本文整理汇总了Python中tensorflow.random_flip_up_down方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow.random_flip_up_down方法的具体用法？Python tensorflow.random_flip_up_down怎么用？Python tensorflow.random_flip_up_down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的用法示例。
在下文中一共展示了tensorflow.random_flip_up_down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: flip_dim
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import random_flip_up_down [as 别名]
def flip_dim(tensor_list, prob=0.5, dim=1):
"""Randomly flips a dimension of the given tensor.
The decision to randomly flip the `Tensors` is made together. In other words,
all or none of the images pass in are flipped.
Note that tf.random_flip_left_right and tf.random_flip_up_down isn't used so
that we can control for the probability as well as ensure the same decision
is applied across the images.
Args:
tensor_list: A list of `Tensors` with the same number of dimensions.
prob: The probability of a left-right flip.
dim: The dimension to flip, 0, 1, ..
Returns:
outputs: A list of the possibly flipped `Tensors` as well as an indicator
`Tensor` at the end whose value is `True` if the inputs were flipped and
`False` otherwise.
Raises:
ValueError: If dim is negative or greater than the dimension of a `Tensor`.
"""
random_value = tf.random_uniform([])
def flip():
flipped = []
for tensor in tensor_list:
if dim < 0 or dim >= len(tensor.get_shape().as_list()):
raise ValueError('dim must represent a valid dimension.')
flipped.append(tf.reverse_v2(tensor, [dim]))
return flipped
is_flipped = tf.less_equal(random_value, prob)
outputs = tf.cond(is_flipped, flip, lambda: tensor_list)
if not isinstance(outputs, (list, tuple)):
outputs = [outputs]
outputs.append(is_flipped)
return outputs 示例2: flip_dim
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import random_flip_up_down [as 别名]
def flip_dim(tensor_list, prob=0.5, dim=1):
"""Randomly flips a dimension of the given tensor.
The decision to randomly flip the `Tensors` is made together. In other words,
all or none of the images pass in are flipped.
Note that tf.random_flip_left_right and tf.random_flip_up_down isn't used so
that we can control for the probability as well as ensure the same decision
is applied across the images.
Args:
tensor_list: A list of `Tensors` with the same number of dimensions.
prob: The probability of a left-right flip.
dim: The dimension to flip, 0, 1, ..
Returns:
outputs: A list of the possibly flipped `Tensors` as well as an indicator
`Tensor` at the end whose value is `True` if the inputs were flipped and
`False` otherwise.
Raises:
ValueError: If dim is negative or greater than the dimension of a `Tensor`.
"""
random_value = tf.random_uniform([])
def flip():
flipped = []
for tensor in tensor_list:
if dim < 0 or dim >= len(tensor.get_shape().as_list()):
raise ValueError('dim must represent a valid dimension.')
flipped.append(tf.reverse_v2(tensor, [dim]))
return flipped
is_flipped = tf.less_equal(random_value, prob)
outputs = tf.cond(is_flipped, flip, lambda: tensor_list)
if not isinstance(outputs, (list, tuple)):
outputs = [outputs]
outputs.append(is_flipped)
return outputs 示例3: flip_dim
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import random_flip_up_down [as 别名]
def flip_dim(tensor_list, prob=0.5, dim=1):
"""Randomly flips a dimension of the given tensor.
The decision to randomly flip the `Tensors` is made together.
In other words, all or none of the images pass in are flipped.
Note that tf.random_flip_left_right and tf.random_flip_up_down isn't used
so that we can control for the probability as well as ensure the
same decision is applied across the images.
Args:
tensor_list: A list of `Tensors` with the same number of dimensions.
prob: The probability of a left-right flip.
dim: The dimension to flip, 0, 1, ..
Returns:
outputs: A list of the possibly flipped `Tensors` as well as an indicator
`Tensor` at the end whose value is `True` if the inputs were flipped and
`False` otherwise.
Raises:
ValueError: If dim is negative or greater than dimension of a `Tensor`.
"""
random_value = tf.random_uniform([])
def flip():
flipped = []
for tensor in tensor_list:
if dim < 0 or dim >= len(tensor.get_shape().as_list()):
raise ValueError('dim must represent a valid dimension.')
flipped.append(tf.reverse_v2(tensor, [dim]))
return flipped
is_flipped = tf.less_equal(random_value, prob)
outputs = tf.cond(is_flipped, flip, lambda: tensor_list)
if not isinstance(outputs, (list, tuple)):
outputs = [outputs]
outputs.append(is_flipped)
return outputs