Computer_Vision/Chapter02/Specifying_batch_size_while_training_a_model.ipynb

from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn

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

X = torch.tensor(x).float()
Y = torch.tensor(y).float()

device = 'cuda' if torch.cuda.is_available() else 'cpu'
X = X.to(device)
Y = Y.to(device)

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

/home/yyr/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  This is separate from the ipykernel package so we can avoid doing imports until
/home/yyr/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  after removing the cwd from sys.path.

dl = DataLoader(ds, batch_size=2, shuffle=True)

class MyNeuralNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.input_to_hidden_layer = nn.Linear(2,8)
        self.hidden_layer_activation = nn.ReLU()
        self.hidden_to_output_layer = nn.Linear(8,1)
    def forward(self, x):
        x = self.input_to_hidden_layer(x)
        x = self.hidden_layer_activation(x)
        x = self.hidden_to_output_layer(x)
        return x

mynet = MyNeuralNet().to(device)
loss_func = nn.MSELoss()
from torch.optim import SGD
opt = SGD(mynet.parameters(), lr = 0.001)

import time
loss_history = []
start = time.time()
for _ in range(50):
    for data in dl:
        x, y = data
        opt.zero_grad()
        loss_value = loss_func(mynet(x),y)
        loss_value.backward()
        opt.step()
        loss_history.append(loss_value)
end = time.time()
print(end - start)

0.07826399803161621

val_x = [[10,11]]

val_x = torch.tensor(val_x).float().to(device)

mynet(val_x)

tensor([[20.7331]], device='cuda:0', grad_fn=<AddmmBackward>)