如何为 keras 使用自定义损失函数答案

【问题标题】：How to use custom loss function for keras如何为 keras 使用自定义损失函数
【发布时间】：2020-11-26 09:16:48
【问题描述】：

我最近遇到了Focal loss function，听说主要用于不平衡数据集。所以我只是通过使用我在网上找到的这个简单的焦点损失函数（对于 Keras）来尝试 Cifar10 数据集。

我一直面临着我最后提到的错误。我尝试了几种方法来解决它，但没有运气。请注意，我非常感谢您的帮助。谢谢！

焦点损失

import keras.backend as K

ALPHA = 0.8
GAMMA = 2

def FocalLoss(targets, inputs, alpha=ALPHA, gamma=GAMMA):    
    
    inputs = K.flatten(inputs)
    targets = K.flatten(targets)
    
    BCE = K.binary_crossentropy(targets, inputs)
    BCE_EXP = K.exp(-BCE)
    focal_loss = K.mean(alpha * K.pow((1-BCE_EXP), gamma) * BCE)
    
    return focal_loss

输入数据

from keras.datasets import cifar10

(xtrain,ytrain),(xtest,ytest) = cifar10.load_data()

神经网络

from keras.layers import Dense, Conv2D, Flatten, MaxPool2D
from keras.models import Sequential
from keras.optimizers import Adam

model = Sequential([
      Conv2D(filters=64, kernel_size=(27,27), strides=(1,1), input_shape=(32,32,3),padding='same', activation='sigmoid'),
      MaxPool2D(pool_size=(13,13), strides=(1,1), padding='valid'),
      Conv2D(filters=32,  kernel_size=(11,11), strides=(1,1), padding='valid', activation='sigmoid'),
      Flatten(),
      Dense(units=600, activation='sigmoid'),
      Dense(units=128, activation='sigmoid'),
      Dense(units=10, activation='softmax')

])

编译和拟合

model.compile(loss=FocalLoss, optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])
model.fit(xtrain, ytrain, epochs=10, batch_size=120, validation_data=(xtest,ytest), verbose=2)

拟合时出错

Epoch 1/10
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-52-52246069690d> in <module>()
----> 1 model.fit(xtrain, ytrain, epochs=10, batch_size=120, validation_data=(xtest,ytest), verbose=2)

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

TypeError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    <ipython-input-50-e8cbeb45fe58>:12 FocalLoss  *
        BCE = K.binary_crossentropy(targets, inputs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper  **
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4829 binary_crossentropy
        bce = target * math_ops.log(output + epsilon())
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1141 binary_op_wrapper
        raise e
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1125 binary_op_wrapper
        return func(x, y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1457 _mul_dispatch
        return multiply(x, y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:509 multiply
        return gen_math_ops.mul(x, y, name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:6176 mul
        "Mul", x=x, y=y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:506 _apply_op_helper
        inferred_from[input_arg.type_attr]))

    TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type uint8 of argument 'x'.

注意

xtrain 和 ytrain 都在相同的 dtype 中。（即）'uint8'

【问题讨论】：

标签： python tensorflow machine-learning keras deep-learning

【解决方案1】：

问题与您的目标类型有关，它们是int8，但您需要将其转换为float32。我在损失内做，我也删除了扁平部分，这是一个错误

def FocalLoss(targets, inputs, alpha=ALPHA, gamma=GAMMA):    
    
    targets = K.cast(targets, 'float32')

    BCE = K.binary_crossentropy(targets, inputs)
    BCE_EXP = K.exp(-BCE)
    focal_loss = K.mean(alpha * K.pow((1-BCE_EXP), gamma) * BCE)
    
    return focal_loss

这里是正在运行的笔记本：https://colab.research.google.com/drive/1E89tggfCvifuoJRdGuXTHuBQPvXFCYN4?usp=sharing

【讨论】：