如何使用自定义损失 function 为 keras

Question

I have recently came across the Focal loss function and heard it's mainly used in imbalanced dataset. So i just gave it a try on Cifar10 dataset by using this simple Focal loss function i found online(For Keras).

I am continuously facing an error which i have mentioned at the end. I have tried several methods to resolve it but no luck. Please see to it, i really appreciate your help. Thank you!

Focal Loss

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

Input Data

from keras.datasets import cifar10

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

Neural Net

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')

])

Comipiling and Fitting

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)

Error while fitting

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'.

NOTE

Both xtrain and ytrain are in same dtype. (ie)'uint8'

Answer 1

the problem is related to your target type, they are int8 but you need to cast the to float32 . I do it inside the loss, where I removed also the flatten part which is a mistake

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

here the running notebook: https://colab.research.google.com/drive/1E89tggfCvifuoJRdGuXTHuBQPvXFCYN4?usp=sharing