
"""Assert that x has integer components (or floats equal to integers).
Args:
x: Floating-point `Tensor`
data: The tensors to print out if the condition is `False`. Defaults to
error message and first few entries of `x` and `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
int_dtype: A `tf.dtype` used to cast the float to. The default (`None`)
implies the smallest possible signed int will be used for casting.
name: A name for this operation (optional).
Returns:
Op raising `InvalidArgumentError` if `cast(x, int_dtype) != x`.
"""
with ops.name_scope(name, values=[x, data]):
x = ops.convert_to_tensor(x, name="x")
if x.dtype.is_integer:
return control_flow_ops.no_op()
message = message or "{} has non-integer components".format(x.op.name)
if int_dtype is None:
try:
int_dtype = {
dtypes.float16: dtypes.int16,
dtypes.float32: dtypes.int32,
dtypes.float64: dtypes.int64,
}[x.dtype.base_dtype]
except KeyError:
raise TypeError("Unrecognized type {}".format(x.dtype.name))
return check_ops.assert_equal(
x, math_ops.cast(math_ops.cast(x, int_dtype), x.dtype),
data=data, summarize=summarize, message=message, name=name)
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:37,代码来源:util.py
示例9: cumsum
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [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10: cumprod
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [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11: cumsum
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [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12: cumprod
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [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13: truediv
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [as 别名]
def truediv(x, y, name=None):
"""Divides x / y elementwise, always producing floating point results.
The same as `tf.div` for floating point arguments, but casts integer arguments
to floating point before dividing so that the result is always floating point.
This op is generated by normal `x / y` division in Python 3 and in Python 2.7
with `from __future__ import division`. If you want integer division that
rounds down, use `x // y` or `tf.floordiv`.
`x` and `y` must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
and `int64` (matching the behavior of Numpy).
Args:
x: `Tensor` numerator of numeric type.
y: `Tensor` denominator of numeric type.
name: A name for the operation (optional).
Returns:
`x / y` evaluated in floating point.
Raises:
TypeError: If `x` and `y` have different dtypes.
"""
with ops.name_scope(name, "truediv", [x, y]) as name:
x = ops.convert_to_tensor(x, name="x")
y = ops.convert_to_tensor(y, name="y")
x_dtype = x.dtype.base_dtype
y_dtype = y.dtype.base_dtype
if x_dtype != y_dtype:
raise TypeError("x and y must have the same dtype, got %r != %r" %
(x_dtype, y_dtype))
try:
dtype = _TRUEDIV_TABLE[x_dtype]
except KeyError:
raise TypeError("Invalid dtype %r in __truediv__" % x_dtype)
if dtype is not None:
x = cast(x, dtype)
y = cast(y, dtype)
return gen_math_ops.div(x, y, name=name)
# TODO(aselle): Deprecate this once all internal functionality uses
# tf.truncatediv
示例14: compress
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [as 别名]
def compress(self, inputs):
"""Compress inputs and store their binary representations into strings.
Args:
inputs: `Tensor` with values to be compressed.
Returns:
String `Tensor` vector containing the compressed representation of each
batch element of `inputs`.
"""
with ops.name_scope(self._name_scope()):
inputs = ops.convert_to_tensor(inputs)
if not self.built:
# Check input assumptions set before layer building, e.g. input rank.
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
if self.dtype is None:
self._dtype = inputs.dtype.base_dtype.name
self.build(inputs.shape)
# Check input assumptions set after layer building, e.g. input shape.
if not context.executing_eagerly():
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
ndim = self.input_spec.ndim
channel_axis = self._channel_axis(ndim)
# Tuple of slices for expanding dimensions of tensors below.
slices = ndim * [None] + [slice(None)]
slices[channel_axis] = slice(None)
slices = tuple(slices)
# Expand dimensions of CDF to input dimensions, keeping the channels along
# the right dimension.
cdf = self._quantized_cdf[slices[1:]]
num_levels = array_ops.shape(cdf)[-1] - 1
# Bring inputs to the right range by centering the range on the medians.
half = constant_op.constant(.5, dtype=self.dtype)
medians = array_ops.squeeze(self._medians, [1, 2])
offsets = (math_ops.cast(num_levels // 2, self.dtype) + half) - medians
# Expand offsets to input dimensions and add to inputs.
values = inputs + offsets[slices[:-1]]
# Clip to range and cast to integers. Because we have added .5 above, and
# all values are positive, the cast effectively implements rounding.
values = math_ops.maximum(values, half)
values = math_ops.minimum(
values, math_ops.cast(num_levels, self.dtype) - half)
values = math_ops.cast(values, dtypes.int16)
def loop_body(tensor):
return coder_ops.range_encode(
tensor, cdf, precision=self.range_coder_precision)
strings = functional_ops.map_fn(
loop_body, values, dtype=dtypes.string, back_prop=False)
if not context.executing_eagerly():
strings.set_shape(inputs.shape[:1])
return strings
示例15: cumsum
# 需要导入模块: from tensorflow.python.framework import dtypes [as 别名]
# 或者: from tensorflow.python.framework.dtypes import int16 [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