Question

我正在寻找一种在每个时期（或每n个时期）重新开始随机分配一部分权重的方法，我发现this解释了如何重新初始化层。我可以使用

weights = layer.get_weights()

，然后使用numpy操作以重新初始化一部分权重，或创建一个虚拟层，从其中提取新的初始化权重并将其与set_weights一起使用。我正在寻找一种更优雅的方法来将我的权重的某个（或随机）部分初始化为一个层。

谢谢

Answer 1

Keras具有set_weights方法来设置图层的权重。要在每个纪元处重置图层的权重，请使用回调。

class My_Callback(keras.callbacks.Callback):

    def on_epoch_begin(self, logs={}):
        return

    def on_epoch_end(self, epoch, logs={}):
        layer_index = 0  ## index of the layer you want to change
        # random weights to reset the layer
        new_weights = numpy.random.randn(*self.model.layers[layer_index].get_weights().shape)

        self.model.layers[layer_index].set_weights(new_weights)

编辑：

要重置层的随机n个权重，可以使用numpy来获取要重置的随机索引。现在的代码是

    def on_epoch_end(self, epoch, logs={}):
        layer_index = np.random.randint(len(self.model.layers)) # Random layer index to reset
        weights_shape = self.model.layers.get_weights().shape
        num = 10 # number of weights to reset
        indexes = np.random.choice(weights_shape[0], num, replace=False)   # indexes of the layer to reset
        reset_weights = numpy.random.randn(*weights_shape[1:]) # random weights to reset the layer

        layer_weights = self.model.layers[layer_index].get_weights()
        layer_weights[indexes] = reset_weights
        self.model.layers[layer_index].set_weights(layer_weights)

类似于重置层的随机权重p %，第一个numpy可用于选择层权重的p %索引。

    def on_epoch_end(self, epoch, logs={}):
        layer_index = np.random.randint(len(self.model.layers)) # Random layer index to reset
        weights_shape = self.model.layers.get_weights().shape
        percent = 10 # Percentage of weights to reset
        indexes = np.random.choice(weights_shape[0], int(percent/100.) * weights_shape[0], replace=False)   # indexes of the layer  to reset
        reset_weights = numpy.random.randn(*weights_shape[1:]) # random weights to reset the layer

        layer_weights = self.model.layers[layer_index].get_weights()
        layer_weights[indexes] = reset_weights
        self.model.layers[layer_index].set_weights(layer_weights)

keras层在每个时期重新启动部分权重

1 个答案:

编辑：