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points, directions = generate_sequences(n=256, seed=13) And then let’s visualize the first five squares: class Encoder(nn.Module): def __init__(self, 阅读全文
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Data Generation points, directions = generate_sequences(n=128, seed=13) And then let’s visualize the first ten squares: The corners show the order in 阅读全文
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Data Generation x, y = load_data(n_points=1000, n_dims=10) Next, we can use these data points to create a dataset and a data loader (no mini-batches t 阅读全文
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rand() - Returns a tensor filled with random numbers from a uniform distribution on the interval [0, 1) torch.rand(100) tensor([0.7880, 0.3032, 0.3627 阅读全文
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Error code: fig, axs = plt.subplots(n_filters, n_in_channels, figsize=figsize) print(axs[0, 0]) This is because n_filters = 1 and n_in_channels = 1, a 阅读全文
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