def get_model(X_input):#重新建立模型，與原來不一樣的是這里inp是傳入n_classes = 5# input_shape = (time_span, feature, 1) # X_input = Input(input_shape)#inp=Input(shape=(120,39))#原來的inp是函數(shù)里，傳入可以三個(gè)公用 # reshape=Reshape((30,5,1))(inp)# pre=ZeroPadding2D(padding=(1, 1))(reshape)# 1#reshape=BatchNormalization()(reshape)conv1=Convolution2D(32, 3, 3, border_mode='same',init='glorot_uniform')(X_input)#model.add(Activation('relu'))l1=PReLU()(conv1)l1=BatchNormalization()(l1)conv2=ZeroPadding2D(padding=(1, 1))(l1)conv2=Convolution2D(32, 3, 3, border_mode='same',init='glorot_uniform')(conv2)#model.add(Activation('relu'))l2=PReLU()(conv2)l2=BatchNormalization()(l2)m2=AveragePooling2D((3, 3), strides=(3, 3))(l2)d2=Dropout(0.25)(m2)# 2conv3=ZeroPadding2D(padding=(1, 1))(d2)conv3=Convolution2D(64, 3, 3, border_mode='same',init='glorot_uniform')(conv3)#model.add(Activation('relu'))l3=PReLU()(conv3)l3=BatchNormalization()(l3)conv4=ZeroPadding2D(padding=(1, 1))(l3)conv4=Convolution2D(64, 3, 3, border_mode='same',init='glorot_uniform')(conv4)#model.add(Activation('relu'))l4=PReLU()(conv4)l4=BatchNormalization()(l4)m4=AveragePooling2D((3, 3), strides=(3, 3))(l4)d4=Dropout(0.25)(m4)g=GlobalAveragePooling2D()(d4)Den=Dense(1024)(g)#model.add(Activation('relu'))ld=PReLU()(Den)ld=Dropout(0.5)(ld)result=Dense(n_classes, activation='softmax')(ld)# result=gmodel=Model(inputs=X_input,outputs=result)return model

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