本文整理汇总了Python中tensorflow.python.ops.nn_ops.softmax_cross_entropy_with_logits方法的典型用法代码示例。如果您正苦于以下问题：Python nn_ops.softmax_cross_entropy_with_logits方法的具体用法？Python nn_ops.softmax_cross_entropy_with_logits怎么用？Python nn_ops.softmax_cross_entropy_with_logits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tensorflow.python.ops.nn_ops的用法示例。
在下文中一共展示了nn_ops.softmax_cross_entropy_with_logits方法的5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: _log_prob
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 别名]
def _log_prob(self, x):
x = self._assert_valid_sample(x)
# broadcast logits or x if need be.
logits = self.logits
if (not x.get_shape().is_fully_defined() or
not logits.get_shape().is_fully_defined() or
x.get_shape() != logits.get_shape()):
logits = array_ops.ones_like(x, dtype=logits.dtype) * logits
x = array_ops.ones_like(logits, dtype=x.dtype) * x
logits_shape = array_ops.shape(math_ops.reduce_sum(logits, -1))
logits_2d = array_ops.reshape(logits, [-1, self.event_size])
x_2d = array_ops.reshape(x, [-1, self.event_size])
ret = -nn_ops.softmax_cross_entropy_with_logits(labels=x_2d,
logits=logits_2d)
# Reshape back to user-supplied batch and sample dims prior to 2D reshape.
ret = array_ops.reshape(ret, logits_shape)
return ret 示例2: _log_prob
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 别名]
def _log_prob(self, x):
x = ops.convert_to_tensor(x, name="x")
# broadcast logits or x if need be.
logits = self.logits
if (not x.get_shape().is_fully_defined() or
not logits.get_shape().is_fully_defined() or
x.get_shape() != logits.get_shape()):
logits = array_ops.ones_like(x, dtype=logits.dtype) * logits
x = array_ops.ones_like(logits, dtype=x.dtype) * x
logits_shape = array_ops.shape(logits)
if logits.get_shape().ndims == 2:
logits_2d = logits
x_2d = x
else:
logits_2d = array_ops.reshape(logits, [-1, self.num_classes])
x_2d = array_ops.reshape(x, [-1, self.num_classes])
ret = -nn_ops.softmax_cross_entropy_with_logits(labels=x_2d,
logits=logits_2d)
ret = array_ops.reshape(ret, logits_shape)
return ret 示例3: test_binary_ops
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 别名]
def test_binary_ops(self):
ops = [
('sigmoid_cross_entropy_with_logits',
nn_impl.sigmoid_cross_entropy_with_logits,
nn.sigmoid_cross_entropy_with_logits),
('softmax_cross_entropy_with_logits',
nn_ops.softmax_cross_entropy_with_logits,
nn.softmax_cross_entropy_with_logits),
('sparse_softmax_cross_entropy_with_logits',
nn_ops.sparse_softmax_cross_entropy_with_logits,
nn.sparse_softmax_cross_entropy_with_logits),
]
for op_name, tf_op, lt_op in ops:
golden_tensor = tf_op(self.original_lt.tensor, self.other_lt.tensor)
golden_lt = core.LabeledTensor(golden_tensor, self.axes)
actual_lt = lt_op(self.original_lt, self.other_lt)
self.assertIn(op_name, actual_lt.name)
self.assertLabeledTensorsEqual(golden_lt, actual_lt) 示例4: _entropy
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 别名]
def _entropy(self):
if self.logits.get_shape().ndims == 2:
logits_2d = self.logits
else:
logits_2d = array_ops.reshape(self.logits, [-1, self.num_classes])
histogram_2d = nn_ops.softmax(logits_2d)
ret = array_ops.reshape(
nn_ops.softmax_cross_entropy_with_logits(labels=histogram_2d,
logits=logits_2d),
self.batch_shape())
ret.set_shape(self.get_batch_shape())
return ret 示例5: _entropy
# 需要导入模块: from tensorflow.python.ops import nn_ops [as 别名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 别名]
def _entropy(self):
if self.logits.get_shape().ndims == 2:
logits_2d = self.logits
else:
logits_2d = array_ops.reshape(self.logits, [-1, self.num_classes])
histogram_2d = nn_ops.softmax(logits_2d)
ret = array_ops.reshape(
nn_ops.softmax_cross_entropy_with_logits(logits_2d, histogram_2d),
self.batch_shape())
ret.set_shape(self.get_batch_shape())
return ret