标签：loss plt paintings variables torch needed 报错 np prob
跑一个GAN DEMO , 运行时出错。
出错代码：
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [128, 1]], which is output 0 of TBackward, is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
原因是：
错误来源于 PYTORCH的版本不同（我的运行版本是1.8.1, 源代码出自1.4.1版本）， 内置的BACKWARD的流程发生了变化，
原始代码：
# 使用 GAN 生成一个类似二次曲线
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
# np.random.seed(1)
# Hyper Parameters
BATCH_SIZE = 64
LR_G = 0.0001 # learning rate for generator
LR_D = 0.0001 # learning rate for discriminator
N_IDEAS = 5 # think of this as number of ideas for generating an art work(Generator)
ART_COMPONENTS = 15 # it could be total point G can drew in the canvas
PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])
# show our beautiful painting range
plt.plot(PAINT_POINTS[0], np.sin(PAINT_POINTS[0] * np.pi), c='#74BCFF', lw=3, label='standard curve')
plt.legend(loc='best')
plt.show()
def artist_works(): # painting from the famous artist (real target)
#a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]
r = 0.02 * np.random.randn(1, ART_COMPONENTS)
paintings = np.sin(PAINT_POINTS * np.pi) + r
paintings = torch.from_numpy(paintings).float()
return paintings
r = 0.02 * np.random.randn(1, ART_COMPONENTS)
paintings = np.sin(PAINT_POINTS * np.pi) + r
plt.plot(PAINT_POINTS[0],paintings[0])
plt.show()
G = nn.Sequential( # Generator
nn.Linear(N_IDEAS, 128), # random ideas (could from normal distribution)
nn.ReLU(),
nn.Linear(128, ART_COMPONENTS), # making a painting from these random ideas
)
D = nn.Sequential( # Discriminator
nn.Linear(ART_COMPONENTS, 128), # receive art work either from the famous artist or a newbie like G
nn.ReLU(),
nn.Linear(128, 1),
nn.Sigmoid(), # tell the probability that the art work is made by artist
)
opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)
opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)
plt.ion() # something about continuous plotting
D_loss_history = []
G_loss_history = []
for step in range(10000):
artist_paintings = artist_works() # real painting from artist , shape of [64,15]
G_ideas = torch.randn(BATCH_SIZE, N_IDEAS) # random ideas, shape of [64, 5]
G_paintings = G(G_ideas) # fake painting from G (random ideas), G_paintings.shape= [64,15])
prob_artist0 = D(artist_paintings) # D try to increase this prob, prob_artist0.shape =[64,1]
prob_artist1 = D(G_paintings) # D try to reduce this prob, prob_artist1.shape =[64,1]
D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))
G_loss = torch.mean(torch.log(1. - prob_artist1))
D_loss_history.append(D_loss)
G_loss_history.append(G_loss)
opt_D.zero_grad()
D_loss.backward(retain_graph=True) # reusing computational graph
opt_D.step()
opt_G.zero_grad()
G_loss.backward()
opt_G.step()
if step % 50 == 0: # plotting
plt.cla()
plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)
plt.plot(PAINT_POINTS[0], np.sin(PAINT_POINTS[0] * np.pi), c='#74BCFF', lw=3, label='standard curve')
plt.text(-1, 0.75, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 8})
plt.text(-1, 0.5, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 8})
plt.ylim((-1, 1));plt.legend(loc='lower right', fontsize=10);plt.draw();plt.pause(0.01)
plt.ioff()
plt.show()
修改后的代码：
# 使用 GAN 生成一个类似二次曲线
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
# np.random.seed(1)
# Hyper Parameters
BATCH_SIZE = 64
LR_G = 0.0001 # learning rate for generator
LR_D = 0.0001 # learning rate for discriminator
N_IDEAS = 5 # think of this as number of ideas for generating an art work(Generator)
ART_COMPONENTS = 15 # it could be total point G can drew in the canvas
PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])
# show our beautiful painting range
plt.plot(PAINT_POINTS[0], np.sin(PAINT_POINTS[0] * np.pi), c='#74BCFF', lw=3, label='standard curve')
plt.legend(loc='best')
plt.show()
def artist_works(): # painting from the famous artist (real target)
#a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]
r = 0.02 * np.random.randn(1, ART_COMPONENTS)
paintings = np.sin(PAINT_POINTS * np.pi) + r
paintings = torch.from_numpy(paintings).float()
return paintings
r = 0.02 * np.random.randn(1, ART_COMPONENTS)
paintings = np.sin(PAINT_POINTS * np.pi) + r
plt.plot(PAINT_POINTS[0],paintings[0])
plt.show()
G = nn.Sequential( # Generator
nn.Linear(N_IDEAS, 128), # random ideas (could from normal distribution)
nn.ReLU(),
nn.Linear(128, ART_COMPONENTS), # making a painting from these random ideas
)
D = nn.Sequential( # Discriminator
nn.Linear(ART_COMPONENTS, 128), # receive art work either from the famous artist or a newbie like G
nn.ReLU(),
nn.Linear(128, 1),
nn.Sigmoid(), # tell the probability that the art work is made by artist
)
opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)
opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)
plt.ion() # something about continuous plotting
D_loss_history = []
G_loss_history = []
for step in range(10000):
artist_paintings = artist_works() # real painting from artist , shape of [64,15]
G_ideas = torch.randn(BATCH_SIZE, N_IDEAS) # random ideas, shape of [64, 5]
G_paintings = G(G_ideas) # fake painting from G (random ideas), G_paintings.shape= [64,15])
prob_artist1 = D(G_paintings) # D try to reduce this prob, prob_artist1.shape =[64,1]
G_loss = torch.mean(torch.log(1. - prob_artist1))
opt_G.zero_grad()
G_loss.backward()
opt_G.step()
prob_artist0 = D(artist_paintings) # D try to increase this prob, prob_artist0.shape =[64,1]
# detach here to make sure we don't backprop in G that was already changed.
prob_artist1 = D(G_paintings.detach()) # D try to reduce this prob
D_loss_history.append(D_loss)
G_loss_history.append(G_loss)
D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))
opt_D.zero_grad()
D_loss.backward(retain_graph=True) # reusing computational graph
opt_D.step()
if step % 50 == 0: # plotting
plt.cla()
plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)
plt.plot(PAINT_POINTS[0], np.sin(PAINT_POINTS[0] * np.pi), c='#74BCFF', lw=3, label='standard curve')
plt.text(-1, 0.75, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 8})
plt.text(-1, 0.5, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 8})
plt.ylim((-1, 1));plt.legend(loc='lower right', fontsize=10);plt.draw();plt.pause(0.01)
plt.ioff()
plt.show()
运行结果：
。。。。。。
标签：loss,plt,paintings,variables,torch,needed,报错,np,prob 来源： https://blog.csdn.net/weixin_43229238/article/details/123203140
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