Tutorial_5.py

#Tutorial 05 - BUILD THE NEURAL NETWORK

import torch
from torch import nn

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")

# Define the Class
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(nn.Linear(28*28,512,),
                                                nn.ReLU(),
                                                nn.Linear(512,512),
                                                nn.ReLU(),
                                                nn.Linear(512,10))
    def forward(self,x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

model = NeuralNetwork().to(device)
print(model)

X = torch.rand(1,28,28, device=device)
logits = model(X)
pred_prob = nn.Softmax(dim=1)(logits)
y_pred = pred_prob.argmax(1)
print("Predicted class ", y_pred)

input_image = torch.rand(3,28,28)
print(input_image.size())
flatten = nn.Flatten()
flat_image = flatten(input_image)
print(flat_image.size())

layer1 = nn.Linear(in_features=28*28, out_features=20)
hidden1 = layer1(flat_image)
print(hidden1.size())
print(f"Before ReLU: {hidden1}\n\n")
hidden1 = nn.ReLU()(hidden1)
print(f"After ReLU: {hidden1}")

seq_modules = nn.Sequential(
    flatten,
    layer1,
    nn.ReLU(),
    nn.Linear(20, 10)
)
input_image = torch.rand(3,28,28)
logits = seq_modules(input_image)

softmax = nn.Softmax(dim=1)
pred_probab = softmax(logits)

print(f"Model structure: {model}\n\n")

for name, param in model.named_parameters():
    print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n")