model_builder.py

"""
Contains Pytorch model code instantiate a TinyVGG model.
"""

import torch
from torch import nn

class TinyVGG(nn.Module):
    """
    Creates the TinyVGG architecture
    """
    
    def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:
        super().__init__()
        self.conv_block_1 = nn.Sequential(
            nn.Conv2d(in_channels=input_shape,
                      out_channels=hidden_units,
                      kernel_size=3,
                      stride=1,
                      padding=0),
            nn.ReLU(),
            nn.Conv2d(in_channels=hidden_units,
                      out_channels=hidden_units,
                      kernel_size=3,
                      stride=1,
                      padding=0),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2,
                         stride=2)
        )
        self.conv_block_2=nn.Sequential(
            nn.Conv2d(in_channels=hidden_units,
                      out_channels=hidden_units,
                      kernel_size=3,
                      padding=0),
            nn.ReLU(),
            nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=0),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2,
                         stride=2)
        )
        self.classifier=nn.Sequential(
            nn.Flatten(),
            nn.Linear(in_features=hidden_units*13*13,
                      out_features=output_shape)
        )
        
    def forward(self, x: torch.Tensor):
        x=self.conv_block_1(x)
        x=self.conv_block_2(x)
        x=self.classifier(x)
        return x