keras lstm层中的多个内核是什么意思? [英] What is the meaning of multiple kernels in keras lstm layer?

问题描述

在 https://keras.io/layers/recurrent/上，我看到LSTM层具有kernel和recurrent_kernel.它们是什么意思?以我的理解，我们需要一个LSTM单元的4个门的权重.但是，在keras实现中，kernel的形状为(input_dim，4 * units)，recurrent_kernel的形状为(units，4 * units).那么，他们两个都以某种方式实现了大门吗?

On https://keras.io/layers/recurrent/ I see that LSTM layers have a kernel and a recurrent_kernel. What is their meaning? In my understanding, we need weights for the 4 gates of an LSTM cell. However, in keras implementation, kernel has a shape of (input_dim, 4*units) and recurrent_kernel has a shape of (units, 4*units). So, are both of them somehow implementing the gates?

推荐答案

如果我错了，请纠正我，但是如果您看一下LSTM方程式:

Correct me if I'm wrong, but if you take a look at the LSTM equations:

您有4个转换输入的 W 矩阵和4个转换隐藏状态的 U 矩阵.

You have 4 W matrices that transform the input and 4 U matrices that transform the hidden state.

Keras将这4组矩阵保存到kernel和recurrent_kernel权重数组中.从使用它们的代码:

Keras saves these sets of 4 matrices into the kernel and recurrent_kernel weight arrays. From the code that uses them:

self.kernel_i = self.kernel[:, :self.units]
self.kernel_f = self.kernel[:, self.units: self.units * 2]
self.kernel_c = self.kernel[:, self.units * 2: self.units * 3]
self.kernel_o = self.kernel[:, self.units * 3:]

self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2]
self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3]
self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]

显然，这4个矩阵存储在沿第二维连接的权重数组中，这解释了权重数组的形状.

Apparently the 4 matrices are stored inside the weight arrays concatenated along the second dimension, which explains the weight array shapes.

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