Keras多类别分类概率总和不超过1 [英] Keras multiclass classification probabilities do not sum up to 1

问题描述

在使用以下Keras网络训练和分类9个班级时:

When using the following Keras network to train and classify 9 classes:

from keras.models import Model
from keras.layers import Convolution1D, Input, Dropout, GlobalMaxPooling1D, Dense, merge

input_window3 = Input(shape=(MEANLEN, W2VLEN))
input_window4 = Input(shape=(MEANLEN, W2VLEN))

conv_w3 = Convolution1D(MEANLEN*2, 3, activation='tanh', border_mode='valid')(input_window3)
drop_w3 = Dropout(0.7)(conv_w3),
pool_w3 = GlobalMaxPooling1D(name='pool_w3')(drop_w3[0])

conv_w4 = Convolution1D(MEANLEN, 5, activation='tanh', border_mode='valid')(input_window4)

drop_w4 = Dropout(0.7)(conv_w4),
pool_w4 = GlobalMaxPooling1D(name='pool_w4')(drop_w4[0])
print(conv_w4.shape)

x = merge([pool_w3, pool_w4], mode='concat', concat_axis=1)
print(x.shape)
x = Dense(MEANLEN*3, activation='relu')(x)
drop_dense = Dropout(0.5)(x)
main_output = Dense(num_categories, activation='sigmoid', name='main_output')(drop_dense)
model = Model(input=[input_window3, input_window4], output=[main_output])
model.compile(optimizer='adam', loss='mse', metrics=['accuracy', f1_score])

预测:

result = model.predict([X_test, X_test])

将与向量相似的向量数组返回:

returns arrays of vectors simillar to these ones:

array([[ 0.08401331,  0.1911521 ,  0.14310306,  0.07138534,  0.19428432,
         0.15808958,  0.16400988,  0.27708355,  0.09983496],
       [ 0.02074078,  0.08897329,  0.03244834,  0.00112842,  0.04122255,
         0.03494435,  0.17535761,  0.55671334,  0.04375785],
       [ 0.04897207,  0.06169643,  0.00313113,  0.002085  ,  0.00275023,
         0.00131959,  0.09961601,  0.56414878,  0.02338091]], dtype=float32)

我认为是类概率的数组中的值不能总计为1.如何获得类概率?

Values in arrays, that I assume to be class probabilities, do not sum up to 1. How to get class probabilities?

推荐答案

基于发布的数组，您有9个类别.在这种情况下，您应该使用 softmax 代替Sigmoid来替换最终的激活功能.另外，如果您还没有做过，则需要将标签转换为一键向量.您可以使用 to_categorical 来实现.最后，作为损失函数，应使用 categorical_crossentropy 损失，而不是mse. 此处 a>.

Based on the array that you posted, you have 9 categories. In such case, you should replace your final activation function with softmax instead of sigmoid. In addition, if you haven't done it yet, you need to transform your labels into one-hot vectors. You can do that using the function to_categorical. Finally, as a loss function, you should use categorical_crossentropy loss, instead of mse. A tutorial on using keras for classification (using the functions mentioned above) is provided here.

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