93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
import torch.nn as nn
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import torch
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class LSTM(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, num_classes, device):
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super(LSTM, self).__init__()
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self.device = device
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.lstm = nn.LSTM(input_size, hidden_size,
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num_layers, batch_first=True)
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self.fc = nn.Sequential(
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nn.ReLU(),
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nn.Dropout(p=0.3),
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nn.Linear(hidden_size, num_classes)
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)
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def forward(self, x):
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# Set initial hidden and cell states
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h0 = torch.zeros(self.num_layers, x.size(0),
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self.hidden_size).to(self.device)
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c0 = torch.zeros(self.num_layers, x.size(0),
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self.hidden_size).to(self.device)
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# Forward propagate LSTM
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# out: tensor of shape (batch_size, seq_length, hidden_size)
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out, _ = self.lstm(x, (h0, c0))
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# Decode the hidden state of the last time step
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# 这里只取了最后一个time step
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out = self.fc(out[:, -1, :])
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return out
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class GRU(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, num_classes, device):
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super(GRU, self).__init__()
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self.device = device
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.gru = nn.GRU(input_size, hidden_size,
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num_layers, batch_first=True)
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self.fc = nn.Sequential(
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nn.ReLU(),
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nn.Dropout(p=0.3),
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nn.Linear(hidden_size, num_classes)
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)
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def forward(self, x):
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# Set initial hidden and cell states
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h0 = torch.zeros(self.num_layers, x.size(0),
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self.hidden_size).to(self.device)
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# Forward propagate GRU
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# out: tensor of shape (batch_size, seq_length, hidden_size)
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out, _ = self.gru(x, h0)
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# Decode the hidden state of the last time step
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# 这里只取了最后一个time step
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out = self.fc(out[:, -1, :])
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return out
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class RNN(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, num_classes, device):
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super(RNN, self).__init__()
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self.device = device
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.rnn = nn.RNN(input_size, hidden_size,
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num_layers, batch_first=True)
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self.fc = nn.Sequential(
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nn.ReLU(),
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nn.Dropout(p=0.3),
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nn.Linear(hidden_size, num_classes)
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)
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def forward(self, x):
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# Set initial hidden and cell states
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h0 = torch.zeros(self.num_layers, x.size(0),
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self.hidden_size).to(self.device)
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# Forward propagate LSTM
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# out: tensor of shape (batch_size, seq_length, hidden_size)
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out, _ = self.rnn(x, h0)
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# Decode the hidden state of the last time step
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# 这里只取了最后一个time step
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out = self.fc(out[:, -1, :])
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return out
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