31 lines
981 B
Python
31 lines
981 B
Python
import torch
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from torch.utils.data import TensorDataset
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import matplotlib.pyplot as plt
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def make_context_pairs(data: bytes, context_length: int) -> TensorDataset:
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data = torch.tensor(list(data), dtype=torch.long)
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sample_count = data.shape[0] - context_length
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x = data.unfold(0, context_length, 1)[:sample_count]
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y = data[context_length:]
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return TensorDataset(x, y)
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def print_distribution(from_to: tuple[int, int], probabilities: list[float]):
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plt.hist(range(from_to[0], from_to[1]), weights=probabilities)
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plt.show()
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def print_losses(train_losses: list[float], validation_losses: list[float], show=False):
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plt.plot(train_losses, label="Training loss")
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plt.plot(validation_losses, label="Validation loss")
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plt.xlabel("Epoch")
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plt.ylabel("Loss (cross entropy)")
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plt.legend()
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if show:
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plt.show()
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plt.savefig("losses.png")
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def load_data(path: str) -> bytes:
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with open(path, "rb") as f:
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return f.read()
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