58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
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import torch
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import torch.nn as nn
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import numpy as np
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from text_rnn import TextRNN
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from config import BATCH_SIZE, N_EPOCHS, LOSS_AVG_MAX, HIDDEN_SIZE, EMBEDDING_SIZE, N_LAYERS
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from generation import get_batch, evaluate
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def create_parameters(idx_to_char):
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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model = TextRNN(input_size=len(idx_to_char), hidden_size=HIDDEN_SIZE, embedding_size=EMBEDDING_SIZE, n_layers=N_LAYERS, device=device)
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model.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, amsgrad=True)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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optimizer,
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patience=5,
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verbose=True,
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factor=0.5
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)
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n_epochs = N_EPOCHS
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loss_avg = []
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return criterion, scheduler, n_epochs, loss_avg, device, model, optimizer
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def check_loss(loss_avg, scheduler, model, char_to_idx, idx_to_char, device):
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if len(loss_avg) >= LOSS_AVG_MAX:
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mean_loss = np.mean(loss_avg)
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print(f'Loss: {mean_loss}')
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scheduler.step(mean_loss)
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loss_avg = []
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model.eval()
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predicted_text = evaluate(model, char_to_idx, idx_to_char, device=device)
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print(predicted_text)
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return loss_avg
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def training(n_epochs, model, sequence, device, criterion, optimizer, loss_avg, scheduler, char_to_idx, idx_to_char):
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for epoch in range(n_epochs):
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model.train()
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train, target = get_batch(sequence)
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train = train.permute(1, 0, 2).to(device)
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target = target.permute(1, 0, 2).to(device)
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hidden = model.init_hidden(BATCH_SIZE)
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output, hidden = model(train, hidden)
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loss = criterion(output.permute(1, 2, 0), target.squeeze(-1).permute(1, 0))
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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loss_avg.append(loss.item())
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loss_avg = check_loss(loss_avg, scheduler, model, char_to_idx, idx_to_char, device)
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