# 05_ Linear regression in PyTorch way

Design your model using class with Variables Construct loss and optimizer (select from PyTorch API) Training cycle(forward, backward, update)

1.

2.

3.

4.

5.

6.

7.Lecture 6: Logistic regression

8.PyTorch forward/backward w = Variable (torch.Tensor([ 1.0 ]), requires_grad = True ) # Any random value # our model forward pass def forward(x): return x * w # Loss function def loss(x, y): y_pred = forward(x) return (y_pred - y) * (y_pred - y) # Training loop for epoch in range ( 10 ): for x_val, y_val in zip (x_data, y_data): l = loss(x_val, y_val) l.backward() print ( &quot; grad: &quot; , x_val, y_val, w.grad.data[ 0 ]) w.data = w.data - 0.01 * w.grad.data # Manually zero the gradients after updating weights w.grad.data.zero_() print ( &quot;progress:&quot; , epoch, l.data[ 0 ])

9.Model class in PyTorch way

10.Training CIFAR10 Classifier http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html Construct loss and optimizer (select from PyTorch API) Training cycle (forward, backward, update) Design your model using class

11.https://github.com/jcjohnson/pytorch-examples for x_val, y_val in zip (x_data, y_data): ... w.data = w.data - 0.01 * w.grad.data Training: forward, loss, backward, step

12.https://github.com/jcjohnson/pytorch-examples for x_val, y_val in zip (x_data, y_data): ... w.data = w.data - 0.01 * w.grad.data Training: forward, loss, backward, step