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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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8
tutorial-contents/301_regression.py
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@ -3,11 +3,10 @@ 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
"""
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
@ -17,8 +16,9 @@ x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape
y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)
# torch can only train on Variable, so convert them to Variable
# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
# x, y = Variable(x), Variable(y)
#The above code is depricated. Now,autograd directly supports tensors
# plt.scatter(x.data.numpy(), y.data.numpy())
# plt.show()
@ -56,7 +56,7 @@ for t in range(200):
plt.cla()
plt.scatter(x.data.numpy(), y.data.numpy())
plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
plt.text(0.5, 0, 'Loss=%.4f' % loss.data[0], fontdict={'size': 20, 'color': 'red'})
plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'})
plt.pause(0.1)
plt.ioff()

7
tutorial-contents/302_classification.py
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@ -3,11 +3,10 @@ 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
"""
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
@ -22,8 +21,8 @@ y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,) LongTensor = 64-bit integer
# torch can only train on Variable, so convert them to Variable
x, y = Variable(x), Variable(y)
# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
# x, y = Variable(x), Variable(y)
# plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')
# plt.show()

7
tutorial-contents/304_save_reload.py
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@ -3,11 +3,10 @@ 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
"""
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
@ -15,7 +14,9 @@ import matplotlib.pyplot as plt
# fake data
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)
y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)
x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)
# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
# x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)
def save():

12
tutorial-contents/306_optimizer.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
"""
import torch
import torch.utils.data as Data
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
@ -27,7 +26,7 @@ plt.scatter(x.numpy(), y.numpy())
plt.show()
# put dateset into torch dataset
torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)
torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)
@ -64,17 +63,14 @@ if __name__ == '__main__':
# training
for epoch in range(EPOCH):
print('Epoch: ', epoch)
for step, (batch_x, batch_y) in enumerate(loader): # for each training step
b_x = Variable(batch_x)
b_y = Variable(batch_y)
for step, (b_x, b_y) in enumerate(loader): # for each training step
for net, opt, l_his in zip(nets, optimizers, losses_his):
output = net(b_x) # get output for every net
loss = loss_func(output, b_y) # compute loss for every net
opt.zero_grad() # clear gradients for next train
loss.backward() # backpropagation, compute gradients
opt.step() # apply gradients
l_his.append(loss.data[0]) # loss recoder
l_his.append(loss.data.numpy()) # loss recoder
labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
for i, l_his in enumerate(losses_his):

15
tutorial-contents/401_CNN.py
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@ -3,7 +3,7 @@ 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
torchvision
matplotlib
"""
@ -14,7 +14,6 @@ import os
# third-party library
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
@ -51,9 +50,9 @@ plt.show()
# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# convert test data into Variable, pick 2000 samples to speed up testing
# pick 2000 samples to speed up testing
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1), volatile=True).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]
@ -106,9 +105,7 @@ def plot_with_labels(lowDWeights, labels):
plt.ion()
# training and testing
for epoch in range(EPOCH):
for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader
b_x = Variable(x) # batch x
b_y = Variable(y) # batch y
for step, (b_x, b_y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader
output = cnn(b_x)[0] # cnn output
loss = loss_func(output, b_y) # cross entropy loss
@ -119,8 +116,8 @@ for epoch in range(EPOCH):
if step % 50 == 0:
test_output, last_layer = cnn(test_x)
pred_y = torch.max(test_output, 1)[1].data.squeeze()
accuracy = sum(pred_y == test_y) / float(test_y.size(0))
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)
accuracy = float(sum(pred_y == test_y)) / float(test_y.size(0))
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
if HAS_SK:
# Visualization of trained flatten layer (T-SNE)
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)

