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import torch
import torch.nn as nn
class LSTMClassifier(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, dropout, output_size=1):
super(LSTMClassifier, self).__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, dropout=dropout, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
h_0 = torch.zeros(self.lstm.num_layers, x.size(0), self.lstm.hidden_size).to(x.device)
c_0 = torch.zeros(self.lstm.num_layers, x.size(0), self.lstm.hidden_size).to(x.device)
out, _ = self.lstm(x, (h_0, c_0))
out = self.fc(out[:, -1, :])
out = torch.sigmoid_(out)
return out
class LSTMClassifierB(nn.Module):
def __init__(self, input_size, hidden_size, dropout, num_layers):
super(LSTMClassifierB, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, dropout=dropout, batch_first=True)
fc_layers = []
input_dim = hidden_size
fc_layers.append(nn.Linear(input_dim, input_dim))
fc_layers.append(nn.ReLU())
fc_layers.append(nn.Linear(input_dim, 1)) # Output layer for binary classification
self.fc = nn.Sequential(*fc_layers)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
out, _ = self.lstm(x, (h0, c0))
out = self.fc(out[:, -1, :]) # Only take the output from the last time step
out = torch.sigmoid(out) # Apply sigmoid activation
return out
class LSTMClassifierC(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, sequence_length):
super(LSTMClassifierC, self).__init__()
self.hidden_size = hidden_size # Number of features in the hidden state
self.num_layers = num_layers # Number of recurrent layers in the LSTM
self.sequence_length = sequence_length # Length of the input sequences
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
# Single linear layer after flattening the LSTM output
self.fc = nn.Linear(hidden_size * sequence_length, 1)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
out, _ = self.lstm(x, (h0, c0))
out = out.reshape(out.size(0), -1) # Flatten the sequence
out = self.fc(out) # Pass through the single linear layer
out = torch.sigmoid(out) # Apply sigmoid activation
return out
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