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update to torch 0.4

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Morvan Zhou
2018-05-30 01:39:53 +08:00
parent 7e7c9bb383
commit 921b69a582
15 changed files with 82 additions and 104 deletions
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23
tutorial-contents/406_conditional_GAN.py
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@ -3,13 +3,12 @@ View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
Dependencies:
torch: 0.1.11
torch: 0.4
numpy
matplotlib
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
@ -37,7 +36,7 @@ def artist_works_with_labels(): # painting from the famous artist (real targ
labels = (a-1) > 0.5 # upper paintings (1), lower paintings (0), two classes
paintings = torch.from_numpy(paintings).float()
labels = torch.from_numpy(labels.astype(np.float32))
return Variable(paintings), Variable(labels)
return paintings, labels
G = nn.Sequential( # Generator
@ -60,7 +59,7 @@ plt.ion() # something about continuous plotting
for step in range(10000):
artist_paintings, labels = artist_works_with_labels() # real painting, label from artist
G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS)) # random ideas
G_ideas = torch.randn(BATCH_SIZE, N_IDEAS) # random ideas
G_inputs = torch.cat((G_ideas, labels), 1) # ideas with labels
G_paintings = G(G_inputs) # fake painting w.r.t label from G
@ -75,7 +74,7 @@ for step in range(10000):
G_loss = torch.mean(D_score1) # minimise D score w.r.t G
opt_D.zero_grad()
D_loss.backward(retain_variables=True) # retain_variables for reusing computational graph
D_loss.backward(retain_graph=True) # reusing computational graph
opt_D.step()
opt_G.zero_grad()
@ -88,20 +87,20 @@ for step in range(10000):
bound = [0, 0.5] if labels.data[0, 0] == 0 else [0.5, 1]
plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound')
plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound')
plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})
plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})
plt.text(-.5, 1.7, 'Class = %i' % int(labels.data[0, 0]), fontdict={'size': 15})
plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.1)
plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 13})
plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 13})
plt.text(-.5, 1.7, 'Class = %i' % int(labels.data[0, 0]), fontdict={'size': 13})
plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.1)
plt.ioff()
plt.show()
# plot a generated painting for upper class
z = Variable(torch.randn(1, N_IDEAS))
label = Variable(torch.FloatTensor([[1.]])) # for upper class
z = torch.randn(1, N_IDEAS)
label = torch.FloatTensor([[1.]]) # for upper class
G_inputs = torch.cat((z, label), 1)
G_paintings = G(G_inputs)
plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='G painting for upper class',)
plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound (class 1)')
plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound (class 1)')
plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.show()
plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.show()