>> 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构建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,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]]

为什么所有的预测都是一个？为什么CNN表现如此糟糕？
以下是损失和acc图表：