84 lines
3.5 KiB
Python
84 lines
3.5 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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numpy
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matplotlib
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"""
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import torch
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import torch.nn as nn
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import numpy as np
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import matplotlib.pyplot as plt
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# torch.manual_seed(1) # reproducible
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# np.random.seed(1)
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# Hyper Parameters
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BATCH_SIZE = 64
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LR_G = 0.0001 # learning rate for generator
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LR_D = 0.0001 # learning rate for discriminator
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N_IDEAS = 5 # think of this as number of ideas for generating an art work (Generator)
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ART_COMPONENTS = 15 # it could be total point G can draw in the canvas
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PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])
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# show our beautiful painting range
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# plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
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# plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
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# plt.legend(loc='upper right')
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# plt.show()
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def artist_works(): # painting from the famous artist (real target)
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a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]
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paintings = a * np.power(PAINT_POINTS, 2) + (a-1)
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paintings = torch.from_numpy(paintings).float()
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return paintings
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G = nn.Sequential( # Generator
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nn.Linear(N_IDEAS, 128), # random ideas (could from normal distribution)
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nn.ReLU(),
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nn.Linear(128, ART_COMPONENTS), # making a painting from these random ideas
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)
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D = nn.Sequential( # Discriminator
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nn.Linear(ART_COMPONENTS, 128), # receive art work either from the famous artist or a newbie like G
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nn.ReLU(),
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nn.Linear(128, 1),
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nn.Sigmoid(), # tell the probability that the art work is made by artist
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)
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opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)
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opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)
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plt.ion() # something about continuous plotting
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for step in range(10000):
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artist_paintings = artist_works() # real painting from artist
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G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True) # random ideas\n
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G_paintings = G(G_ideas) # fake painting from G (random ideas)
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prob_artist1 = D(G_paintings) # D try to reduce this prob
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G_loss = torch.mean(torch.log(1. - prob_artist1))
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opt_G.zero_grad()
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G_loss.backward()
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opt_G.step()
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prob_artist0 = D(artist_paintings) # D try to increase this prob
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prob_artist1 = D(G_paintings.detach()) # D try to reduce this prob
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D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))
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opt_D.zero_grad()
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D_loss.backward(retain_graph=True) # reusing computational graph
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opt_D.step()
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if step % 50 == 0: # plotting
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plt.cla()
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plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)
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plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
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plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')
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plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 13})
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plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 13})
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plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.01)
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plt.ioff()
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plt.show() |