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"""
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
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