标签:layers kernel keras 现象 model LocallyConnected2D momentum size
只是先记录下
keras LocallyConnected2D 连续建4层(或者更少),就可能会出现模型编译时间超长,狂占GPU显存的问题。原因没有找到。
input = layers.Input(shape = (window_size, factor_num, 1))
model = layers.LocallyConnected2D(8, kernel_size = (1,1))(input)
model = layers.BatchNormalization(axis=-1, momentum=momentum)(model)
model = layers.Activation("relu")(model)
model = layers.LocallyConnected2D(8, kernel_size = (1,1))(model)
model = layers.BatchNormalization(axis=-1, momentum=momentum)(model)
model = layers.Activation("relu")(model)
model = layers.LocallyConnected2D(1, kernel_size = (1,factor_num))(model)
model = layers.BatchNormalization(axis=-1, momentum=momentum)(model)
model = layers.Activation("relu")(model)
model = layers.Reshape((-1,))(model)
model = dense(model,32)
model = layers.Dense(1)(model)
model = Model(inputs = input, outputs = model)
model.compile(loss = "mse", optimizer = opt, metrics = [r_square])
标签:layers,kernel,keras,现象,model,LocallyConnected2D,momentum,size 来源: https://blog.csdn.net/u010048197/article/details/122782509
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