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import torch
import torch.nn as nn
import math
class EnhancedLSTM(nn.Module):
def __init__(self, input_size, lstm_layer_sizes, linear_layer_size, output_size):
super(EnhancedLSTM, self).__init__()
self.input_size = input_size
self.lstm_layers = nn.ModuleList()
for i in range(len(lstm_layer_sizes)):
if i == 0:
self.lstm_layers.append(nn.LSTM(input_size, lstm_layer_sizes[i], batch_first=True, bidirectional=True))
else:
self.lstm_layers.append(nn.LSTM(lstm_layer_sizes[i-1]*2, lstm_layer_sizes[i], batch_first=True, bidirectional=True))
self.dropout = nn.Dropout(0.3)
self.fc_layers = nn.ModuleList()
for i in range(len(linear_layer_size)):
if i == 0:
self.fc_layers.append(nn.Linear(lstm_layer_sizes[-1]*2, linear_layer_size[i]))
else:
self.fc_layers.append(nn.Linear(linear_layer_size[i-1], linear_layer_size[i]))
self.output_layer = nn.Linear(linear_layer_size[-1], output_size)
self.relu = nn.ReLU()
self.apply(self.initialize_weights)
def forward(self, x):
out = x
for lstm in self.lstm_layers:
out, (hn, cn) = lstm(out)
out = self.dropout(out[:, -1, :])
for fc in self.fc_layers:
out = self.relu(fc(out))
out = self.output_layer(out)
return out
def initialize_weights(self, layer):
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
nn.init.zeros_(layer.bias)
elif isinstance(layer, nn.LSTM):
for name, param in layer.named_parameters():
if 'weight' in name:
nn.init.xavier_uniform_(param.data)
elif 'bias' in name:
nn.init.zeros_(param.data)
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