50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
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import torch
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from torch.autograd import Variable
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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EPOCHS_TO_TRAIN = 50000000000
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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if not torch.cuda.is_available():
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print(f'The current used device for this testing training is {device}. Please make sure that at least one Cuda device is used instead for this testing training.')
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assert False
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.fc1 = nn.Linear(2, 3, True)
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self.fc2 = nn.Linear(3, 1, True)
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def forward(self, x):
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x = F.sigmoid(self.fc1(x))
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x = self.fc2(x)
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return x
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net = Net().to(device)
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print(device)
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inputs = list(
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map(
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lambda s: Variable(torch.Tensor([s]).to(device)),
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[[0, 0], [0, 1], [1, 0], [1, 1]],
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)
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)
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targets = list(
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map(lambda s: Variable(torch.Tensor([s]).to(device)), [[0], [1], [1], [0]])
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)
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criterion = nn.MSELoss()
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optimizer = optim.SGD(net.parameters(), lr=0.01)
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print("Training loop:")
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for idx in range(0, EPOCHS_TO_TRAIN):
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for input, target in zip(inputs, targets):
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optimizer.zero_grad() # zero the gradient buffers
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output = net(input)
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loss = criterion(output, target)
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loss.backward()
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optimizer.step() # Does the update
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if idx % 5000 == 0:
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print(loss.item())
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