本文整理匯總了Python中tensorflow.python.ops.nn_ops.softmax_cross_entropy_with_logits方法的典型用法代碼示例。如果您正苦於以下問題：Python nn_ops.softmax_cross_entropy_with_logits方法的具體用法？Python nn_ops.softmax_cross_entropy_with_logits怎麽用？Python nn_ops.softmax_cross_entropy_with_logits使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類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個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _log_prob
# 需要導入模塊: from tensorflow.python.ops import nn_ops [as 別名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 別名]
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
# 需要導入模塊: from tensorflow.python.ops import nn_ops [as 別名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 別名]
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
# 需要導入模塊: from tensorflow.python.ops import nn_ops [as 別名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 別名]
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
# 需要導入模塊: from tensorflow.python.ops import nn_ops [as 別名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 別名]
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
# 需要導入模塊: from tensorflow.python.ops import nn_ops [as 別名]
# 或者: from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits [as 別名]
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