本文整理汇总了Python中cntk.log方法的典型用法代码示例。如果您正苦于以下问题：Python cntk.log方法的具体用法？Python cntk.log怎么用？Python cntk.log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cntk的用法示例。
在下文中一共展示了cntk.log方法的6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: test_log
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import log [as 别名]
def test_log():
assert_cntk_ngraph_array_equal(C.log([1., 2.]))
assert_cntk_ngraph_array_equal(C.log([[1, 2], [3, 4]]))
assert_cntk_ngraph_array_equal(C.log([[[1, 2], [3, 4]], [[1, 2], [3, 4]]])) 示例2: logsumexp
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import log [as 别名]
def logsumexp(x, axis=None, keepdims=False):
return log(sum(exp(x), axis=axis, keepdims=keepdims)) 示例3: log
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import log [as 别名]
def log(x):
return C.log(x) 示例4: binary_crossentropy
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import log [as 别名]
def binary_crossentropy(target, output, from_logits=False):
if from_logits:
output = C.sigmoid(output)
output = C.clip(output, epsilon(), 1.0 - epsilon())
output = -target * C.log(output) - (1.0 - target) * C.log(1.0 - output)
return output 示例5: categorical_crossentropy
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import log [as 别名]
def categorical_crossentropy(target, output, from_logits=False, axis=-1):
# Here, unlike other backends, the tensors lack a batch dimension:
axis_without_batch = -1 if axis == -1 else axis - 1
output_dimensions = list(range(len(output.shape)))
if axis_without_batch != -1 and axis_without_batch not in output_dimensions:
raise ValueError(
'{}{}{}'.format(
'Unexpected channels axis {}. '.format(axis_without_batch),
'Expected to be -1 or one of the axes of `output`, ',
'which has {} dimensions.'.format(len(output.shape))))
# If the channels are not in the last axis, move them to be there:
if axis_without_batch != -1 and axis_without_batch != output_dimensions[-1]:
permutation = output_dimensions[:axis_without_batch]
permutation += output_dimensions[axis_without_batch + 1:]
permutation += [axis_without_batch]
output = C.transpose(output, permutation)
target = C.transpose(target, permutation)
if from_logits:
result = C.cross_entropy_with_softmax(output, target)
# cntk's result shape is (batch, 1), while keras expect (batch, )
return C.reshape(result, ())
else:
# scale preds so that the class probas of each sample sum to 1
output /= C.reduce_sum(output, axis=-1)
# avoid numerical instability with epsilon clipping
output = C.clip(output, epsilon(), 1.0 - epsilon())
return -sum(target * C.log(output), axis=-1) 示例6: categorical_crossentropy
# 需要导入模块: import cntk [as 别名]
# 或者: from cntk import log [as 别名]
def categorical_crossentropy(target, output, from_logits=False):
if from_logits:
result = C.cross_entropy_with_softmax(output, target)
# cntk's result shape is (batch, 1), while keras expect (batch, )
return C.reshape(result, ())
else:
# scale preds so that the class probas of each sample sum to 1
output /= C.reduce_sum(output, axis=-1)
# avoid numerical instability with epsilon clipping
output = C.clip(output, epsilon(), 1.0 - epsilon())
return -sum(target * C.log(output), axis=-1)