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paddle.fluid / layers / sampled_softmax_with_cross_entropy
sampled_softmax_with_cross_entropy¶
-
paddle.fluid.layers.
sampled_softmax_with_cross_entropy
( logits, label, num_samples, num_true=1, remove_accidental_hits=True, use_customized_samples=False, customized_samples=None, customized_probabilities=None, seed=0 ) [源代码] ¶
Sampled Softmax With Cross Entropy Operator
对于较大的输出类,采样的交叉熵损失Softmax被广泛地用作输出层。该运算符为所有示例采样若干个样本,并计算每行采样张量的SoftMax标准化值,然后计算交叉熵损失。
由于此运算符在内部对逻辑执行SoftMax,因此它需要未分级的逻辑。此运算符不应与SoftMax运算符的输出一起使用,因为这样会产生不正确的结果。
对于T真标签(T>=1)的示例,我们假设每个真标签的概率为1/T。对于每个样本,使用对数均匀分布生成S个样本。真正的标签与这些样本连接起来,形成每个示例的T+S样本。因此,假设逻辑的形状是[N x K],样本的形状是[N x(T+S)]。对于每个取样标签,计算出一个概率,对应于[Jean et al., 2014]( http://arxiv.org/abs/1412.2007 )中的Q(y|x)。
根据采样标签对逻辑进行采样。如果remove_accidental_hits为“真”,如果sample[i, j] 意外匹配“真”标签,则相应的sampled_logits[i, j]减去1e20,使其SoftMax结果接近零。然后用logQ(y|x)减去采样的逻辑,这些采样的逻辑和重新索引的标签被用来计算具有交叉熵的SoftMax。
- 参数:
-
logits (Variable)- 非比例对数概率,是一个二维张量,形状为[N x K]。N是批大小,K是类别号。
label (Variable)- 基本事实,是一个二维张量。label是一个张量<int64>,其形状为[N x T],其中T是每个示例的真实标签数。
num_samples (int)- 每个示例的数目num_samples应该小于类的数目。
num_true (int)- 每个训练实例的目标类别总数。
remove_accidental_hits (bool)- 指示采样时是否删除意外命中的标签。如果为真,如果一个sample[i,j]意外地碰到了真标签,那么相应的sampled_logits[i,j]将被减去1e20,使其SoftMax结果接近零。默认值为True。
use_customized_samples (bool)- 是否使用自定义样本和可能性对logits进行抽样。
customized_samples (Variable)- 用户定义的示例,它是一个具有形状[N, T + S]的二维张量。S是num_samples,T是每个示例的真标签数。
customized_probabilities (Variable)- 用户定义的样本概率,与customized_samples形状相同的二维张量。
seed (int)- 用于生成随机数的随机种子,在采样过程中使用。默认值为0。
返回:交叉熵损失,是一个二维张量,形状为[N x 1]。
返回类型:Variable
代码示例:
import paddle.fluid as fluid
input = fluid.layers.data(name='data', shape=[256], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
fc = fluid.layers.fc(input=input, size=100)
out = fluid.layers.sampled_softmax_with_cross_entropy(
logits=fc, label=label, num_samples=25)
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