如何将张量流对象包装为Keras层? [英] How to wrap a tensorflow object as Keras layer?

问题描述

我想将多层多尺度LSTM实现为Keras层.
它在此处发布，并在tensorflow

I would like to implement Hierarchical Multiscale LSTM as a Keras layer.
It was published here and implemented in tensorflow here.
My understanding is that there's a way to wrap such a tensorflow object in Keras as a layer. I'm not sure how complicated it is but I think it's feasible. Can you help me how to do it?

推荐答案

通常由 keras .engine.topology.layer 并为以下方法提供自定义实现(并将TensorFlow代码放入其中):

This is usually done by implementing a custom Layer. To be more specific, you should inherit from keras.engine.topology.layer and provide a custom implementation for the following methods (and place the TensorFlow code within them):

• build(input_shape):这是定义权重的地方.此方法必须设置self.built = True，可以通过调用 super([Layer], self).build()
• call(x):这是层的逻辑所在.除非您希望您的图层支持遮罩，否则您只需要关心第一个 传递给call的参数:输入张量.
• compute_output_shape(input_shape):如果您的图层修改了其输入的形状，则应在此处指定形状 转换逻辑.这允许Keras进行自动造型 推断.

• build(input_shape): this is where you will define your weights. This method must set self.built = True, which can be done by calling super([Layer], self).build()
• call(x): this is where the layer's logic lives. Unless you want your layer to support masking, you only have to care about the first argument passed to call: the input tensor.
• compute_output_shape(input_shape): in case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference.

由于您尝试实现循环层，因此直接从

Since you're trying to implement a recurrent layer, it would also be convenient to inherit directly from keras.legacy.layers.recurrent. In this case, you probably do not need to redefine compute_output_shape(input_shape). If your layer needs additional arguments, you can pass them to the __init__ method of your custom layer.

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