Computer_Vision/Chapter02/Sequential_method_to_build_a_neural_network.ipynb

x = [[1,2],[3,4],[5,6],[7,8]]
y = [[3],[7],[11],[15]]

import torch
import torch.nn as nn
import numpy as np
from torch.utils.data import Dataset, DataLoader
device = 'cuda' if torch.cuda.is_available() else 'cpu'

class MyDataset(Dataset):
    def __init__(self, x, y):
        self.x = torch.tensor(x).float().to(device)
        self.y = torch.tensor(y).float().to(device)
    def __getitem__(self, ix):
        return self.x[ix], self.y[ix]
    def __len__(self): 
        return len(self.x)

ds = MyDataset(x, y)
dl = DataLoader(ds, batch_size=2, shuffle=True)

model = nn.Sequential(
    nn.Linear(2, 8),
    nn.ReLU(),
    nn.Linear(8, 1)
).to(device)

!pip install torch_summary
from torchsummary import summary

Requirement already satisfied: torch_summary in /home/yyr/anaconda3/lib/python3.7/site-packages (1.4.1)
WARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.
You should consider upgrading via the '/home/yyr/anaconda3/bin/python -m pip install --upgrade pip' command.

summary(model, torch.zeros(1,2));

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Linear: 1-1                            [-1, 8]                   24
├─ReLU: 1-2                              [-1, 8]                   --
├─Linear: 1-3                            [-1, 1]                   9
==========================================================================================
Total params: 33
Trainable params: 33
Non-trainable params: 0
Total mult-adds (M): 0.00
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
==========================================================================================

loss_func = nn.MSELoss()
from torch.optim import SGD
opt = SGD(model.parameters(), lr = 0.001)
import time
loss_history = []
start = time.time()
for _ in range(50):
    for ix, iy in dl:
        opt.zero_grad()
        loss_value = loss_func(model(ix),iy)
        loss_value.backward()
        opt.step()
        loss_history.append(loss_value)
end = time.time()
print(end - start)

0.0754392147064209

val = [[8,9],[10,11],[1.5,2.5]]
val = torch.tensor(val).float()

model(val.to(device))

tensor([[16.7953],
        [20.6512],
        [ 4.2647]], device='cuda:0', grad_fn=<AddmmBackward>)

val.sum(-1)

tensor([17., 21.,  4.])