14
tutorial-contents/402_RNN_classifier.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
torchvision
"""
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
@ -47,7 +46,7 @@ train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_
# convert test data into Variable, pick 2000 samples to speed up testing
test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())
test_x = Variable(test_data.test_data, volatile=True).type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)
test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255. # shape (2000, 28, 28) value in range(0,1)
test_y = test_data.test_labels.numpy().squeeze()[:2000] # covert to numpy array
@ -84,9 +83,8 @@ loss_func = nn.CrossEntropyLoss() # the target label is no
# training and testing
for epoch in range(EPOCH):
for step, (x, y) in enumerate(train_loader): # gives batch data
b_x = Variable(x.view(-1, 28, 28)) # reshape x to (batch, time_step, input_size)
b_y = Variable(y) # batch y
for step, (b_x, b_y) in enumerate(train_loader): # gives batch data
b_x = b_x.view(-1, 28, 28) # reshape x to (batch, time_step, input_size)
output = rnn(b_x) # rnn output
loss = loss_func(output, b_y) # cross entropy loss
@ -97,8 +95,8 @@ for epoch in range(EPOCH):
if step % 50 == 0:
test_output = rnn(test_x) # (samples, time_step, input_size)
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
accuracy = sum(pred_y == test_y) / float(test_y.size)
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)
accuracy = float(sum(pred_y == test_y)) / float(test_y.size)
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
# print 10 predictions from test data
test_output = rnn(test_x[:10].view(-1, 28, 28))

11
tutorial-contents/403_RNN_regressor.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
from torch import nn
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
@ -69,19 +68,19 @@ h_state = None # for initial hidden state
plt.figure(1, figsize=(12, 5))
plt.ion() # continuously plot
for step in range(60):
for step in range(100):
start, end = step * np.pi, (step+1)*np.pi # time range
# use sin predicts cos
steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)
x_np = np.sin(steps) # float32 for converting torch FloatTensor
y_np = np.cos(steps)
x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis])) # shape (batch, time_step, input_size)
y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))
x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size)
y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
prediction, h_state = rnn(x, h_state) # rnn output
# !! next step is important !!
h_state = Variable(h_state.data) # repack the hidden state, break the connection from last iteration
h_state = h_state.data # repack the hidden state, break the connection from last iteration
loss = loss_func(prediction, y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step

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()

13
tutorial-contents/405_DQN_Reinforcement_learning.py
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@ -4,13 +4,12 @@ My Youtube Channel: https://www.youtube.com/user/MorvanZhou
More about Reinforcement learning: https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/
Dependencies:
torch: 0.3
torch: 0.4
gym: 0.8.1
numpy
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import gym
@ -55,7 +54,7 @@ class DQN(object):
self.loss_func = nn.MSELoss()
def choose_action(self, x):
x = Variable(torch.unsqueeze(torch.FloatTensor(x), 0))
x = torch.unsqueeze(torch.FloatTensor(x), 0)
# input only one sample
if np.random.uniform() < EPSILON: # greedy
actions_value = self.eval_net.forward(x)
@ -82,10 +81,10 @@ class DQN(object):
# sample batch transitions
sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)
b_memory = self.memory[sample_index, :]
b_s = Variable(torch.FloatTensor(b_memory[:, :N_STATES]))
b_a = Variable(torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int)))
b_r = Variable(torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2]))
b_s_ = Variable(torch.FloatTensor(b_memory[:, -N_STATES:]))
b_s = torch.FloatTensor(b_memory[:, :N_STATES])
b_a = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int))
b_r = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2])
b_s_ = torch.FloatTensor(b_memory[:, -N_STATES:])
# q_eval w.r.t the action in experience
q_eval = self.eval_net(b_s).gather(1, b_a) # shape (batch, 1)

15
tutorial-contents/406_GAN.py
View File

@ -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
@ -35,7 +34,7 @@ def artist_works(): # painting from the famous artist (real target)
a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]
paintings = a * np.power(PAINT_POINTS, 2) + (a-1)
paintings = torch.from_numpy(paintings).float()
return Variable(paintings)
return paintings
G = nn.Sequential( # Generator
nn.Linear(N_IDEAS, 128), # random ideas (could from normal distribution)
@ -57,7 +56,7 @@ plt.ion() # something about continuous plotting
for step in range(10000):
artist_paintings = artist_works() # real painting from artist
G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS)) # random ideas
G_ideas = torch.randn(BATCH_SIZE, N_IDEAS) # random ideas
G_paintings = G(G_ideas) # fake painting from G (random ideas)
prob_artist0 = D(artist_paintings) # D try to increase this prob
@ -67,7 +66,7 @@ for step in range(10000):
G_loss = torch.mean(torch.log(1. - prob_artist1))
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()
@ -79,9 +78,9 @@ for step in range(10000):
plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)
plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')
plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 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.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)
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.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.01)
plt.ioff()
plt.show()

