This is a simple example for series prediction (sin function), when I set device as ‘cpu’, it works well, however, if I change to device = 'cuda' if torch.cuda.is_available() else 'cpu', it shows
Process finished with exit code -1073740940 (0xC0000374)
All of the codes are shown below.
Anyone can help or provide any advice?
Regards!
import numpy as np
import matplotlib.pyplot as plt
import torch.optim as optim
from visdom import Visdom
import torch
import torch.nn as nn
num_step = 50
input_size = 1
output_size = 1
hidden_size = 16
lr = 0.01
device = 'cuda' if torch.cuda.is_available() else 'cpu'


class RNNnet(nn.Module):

    def __init__(self):
        super(RNNnet, self).__init__()
        self.rnn = nn.RNN(
            input_size=input_size,
            hidden_size=hidden_size,
            num_layers=1,
            batch_first=True
        )

        self.linear = nn.Linear(hidden_size, output_size)

    def forward(self, x, hidden):
        out, hidden = self.rnn(x, hidden)
        out = out.view(-1, hidden_size)
        out = self.linear(out)
        out = out.unsqueeze(dim=0)
        return out, hidden


if __name__ == '__main__':
    rnnnet = RNNnet().to(device)
    loss_func = nn.MSELoss()
    optimizer = optim.Adam(rnnnet.parameters(), lr=lr)
    hidden = torch.zeros(1, 1, hidden_size)

    for epoch in range(10000):
        start = np.random.randint(3, size=1)[0]
        time_steps = np.linspace(start, start+10, num_step)
        data = np.sin(time_steps)
        data = data.reshape(num_step, 1)
        x = torch.tensor(data[:-1]).float().view(1, num_step - 1, 1).to(device)
        y = torch.tensor(data[1:]).float().view(1, num_step - 1, 1).to(device)

        output, hidden = rnnnet(x, hidden)
        hidden = hidden.detach()
        loss = loss_func(output, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if epoch % 100 == 0:
            print("Iteration: {} loss: {}".format(epoch, loss.item()))