andre/PyTorch-Tutorial
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
Morvan Zhou
2017-05-08 12:48:29 +10:00
committed by Morvan Zhou
parent 468039f49c
commit b212b3e026
7 changed files with 80 additions and 84 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
tutorial-contents/401_CNN.py
View File

@ -17,10 +17,10 @@ import matplotlib.pyplot as plt
torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 50
LR = 0.001 # learning rate
DOWNLOAD_MNIST = False
LR = 0.001 # learning rate
DOWNLOAD_MNIST = True # set to False if you have downloaded
# Mnist digits dataset
@ -33,8 +33,8 @@ train_data = torchvision.datasets.MNIST(
)
# plot one example
print(train_data.train_data.size()) # (60000, 28, 28)
print(train_data.train_labels.size()) # (60000)
print(train_data.train_data.size()) # (60000, 28, 28)
print(train_data.train_labels.size()) # (60000)
plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[0])
plt.show()
@ -51,28 +51,28 @@ test_y = test_data.test_labels[:2000]
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( # input shape (1, 28, 28)
self.conv1 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(
in_channels=1, # input height
out_channels=16, # n_filters
kernel_size=5, # filter size
stride=1, # filter movement/step
padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
), # output shape (16, 28, 28)
nn.ReLU(), # activation
nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
in_channels=1, # input height
out_channels=16, # n_filters
kernel_size=5, # filter size
stride=1, # filter movement/step
padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
), # output shape (16, 28, 28)
nn.ReLU(), # activation
nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
)
self.conv2 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
nn.ReLU(), # activation
nn.MaxPool2d(2), # output shape (32, 7, 7)
self.conv2 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
nn.ReLU(), # activation
nn.MaxPool2d(2), # output shape (32, 7, 7)
)
self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
output = self.out(x)
return output
@ -81,7 +81,7 @@ cnn = CNN()
print(cnn) # net architecture
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
# training and testing
for epoch in range(EPOCH):

50
tutorial-contents/402_RNN_classifier.py
View File

@ -18,26 +18,26 @@ import matplotlib.pyplot as plt
torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64
TIME_STEP = 28 # rnn time step / image height
INPUT_SIZE = 28 # rnn input size / image width
LR = 0.01 # learning rate
DOWNLOAD_MNIST = False # set to True if haven't download the data
TIME_STEP = 28 # rnn time step / image height
INPUT_SIZE = 28 # rnn input size / image width
LR = 0.01 # learning rate
DOWNLOAD_MNIST = True # set to True if haven't download the data
# Mnist digital dataset
train_data = dsets.MNIST(
root='./mnist/',
train=True, # this is training data
transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST, # download it if you don't have it
train=True, # this is training data
transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST, # download it if you don't have it
)
# plot one example
print(train_data.train_data.size()) # (60000, 28, 28)
print(train_data.train_labels.size()) # (60000)
print(train_data.train_data.size()) # (60000, 28, 28)
print(train_data.train_labels.size()) # (60000)
plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[0])
plt.show()
@ -55,11 +55,11 @@ class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM( # if use nn.RNN(), it hardly learns
self.rnn = nn.LSTM( # if use nn.RNN(), it hardly learns
input_size=28,
hidden_size=64, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
hidden_size=64, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
)
self.out = nn.Linear(64, 10)
@ -80,22 +80,22 @@ rnn = RNN()
print(rnn)
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
# 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, (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
output = rnn(b_x) # rnn output
loss = loss_func(output, b_y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
output = rnn(b_x) # rnn output
loss = loss_func(output, b_y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if step % 50 == 0:
test_output = rnn(test_x) # (samples, time_step, input_size)
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) / test_y.size
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)

20
tutorial-contents/403_RNN_regressor.py
View File

@ -37,9 +37,9 @@ class RNN(nn.Module):
self.rnn = nn.RNN(
input_size=1,
hidden_size=32, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
hidden_size=32, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
)
self.out = nn.Linear(32, 1)
@ -61,10 +61,10 @@ print(rnn)
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.MSELoss()
h_state = None # for initial hidden state
h_state = None # for initial hidden state
plt.figure(1, figsize=(12, 5))
plt.ion() # continuously plot
plt.ion() # continuously plot
plt.show()
for step in range(60):
@ -79,12 +79,12 @@ for step in range(60):
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 = Variable(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
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
loss = loss_func(prediction, y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
# plotting
plt.plot(steps, y_np.flatten(), 'r-')

6
tutorial-contents/404_autoencoder.py
View File

@ -39,9 +39,9 @@ train_data = torchvision.datasets.MNIST(
# plot one example
print(train_data.train_data.size()) # (60000, 28, 28)
print(train_data.train_labels.size()) # (60000)
# plt.imshow(train_data.train_data[2].numpy(), cmap='gray')
# plt.title('%i' % train_data.train_labels[2])
# plt.show()
plt.imshow(train_data.train_data[2].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[2])
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)

