human
I am working on the file "DEEP LEARNING/Pytorch from scratch/CNN/project-dog-classification/dog_app.ipynb".
The first task is:
Question 3: Describe your chosen procedure for preprocessing the data. 
- How does your code resize the images (by cropping, stretching, etc)?  What size did you pick for the input tensor, and why?
- Did you decide to augment the dataset?  If so, how (through translations, flips, rotations, etc)?  If not, why not?
Answer:
(IMPLEMENTATION) Model Architecture
Create a CNN to classify dog breed.  Use the template in the code cell below.
Can you write Python program for it?
assistant
#Here size did you pick for the input tensor, and why.

import torch.nn as nn
import torch.nn.functional as F

# define the CNN architecture
class Net(nn.Module):
    ### TODO: choose an architecture, and complete the class
    def __init__(self):
        super(Net, self).__init__()
        ## Define layers of a CNN
    
    def forward(self, x):
        ## Define forward behavior
        return x

#-#-# You so NOT have to modify the code below this line. #-#-#

# instantiate the CNN
model_scratch = Net()

# move tensors to GPU if CUDA is available
if use_cuda:
    model_scratch.cuda()