23
tutorial-contents/406_conditional_GAN.py
View File

@ -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()

9
tutorial-contents/501_why_torch_dynamic_graph.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
from torch import nn
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
@ -80,12 +79,12 @@ for i in range(60):
x_np = np.sin(steps) # float32 for converting torch FloatTensor
y_np = np.cos(steps)
x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis])) # shape (batch, time_step, input_size)
y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))
x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size)
y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])
prediction, h_state = rnn(x, h_state) # rnn output
# !! next step is important !!
h_state = Variable(h_state.data) # repack the hidden state, break the connection from last iteration
h_state = h_state.data # repack the hidden state, break the connection from last iteration
loss = loss_func(prediction, y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step

11
tutorial-contents/502_GPU.py
View File

@ -3,12 +3,11 @@ 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
torchvision
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
@ -25,7 +24,7 @@ train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffl
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
# !!!!!!!! Change in here !!!!!!!!! #
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000].cuda()/255. # Tensor on GPU
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000].cuda()/255. # Tensor on GPU
test_y = test_data.test_labels[:2000].cuda()
@ -56,8 +55,8 @@ for epoch in range(EPOCH):
for step, (x, y) in enumerate(train_loader):
# !!!!!!!! Change in here !!!!!!!!! #
b_x = Variable(x).cuda() # Tensor on GPU
b_y = Variable(y).cuda() # Tensor on GPU
b_x = x.cuda() # Tensor on GPU
b_y = y.cuda() # Tensor on GPU
output = cnn(b_x)
loss = loss_func(output, b_y)
@ -72,7 +71,7 @@ for epoch in range(EPOCH):
pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # move the computation in GPU
accuracy = torch.sum(pred_y == test_y) / test_y.size(0)
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
test_output = cnn(test_x[:10])

9
tutorial-contents/503_dropout.py
View File

@ -3,11 +3,10 @@ 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
"""
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
@ -18,12 +17,10 @@ N_HIDDEN = 300
# training data
x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
y = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))
x, y = Variable(x), Variable(y)
# test data
test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
test_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))
test_x, test_y = Variable(test_x, volatile=True), Variable(test_y, volatile=True)
# show data
plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train')
@ -85,8 +82,8 @@ for t in range(500):
plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.3, label='test')
plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting')
plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)')
plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data[0], fontdict={'size': 20, 'color': 'red'})
plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data[0], fontdict={'size': 20, 'color': 'blue'})
plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data.numpy(), fontdict={'size': 20, 'color': 'red'})
plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data.numpy(), fontdict={'size': 20, 'color': 'blue'})
plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1)
# change back to train mode

14
tutorial-contents/504_batch_normalization.py
View File

@ -3,12 +3,11 @@ 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
from torch.autograd import Variable
from torch import nn
from torch.nn import init
import torch.utils.data as Data
@ -39,10 +38,10 @@ noise = np.random.normal(0, 2, test_x.shape)
test_y = np.square(test_x) - 5 + noise
train_x, train_y = torch.from_numpy(x).float(), torch.from_numpy(y).float()
test_x = Variable(torch.from_numpy(test_x).float(), volatile=True) # not for computing gradients
test_y = Variable(torch.from_numpy(test_y).float(), volatile=True)
test_x = torch.from_numpy(test_x).float()
test_y = torch.from_numpy(test_y).float()
train_dataset = Data.TensorDataset(data_tensor=train_x, target_tensor=train_y)
train_dataset = Data.TensorDataset(train_x, train_y)
train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)
# show data
@ -72,8 +71,8 @@ class Net(nn.Module):
self._set_init(self.predict) # parameters initialization
def _set_init(self, layer):
init.normal(layer.weight, mean=0., std=.1)
init.constant(layer.bias, B_INIT)
init.normal_(layer.weight, mean=0., std=.1)
init.constant_(layer.bias, B_INIT)
def forward(self, x):
pre_activation = [x]
@ -127,7 +126,6 @@ for epoch in range(EPOCH):
plot_histogram(*layer_inputs, *pre_acts) # plot histogram
for step, (b_x, b_y) in enumerate(train_loader):
b_x, b_y = Variable(b_x), Variable(b_y)
for net, opt in zip(nets, opts): # train for each network
pred, _, _ = net(b_x)
loss = loss_func(pred, b_y)