10
tutorial-contents/405_DQN_Reinforcement_learning.py
View File

@ -46,8 +46,8 @@ class DQN(object):
def __init__(self):
self.eval_net, self.target_net = Net(), Net()
self.learn_step_counter = 0 # for target updateing
self.memory_counter = 0 # for storing memory
self.learn_step_counter = 0 # for target updating
self.memory_counter = 0 # for storing memory
self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2)) # initialize memory
self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)
self.loss_func = nn.MSELoss()
@ -100,7 +100,6 @@ for i_episode in range(400):
ep_r = 0
while True:
env.render()
a = dqn.choose_action(s)
# take action
@ -112,7 +111,6 @@ for i_episode in range(400):
r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
r = r1 + r2
# store experience
dqn.store_transition(s, a, r, s_)
ep_r += r
@ -120,10 +118,8 @@ for i_episode in range(400):
dqn.learn()
if done:
print('Ep: ', i_episode,
'| Ep_r: ', round(ep_r, 2),
)
'| Ep_r: ', round(ep_r, 2))
if done:
break
s = s_

32
tutorial-contents/501_why_torch_dynamic_graph.py
View File

@ -17,10 +17,10 @@ torch.manual_seed(1) # reproducible
# Hyper Parameters
BATCH_SIZE = 64
TIME_STEP = 5 # rnn time step / image height
INPUT_SIZE = 1 # rnn input size / image width
LR = 0.02 # learning rate
DOWNLOAD_MNIST = False # set to True if haven't download the data
TIME_STEP = 5 # rnn time step / image height
INPUT_SIZE = 1 # rnn input size / image width
LR = 0.02 # learning rate
DOWNLOAD_MNIST = True # set to False if have downloaded the data
class RNN(nn.Module):
@ -29,9 +29,9 @@ class RNN(nn.Module):
self.rnn = nn.RNN(
input_size=1,
hidden_size=32, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
hidden_size=32, # rnn hidden unit
num_layers=1, # number of rnn layer
batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
)
self.out = nn.Linear(32, 1)
@ -41,8 +41,8 @@ class RNN(nn.Module):
# r_out (batch, time_step, output_size)
r_out, h_state = self.rnn(x, h_state)
outs = [] # this is where you can find torch is dynamic
for time_step in range(r_out.size(1)): # calculate output for each time step
outs = [] # this is where you can find torch is dynamic
for time_step in range(r_out.size(1)): # calculate output for each time step
outs.append(self.out(r_out[:, time_step, :]))
return torch.stack(outs, dim=1), h_state
@ -51,7 +51,7 @@ rnn = RNN()
print(rnn)
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.MSELoss() # the target label is not one-hotted
loss_func = nn.MSELoss() # the target label is not one-hotted
h_state = None # for initial hidden state
@ -79,7 +79,7 @@ for i in range(60):
####################### Above is different ###########################
print(len(steps)) # print how many time step feed to RNN
print(len(steps)) # print how many time step feed to RNN
x_np = np.sin(steps) # float32 for converting torch FloatTensor
y_np = np.cos(steps)
@ -89,12 +89,12 @@ for i in range(60):
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 = Variable(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
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
loss = loss_func(prediction, y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
# plotting
plt.plot(steps, y_np.flatten(), 'r-')

6
tutorial-contents/504_batch_normalization.py
View File

@ -58,7 +58,7 @@ class Net(nn.Module):
self.bns = []
self.bn_input = nn.BatchNorm1d(1, momentum=0.5) # for input data
for i in range(N_HIDDEN): # build hidden layers and BN layers
for i in range(N_HIDDEN): # build hidden layers and BN layers
input_size = 1 if i == 0 else 10
fc = nn.Linear(input_size, 10)
setattr(self, 'fc%i' % i, fc) # IMPORTANT set layer to the Module
@ -83,7 +83,7 @@ class Net(nn.Module):
for i in range(N_HIDDEN):
x = self.fcs[i](x)
pre_activation.append(x)
if self.do_bn: x = self.bns[i](x) # batch normalization
if self.do_bn: x = self.bns[i](x) # batch normalization
x = ACTIVATION(x)
layer_input.append(x)
out = self.predict(x)
@ -147,7 +147,7 @@ for epoch in range(EPOCH):
loss = loss_func(pred, b_y)
opt.zero_grad()
loss.backward()
opt.step() # it will also learn the parameters in Batch Normalization
opt.step() # it will also learns the parameters in Batch Normalization
plt.ioff()