我想使用Keras CNN构建二进制分类器。 我有大约6000行输入数据,如下所示:
>> print(X_train[0])
[[[-1.06405307 -1.06685851 -1.05989663 -1.06273152]
[-1.06295958 -1.06655996 -1.05969803 -1.06382503]
[-1.06415248 -1.06735609 -1.05999593 -1.06302975]
[-1.06295958 -1.06755513 -1.05949944 -1.06362621]
[-1.06355603 -1.06636092 -1.05959873 -1.06173742]
[-1.0619655 -1.06655996 -1.06039312 -1.06412326]
[-1.06415248 -1.06725658 -1.05940014 -1.06322857]
[-1.06345662 -1.06377347 -1.05890365 -1.06034568]
[-1.06027557 -1.06019084 -1.05592469 -1.05537518]
[-1.05550398 -1.06038988 -1.05225064 -1.05676692]]]
>>> print(y_train[0])
[1]
然后我通过这种方式建立CNN:
model = Sequential()
model.add(Convolution1D(input_shape = (10, 4),
nb_filter=16,
filter_length=4,
border_mode='same'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.2))
model.add(Convolution1D(nb_filter=8,
filter_length=4,
border_mode='same'))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(64))
model.add(BatchNormalization())
model.add(LeakyReLU())
model.add(Dense(1))
model.add(Activation('softmax'))
reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.9, patience=30, min_lr=0.000001, verbose=0)
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train, y_train,
nb_epoch = 100,
batch_size = 128,
verbose=0,
validation_data=(X_test, y_test),
callbacks=[reduce_lr],
shuffle=True)
y_pred = model.predict(X_test)
但它返回以下内容:
>> print(confusion_matrix(y_test, y_pred))
[[ 0 362]
[ 0 608]]
答案 0 :(得分:9)
由于网络中的输出,它始终会预测一个。你有一个带有一个神经元的密集层,并且有一个Softmax激活。 Softmax通过每个输出的指数之和进行归一化。由于有一个输出,唯一可能的输出是1.0。
对于二元分类器,您可以使用sigmoid激活" binary_crossentropy"丢失,或在最后一层放置两个输出单位,继续使用softmax并将损失更改为categorical_crossentropy。