您可以使用get_weights和set_weights方法访问并设置模型层的权重或参数.来自 Keras文档:

You can access and set the weights or parameters of the model's layers using get_weights and set_weights methods. From Keras documentation:

layer.get_weights():将图层的权重作为以下项的列表返回 numpy数组. layer.set_weights(weights):设置 Numpy数组列表中的图层(与输出的形状相同) get_weights).

layer.get_weights(): returns the weights of the layer as a list of Numpy arrays. layer.set_weights(weights): sets the weights of the layer from a list of Numpy arrays (with the same shapes as the output of get_weights).

每个Keras模型都有一个layers属性，该属性是模型中所有图层的列表.例如，在您提供的样本模型中，您可以通过运行以下命令来获取第一Dense层的权重:

Each Keras model has a layers attribute which is the list of all the layers in the model. For example, in the sample model you provided, you can get the weights of the first Dense layer by running:

model.layers[1].get_weights()

它将返回两个numpy数组的列表:第一个是Dense层的内核参数，第二个数组是bias参数.

It would return a list of two numpy arrays: the first one is the kernel parameters of the Dense layer the second array is the bias parameters.