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python - Keras Categorical_crossentropy 损失实现

问题描述

我正在尝试重新实现 Keras 的分类交叉熵损失，以便我可以自定义它。我得到了以下

def CustomCrossEntropy(output, target, axis=-1):
    target = ops.convert_to_tensor_v2_with_dispatch(target)
    output = ops.convert_to_tensor_v2_with_dispatch(output)
    target.shape.assert_is_compatible_with(output.shape)
    # scale preds so that the class probas of each sample sum to 1
    output = output / math_ops.reduce_sum(output, axis, True)
    # Compute cross entropy from probabilities.
    epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)
    output = clip_ops.clip_by_value(output, epsilon_, 1. - epsilon_)
    return -math_ops.reduce_sum(target * math_ops.log(output), axis)

它产生的结果与让我困惑的内部函数不同，因为我到目前为止只是从github复制了代码。我在这里想念什么？

证明：

y_true = [[0., 1., 0.], [0., 0., 1.]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)
customLoss = CustomCrossEntropy(y_true, y_pred)
assert loss.shape == (2,)
print(loss)
print(customLoss)
>>tf.Tensor([0.05129331 2.3025851 ], shape=(2,), dtype=float32)
>>tf.Tensor([ 0.8059049 14.506287 ], shape=(2,), dtype=float32)

标签： pythontensorflowkeras

解决方案

你已经在你的定义中反转了函数的参数CustomCrossEntropy，如果你仔细检查 GitHub 中的源代码，你会发现第一个参数是target，第二个是output. 如果您将它们切换回来，您将获得与预期相同的结果。

import tensorflow as tf
from tensorflow.keras.backend import categorical_crossentropy as CustomCrossEntropy

y_true = [[0., 1., 0.], [0., 0., 1.]]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]

y_true = tf.convert_to_tensor(y_true)
y_pred = tf.convert_to_tensor(y_pred)

loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)
print(loss)
# tf.Tensor([0.05129331 2.3025851 ], shape=(2,), dtype=float32)

loss = CustomCrossEntropy(y_true, y_pred)
print(loss)
# tf.Tensor([0.05129331 2.3025851 ], shape=(2,), dtype=float32)

loss = CustomCrossEntropy(y_pred, y_true)
print(loss)
# tf.Tensor([ 0.8059049 14.506287 ], shape=(2,), dtype=float32)

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