codersgyan

{"lastUpload":"2022-01-10T10:45:13.272Z","extensionVersion":"v3.4.3"}

devil-cyber

import torch
import torch.nn as nn
import torchvision
from torchvision.transforms import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F

# HyperParameter
device = torch.device('cuda' if torch.cuda.is_available else 'cpu')

devil-cyber

# MNIST 
# DataLoader Neural Net, activation function
# Loss and Optimizer
# Training Loop (batch training)
# Model evaluation
# GPU Support
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

devil-cyber

import torch
'''
Backpropgation

eg. x = [1.,1.,1]  -> "This is the fixed feature value:"
    y = x + 2  -> a(x) -> "This is the respective y value:"
    z = y*y*3  -> b(y) -> "This is the example for loss function"
Backpropgation    x -> a(x) -> b(y) -> z

dz/dx = dz/dy * dy/dx (Chain Rule)