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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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16
tutorial-contents/404_autoencoder.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
matplotlib
numpy
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
@ -87,15 +86,14 @@ f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))
plt.ion() # continuously plot
# original data (first row) for viewing
view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)
view_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.
for i in range(N_TEST_IMG):
a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(())
for epoch in range(EPOCH):
for step, (x, y) in enumerate(train_loader):
b_x = Variable(x.view(-1, 28*28)) # batch x, shape (batch, 28*28)
b_y = Variable(x.view(-1, 28*28)) # batch y, shape (batch, 28*28)
b_label = Variable(y) # batch label
for step, (x, b_label) in enumerate(train_loader):
b_x = x.view(-1, 28*28) # batch x, shape (batch, 28*28)
b_y = x.view(-1, 28*28) # batch y, shape (batch, 28*28)
encoded, decoded = autoencoder(b_x)
@ -105,7 +103,7 @@ for epoch in range(EPOCH):
optimizer.step() # apply gradients
if step % 100 == 0:
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0])
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy())
# plotting decoded image (second row)
_, decoded_data = autoencoder(view_data)
@ -119,7 +117,7 @@ plt.ioff()
plt.show()
# visualize in 3D plot
view_data = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)
view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.
encoded_data, _ = autoencoder(view_data)
fig = plt.figure(2); ax = Axes3D(fig)
X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()