algorithm/pytorch-tutorial
1
0
Fork 0
mirror of https://github.com/yunjey/pytorch-tutorial.git synced 2025-07-24 18:45:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add some examples to 'PyTorch Basics'

Browse Source
This commit is contained in:
yunjey
2017-03-12 14:01:24 +09:00
parent fcb53f3947
commit f1d21a69fa
1 changed files with 10 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
tutorials/00 - PyTorch Basics/main.py
View File

@ -10,12 +10,12 @@ from torch.autograd import Variable
#========================== Table of Contents ==========================#
# 1. Basic autograd example 1 (Line 21 to 36)
# 2. Basic autograd example 2 (Line 39 to 80)
# 3. Loading data from numpy (Line 83 to 86)
# 4. Implementing the input pipline (Line 90 to 117)
# 5. Input pipline for custom dataset (Line 119 to 139)
# 6. Using pretrained model (Line142 to 156)
# 7. Save and load model (Line 159 to L161)
# 2. Basic autograd example 2 (Line 39 to 76)
# 3. Loading data from numpy (Line 79 to 82)
# 4. Implementing the input pipline (Line 86 to 113)
# 5. Input pipline for custom dataset (Line 115 to 135)
# 6. Using pretrained model (Line 138 to 152)
# 7. Save and load model (Line 155 to L157)
#======================= Basic autograd example 1 =======================#
@ -25,23 +25,21 @@ w = Variable(torch.Tensor([2]), requires_grad=True)
b = Variable(torch.Tensor([3]), requires_grad=True)
# Build a computational graph.
y = w * x + b # y = 2 * x + 3
y = w * x + b # y = 2 * x + 3
# Compute gradients
y.backward()
# Print out the gradients
print(x.grad) # x.grad = 2
print(w.grad) # w.grad = 1
print(b.grad) # b.grad = 1
print(x.grad) # x.grad = 2
print(w.grad) # w.grad = 1
print(b.grad) # b.grad = 1
#======================== Basic autograd example 2 =======================#
# Create tensors.
x = Variable(torch.randn(5, 3))
y = Variable(torch.randn(5, 2))
print ('x: ', x)
print ('y: ', y)
# Build a linear layer.
linear = nn.Linear(3, 2)
@ -54,7 +52,6 @@ optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)
# Forward propagation.
pred = linear(x)
print('pred: ', pred)
# Compute loss.
loss = criterion(pred, y)
@ -69,7 +66,6 @@ print ('dL/db: ', linear.bias.grad)
# 1-step Optimization (gradient descent).
optimizer.step()
print ('Optimized..!')
# You can also do optimization at the low level as shown below.
# linear.weight.data.sub_(0.01 * linear.weight.grad.data)