Awesome question! Customizing an LSTM cell's internal mechanics is super common when experimenting with new sequence modeling ideas—let’s walk through exactly how to pull this off in Keras, plus cover other frameworks that might fit your needs better.

Keras Implementation: What to Overwrite & Source Files #

If you're using TensorFlow Keras (the de facto standard these days), here's what you need to know:

Key Classes & Methods to Overwrite #

• Base Class: You'll want to inherit from keras.layers.LSTMCell (or the more generic keras.layers.Layer if you want to build from scratch).
• call() Method: This is the core—all the standard LSTM gate calculations (input gate, forget gate, output gate, cell state update) live here. Overwrite this to replace the default formulas with your custom logic.
• build() Method: If your modified cell needs new trainable weights/biases (e.g., an extra term for the forget gate), use this method to define them (call super().build() first to retain the original weights, then add your own).
• get_config() (Optional): If your cell has custom parameters, overwrite this to make it serializable (so you can save/load models with your custom cell).

Where to Find Keras Source Code #

For TensorFlow Keras, the LSTMCell implementation lives in tensorflow/python/keras/layers/recurrent.py. Take a look at the original call method here to understand exactly how the standard gates are computed—this will give you a blueprint for your modifications.

Quick Example: Modified Forget Gate #

Here's a simple snippet that adds an extra bias term to the forget gate:

import tensorflow as tf
from tensorflow.keras.layers import LSTMCell

class CustomLSTMCell(LSTMCell):
    def __init__(self, units, additional_forget_bias_init="zeros", **kwargs):
        self.additional_forget_bias_init = additional_forget_bias_init
        super().__init__(units, **kwargs)

    def build(self, input_shape):
        # Initialize standard LSTM weights first
        super().build(input_shape)
        # Add our custom forget gate bias
        self.additional_forget_bias = self.add_weight(
            shape=(self.units,),
            initializer=self.additional_forget_bias_init,
            name="additional_forget_bias"
        )

    def call(self, inputs, states, training=None):
        h_prev, c_prev = states  # Previous hidden and cell states
        
        # Grab standard weights from parent class
        kernel = self.kernel
        recurrent_kernel = self.recurrent_kernel
        bias = self.bias

        # Custom gate calculations
        # Input gate
        i = tf.matmul(inputs, kernel[:, :self.units]) + tf.matmul(h_prev, recurrent_kernel[:, :self.units]) + bias[:self.units]
        # Forget gate with extra bias
        f = tf.matmul(inputs, kernel[:, self.units:self.units*2]) + tf.matmul(h_prev, recurrent_kernel[:, self.units:self.units*2]) + bias[self.units:self.units*2] + self.additional_forget_bias
        # Output gate
        o = tf.matmul(inputs, kernel[:, self.units*2:self.units*3]) + tf.matmul(h_prev, recurrent_kernel[:, self.units*2:self.units*3]) + bias[self.units*2:self.units*3]
        # Candidate cell state
        g = tf.matmul(inputs, kernel[:, self.units*3:]) + tf.matmul(h_prev, recurrent_kernel[:, self.units*3:]) + bias[self.units*3:]

        # Custom activations (if needed)
        i = tf.nn.sigmoid(i)
        f = tf.nn.sigmoid(f)
        o = tf.nn.sigmoid(o)
        g = tf.nn.tanh(g)

        # Update cell and hidden states
        c_current = f * c_prev + i * g
        h_current = o * tf.nn.tanh(c_current)

        return h_current, [h_current, c_current]

Alternative Frameworks for Custom LSTMs #

If Keras feels a bit restrictive, these frameworks offer more flexibility for custom cell implementations:

PyTorch #

PyTorch's nn.LSTMCell is designed for easy customization. You can either inherit from nn.LSTMCell or build a completely custom cell from scratch by inheriting nn.Module. The forward method is where you'll define your custom logic, and PyTorch's tensor operations make it straightforward to tweak gate calculations.

Example snippet:

import torch
import torch.nn as nn

class CustomLSTMCell(nn.Module):
    def __init__(self, input_size, hidden_size):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        # Combined layer for all gates (simpler than separate layers)
        self.gate_linear = nn.Linear(input_size + hidden_size, 4 * hidden_size)

    def forward(self, x, hidden):
        h_prev, c_prev = hidden
        # Concatenate input and previous hidden state
        combined = torch.cat([x, h_prev], dim=1)
        # Split into 4 gates: input, forget, output, candidate
        i, f, o, g = torch.chunk(self.gate_linear(combined), 4, dim=1)

        # Custom gate logic (e.g., scaled sigmoid for forget gate)
        i = torch.sigmoid(i)
        f = torch.sigmoid(f * 1.5)  # Scale forget gate logits
        o = torch.sigmoid(o)
        g = torch.tanh(g)

        # Update states
        c_current = f * c_prev + i * g
        h_current = o * torch.tanh(c_current)

        return h_current, (h_current, c_current)

JAX/Flax #

If you prefer functional programming, Flax (built on JAX) offers a pure-functional approach to building RNN cells. You can define your custom LSTM cell as a function with no class inheritance, making it easy to experiment with different gate structures without boilerplate.

MXNet Gluon #

MXNet's gluon.rnn.LSTMCell allows you to overwrite the forward method to modify internal logic. It's particularly useful if you're working with distributed training, as MXNet has strong support for multi-GPU setups.

内容的提问来源于stack exchange，提问作者humble_me