71 lines
2.6 KiB
Python
71 lines
2.6 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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# make fake data
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n_data = torch.ones(100, 2)
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x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2)
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y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1)
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x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2)
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y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1)
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x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating
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y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,) LongTensor = 64-bit integer
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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()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')
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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.out = 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.out(x)
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return x
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net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network
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print(net) # net architecture
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optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
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loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted
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plt.ion() # something about plotting
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for t in range(100):
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out = net(x) # input x and predict based on x
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loss = loss_func(out, y) # must be (1. nn output, 2. target), the target label is NOT one-hotted
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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 % 2 == 0:
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# plot and show learning process
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plt.cla()
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prediction = torch.max(out, 1)[1]
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pred_y = prediction.data.numpy()
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target_y = y.data.numpy()
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plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')
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accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)
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plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, 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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