本文整理汇总了Python中syntaxnet.util.check.IsNone方法的典型用法代码示例。如果您正苦于以下问题：Python check.IsNone方法的具体用法？Python check.IsNone怎么用？Python check.IsNone使用的例子？那么恭喜您, 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syntaxnet.util.check的用法示例。
在下文中一共展示了check.IsNone方法的3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: __init__
# 需要导入模块: from syntaxnet.util import check [as 别名]
# 或者: from syntaxnet.util.check import IsNone [as 别名]
def __init__(self, tensor=None, array=None, stride=None, dim=None):
"""Creates ops for converting the input to either format.
If 'tensor' is used, then a conversion from [stride * steps, dim] to
[steps + 1, stride, dim] is performed for dynamic_tensor reads.
If 'array' is used, then a conversion from [steps + 1, stride, dim] to
[stride * steps, dim] is performed for bulk_tensor reads.
Args:
tensor: Bulk tensor input.
array: TensorArray dynamic input.
stride: stride of bulk tensor. Not used for dynamic.
dim: dim of bulk tensor. Not used for dynamic.
"""
if tensor is not None:
check.IsNone(array, 'Cannot initialize from tensor and array')
check.NotNone(stride, 'Stride is required for bulk tensor')
check.NotNone(dim, 'Dim is required for bulk tensor')
self._bulk_tensor = tensor
with tf.name_scope('convert_to_dyn'):
tensor = tf.reshape(tensor, [stride, -1, dim])
tensor = tf.transpose(tensor, perm=[1, 0, 2])
pad = tf.zeros([1, stride, dim], dtype=tensor.dtype)
self._array_tensor = tf.concat([pad, tensor], 0)
if array is not None:
check.IsNone(tensor, 'Cannot initialize from both tensor and array')
with tf.name_scope('convert_to_bulk'):
self._bulk_tensor = convert_network_state_tensorarray(array)
with tf.name_scope('convert_to_dyn'):
self._array_tensor = array.stack() 示例2: testCheckIsNone
# 需要导入模块: from syntaxnet.util import check [as 别名]
# 或者: from syntaxnet.util.check import IsNone [as 别名]
def testCheckIsNone(self):
check.IsNone(None, 'foo')
with self.assertRaisesRegexp(ValueError, 'bar'):
check.IsNone(1, 'bar')
with self.assertRaisesRegexp(RuntimeError, 'baz'):
check.IsNone([], 'baz', RuntimeError) 示例3: __init__
# 需要导入模块: from syntaxnet.util import check [as 别名]
# 或者: from syntaxnet.util.check import IsNone [as 别名]
def __init__(self, tensor=None, array=None, stride=None, dim=None):
"""Creates ops for converting the input to either format.
If 'tensor' is used, then a conversion from [stride * steps, dim] to
[steps + 1, stride, dim] is performed for dynamic_tensor reads.
If 'array' is used, then a conversion from [steps + 1, stride, dim] to
[stride * steps, dim] is performed for bulk_tensor reads.
Args:
tensor: Bulk tensor input.
array: TensorArray dynamic input.
stride: stride of bulk tensor. Not used for dynamic.
dim: dim of bulk tensor. Not used for dynamic.
"""
if tensor is not None:
check.IsNone(array, 'Cannot initialize from tensor and array')
check.NotNone(stride, 'Stride is required for bulk tensor')
check.NotNone(dim, 'Dim is required for bulk tensor')
self._bulk_tensor = tensor
if dim >= 0:
# These operations will fail if |dim| is negative.
with tf.name_scope('convert_to_dyn'):
tensor = tf.reshape(tensor, [stride, -1, dim])
tensor = tf.transpose(tensor, perm=[1, 0, 2])
pad = tf.zeros([1, stride, dim], dtype=tensor.dtype)
self._array_tensor = tf.concat([pad, tensor], 0)
if array is not None:
check.IsNone(tensor, 'Cannot initialize from both tensor and array')
with tf.name_scope('convert_to_bulk'):
self._bulk_tensor = convert_network_state_tensorarray(array)
with tf.name_scope('convert_to_dyn'):
self._array_tensor = array.stack()