64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
"""
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View more, visit my tutorial page: https://mofanpy.com/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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torch: 0.4
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matplotlib
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"""
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import torch
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import torch.nn.functional as F
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import matplotlib.pyplot as plt
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# torch.manual_seed(1) # reproducible
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x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)
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y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)
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# torch can only train on Variable, so convert them to Variable
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# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
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# x, y = Variable(x), Variable(y)
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# plt.scatter(x.data.numpy(), y.data.numpy())
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# plt.show()
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class Net(torch.nn.Module):
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def __init__(self, n_feature, n_hidden, n_output):
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super(Net, self).__init__()
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self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer
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self.predict = torch.nn.Linear(n_hidden, n_output) # output layer
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def forward(self, x):
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x = F.relu(self.hidden(x)) # activation function for hidden layer
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x = self.predict(x) # linear output
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return x
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net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network
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print(net) # net architecture
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optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
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loss_func = torch.nn.MSELoss() # this is for regression mean squared loss
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plt.ion() # something about plotting
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for t in range(200):
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prediction = net(x) # input x and predict based on x
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loss = loss_func(prediction, y) # must be (1. nn output, 2. target)
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optimizer.zero_grad() # clear gradients for next train
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loss.backward() # backpropagation, compute gradients
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optimizer.step() # apply gradients
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if t % 5 == 0:
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# plot and show learning process
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plt.cla()
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plt.scatter(x.data.numpy(), y.data.numpy())
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plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
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plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'})
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plt.pause(0.1)
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plt.ioff()
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plt.show()
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