如何在 keras 中实现自定义指标?

问题描述

我收到此错误:

sum() 得到一个意外的关键字参数 'out'

当我运行这段代码时:

import pandas as pd, numpy as np
import keras
from keras.layers.core import Dense, Activation
from keras.models import Sequential

def AUC(y_true,y_pred):
    not_y_pred=np.logical_not(y_pred)
    y_int1=y_true*y_pred
    y_int0=np.logical_not(y_true)*not_y_pred
    TP=np.sum(y_pred*y_int1)
    FP=np.sum(y_pred)-TP
    TN=np.sum(not_y_pred*y_int0)
    FN=np.sum(not_y_pred)-TN
    TPR=np.float(TP)/(TP+FN)
    FPR=np.float(FP)/(FP+TN)
    return((1+TPR-FPR)/2)

# Input datasets

train_df = pd.DataFrame(np.random.rand(91,1000))
train_df.iloc[:,-2]=(train_df.iloc[:,-2]>0.8)*1


model = Sequential()
model.add(Dense(output_dim=60, input_dim=91, init="glorot_uniform"))
model.add(Activation("sigmoid"))
model.add(Dense(output_dim=1, input_dim=60, init="glorot_uniform"))
model.add(Activation("sigmoid"))

model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=[AUC])


train_df.iloc[:,-1]=np.ones(train_df.shape[0]) #bias
X=train_df.iloc[:,:-1].values
Y=train_df.iloc[:,-1].values
print X.shape,Y.shape

model.fit(X, Y, batch_size=50,show_accuracy = False, verbose = 1)

除了循环批处理和编辑源代码之外，是否可以实现自定义指标?

Is it possible to implement a custom metric aside from doing a loop on batches and editing the source code?

推荐答案

问题在于 y_pred 和 y_true 不是 NumPy 数组，而是 Theano 或 TensorFlow 张量.这就是您收到此错误的原因.

The problem is that y_pred and y_true are not NumPy arrays but either Theano or TensorFlow tensors. That's why you got this error.

您可以定义自定义指标，但必须记住它的参数是那些张量，而不是 NumPy 数组.

You can define your custom metrics but you have to remember that its arguments are those tensors – not NumPy arrays.

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