keras 模型中的平均权重

问题描述

当我用不同的初始化训练几个具有相同架构的模型时，如何在 Keras 模型中平均权重?

How to average weights in Keras models, when I train few models with the same architecture with different initialisations?

现在我的代码看起来像这样?

Now my code looks something like this?

datagen = ImageDataGenerator(rotation_range=15,
                             width_shift_range=2.0/28,
                             height_shift_range=2.0/28
                            )

epochs = 40 
lr = (1.234e-3)
optimizer = Adam(lr=lr)

main_input = Input(shape= (28,28,1), name='main_input')

sub_models = []

for i in range(5):

    x = Conv2D(32, kernel_size=(3,3), strides=1)(main_input)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPool2D(pool_size=2)(x)

    x = Conv2D(64, kernel_size=(3,3), strides=1)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPool2D(pool_size=2)(x)

    x = Conv2D(64, kernel_size=(3,3), strides=1)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Flatten()(x)

    x = Dense(1024)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Dropout(0.1)(x)

    x = Dense(256)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Dropout(0.4)(x)

    x = Dense(10, activation='softmax')(x)

    sub_models.append(x)

x = keras.layers.average(sub_models)

main_output = keras.layers.average(sub_models)

model = Model(inputs=[main_input], outputs=[main_output])

model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
              optimizer=optimizer)

print(model.summary())

plot_model(model, to_file='model.png')

filepath="weights.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
tensorboard = TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)
callbacks = [checkpoint, tensorboard]

model.fit_generator(datagen.flow(X_train, y_train, batch_size=128),
                    steps_per_epoch=len(X_train) / 128,
                    epochs=epochs,
                    callbacks=callbacks,
                    verbose=1,
                    validation_data=(X_test, y_test))

所以现在我只平均最后一层，但我想在单独训练每一层后平均所有层的权重.

So now I average only last layer, but I want to average weights in all layers after training each one separately.

谢谢！

推荐答案

所以让我们假设 models 是您的模型的集合.首先 - 收集所有权重:

So let's assume that models is a collection of your models. First - collect all weights:

weights = [model.get_weights() for model in models]

现在 - 创建一个新的平均权重:

Now - create a new averaged weights:

new_weights = list()

for weights_list_tuple in zip(*weights):
    new_weights.append(
        [numpy.array(weights_).mean(axis=0)
            for weights_ in zip(*weights_list_tuple)])

剩下的就是在新模型中设置这些权重:

And what is left is to set these weights in a new model:

new_model.set_weights(new_weights)

当然 - 平均权重可能不是一个好主意，但如果您尝试 - 您应该遵循这种方法.

Of course - averaging weights might be a bad idea, but in case you try - you should follow this approach.

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