keras搭建的cnn无法直接第二次重新运行，需要重启之后才能运行

def wdcnn(filters1,kernel_size1,strides1,conv_padding1,pool_padding1,pool_size1,BatchNorm1):

model.add(Conv1D(filters=filters1,kernel_size=kernel_size1,strides=strides1,padding=conv_padding1,
                  kernel_regularizer=l2(1e-4)))
if BatchNorm1:
    model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D(pool_size=pool_size1,padding=pool_padding1))
return model

实例化序贯模型

model = Sequential()

搭建输入层，第一层卷积。

model.add(Conv1D(filters=16,kernel_size=32,strides=4,padding='same',
kernel_regularizer=l2(1e-4),input_shape=input_shape1))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D(pool_size=2))
model = wdcnn(filters1=32, kernel_size1=3, strides1=1, conv_padding1='same',
pool_padding1='valid', pool_size1=2, BatchNorm1=BatchNorm)
model = wdcnn(filters1=64, kernel_size1=3, strides1=1, conv_padding1='same',
pool_padding1='valid', pool_size1=2, BatchNorm1=BatchNorm)
model.add(Flatten())
model.add(Dense(units=50,activation='relu',kernel_regularizer=l2(1e-4)))
model.add(Dense(units=num_classes,activation='softmax',kernel_regularizer=l2(1e-4)))
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])

训练完成之后，第二次run，会显示You must feed a value for placeholder tensor 'conv1d_4_input' with dtype float and shape 这样的报错，但是重启又可以运行，是为什么呢？感觉我的数据没有问题