MLWhiz

class CustomNLLLoss(nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, x, y):
        # x should be output from LogSoftmax Layer 
        log_prob = -1.0 * x
        # Get log_prob based on y class_index as loss=-mean(ylogp)
        loss = log_prob.gather(1, y.unsqueeze(1))
        loss = loss.mean()
        return loss

MLWhiz

class myClassificationNet(nn.Module):
    def __init__(self):
        super().__init__()
        # Define all Layers Here
        self.lin = nn.Linear(784, 10)
        self.logsoftmax = nn.LogSoftmax(dim=1)
    def forward(self, x):
        # Connect the layer Outputs here to define the forward pass
        x = self.lin(x)
        x = self.logsoftmax(x)

MLWhiz

num_epochs = 5
for epoch in range(num_epochs):
    # Set model to train mode
    model.train()
    for x_batch,y_batch in train_dataloader:
        # Clear gradients
        optimizer.zero_grad()
        # Forward pass - Predicted outputs
        pred = model(x_batch)
        # Find Loss and backpropagation of gradients

MLWhiz

import numpy as np
train_data_size = 1024
sizes = np.random.randint(low=50,high=300,size=(train_data_size,))
X = [np.random.randint(0,10000, (sizes[i])) for i in range(train_data_size)]
y = np.random.rand(train_data_size).round()
#checking one example in dataset
print((X[0],y[0]))

MLWhiz

from torchvision import transforms
from torchvision.datasets import ImageFolder
traindir = "data/train/"
t = transforms.Compose([
        transforms.Resize(size=256),
    transforms.CenterCrop(size=224),
        transforms.ToTensor()])
train_dataset = ImageFolder(root=traindir,transform=t)
print("Num Images in Dataset:", len(train_dataset))
print("Example Image and Label:", train_dataset[2])

MLWhiz

class myCustomNeuralNet(nn.Module):
    def __init__(self):
        super().__init__()
        # Define all Layers Here
        self.lin1 = myCustomLinearLayer(784,10)
        
    def forward(self, x):
        # Connect the layer Outputs here to define the forward pass
        x = self.lin1(x)
        return x

MLWhiz

class myCustomLinearLayer(nn.Module):
    def __init__(self,in_size,out_size):
        super().__init__()
        self.weights = nn.Parameter(torch.randn(in_size, out_size))
        self.bias = nn.Parameter(torch.zeros(out_size))
    def forward(self, x):
        return x.mm(self.weights) + self.bias

MLWhiz

class myCrazyNeuralNet(nn.Module):
    def __init__(self):
        super().__init__()
        # Define all Layers Here
        self.lin1 = nn.Linear(784, 30)
        self.lin2 = nn.Linear(30, 784)
        self.lin3 = nn.Linear(30, 10)
        
    def forward(self, x):
        # Connect the layer Outputs here to define the forward pass

MLWhiz

class myNeuralNet(nn.Module):
    def __init__(self):
        super().__init__()
        # Define all Layers Here
        self.lin1 = nn.Linear(784, 30)
        self.lin2 = nn.Linear(30, 10)
    def forward(self, x):
        # Connect the layer Outputs here to define the forward pass
        x = self.lin1(x)
        x = self.lin2(x)

MLWhiz

A = torch.randn(3,4)
W = torch.randn(4,2)
# Multiply Matrix A and W
t = A.mm(W)
print(f"Created Tensor t by Multiplying A and W:\n{t}")
# Transpose Tensor t
t = t.t()
print(f"Transpose of Tensor t:\n{t}")
# Square each element of t
t = t**2