Computer_Vision/Chapter02/Implementing_custom_loss_function.ipynb

x = [[1,2],[3,4],[5,6],[7,8]]
y = [[3],[7],[11],[15]]
import torch
X = torch.tensor(x).float()
Y = torch.tensor(y).float()
import torch.nn as nn
device = 'cuda' if torch.cuda.is_available() else 'cpu'
X = X.to(device)
Y = Y.to(device) 
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
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)
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)

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: 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).
  
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: 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).
  from ipykernel import kernelapp as app

def my_mean_squared_error(_y, y):
    loss = (_y-y)**2
    loss = loss.mean()
    return loss

loss_func = nn.MSELoss()
loss_value = loss_func(mynet(X),Y)
print(loss_value)

tensor(177.9081, grad_fn=<MseLossBackward>)

my_mean_squared_error(mynet(X),Y)

tensor(177.9081, grad_fn=<MeanBackward0>)

input_to_hidden = mynet.input_to_hidden_layer(X)
hidden_activation = mynet.hidden_layer_activation(input_to_hidden)