| """ | |
| Contains PyTorch model code to instantiate a TinyVGG model. | |
| """ | |
| import torch | |
| from torch import nn | |
| class TrashClassificationCNNModel(nn.Module): | |
| def __init__(self, input_shape: int, hidden_units: int, output_shape: int): | |
| super().__init__() | |
| self.block_1 = nn.Sequential( | |
| nn.Conv2d(input_shape, hidden_units, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1), | |
| nn.ReLU(), | |
| nn.Conv2d(hidden_units, hidden_units, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1), | |
| nn.ReLU(), | |
| nn.MaxPool2d(kernel_size=2) | |
| ) | |
| self.block_2 = nn.Sequential( | |
| nn.Conv2d(hidden_units, hidden_units, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1), | |
| nn.ReLU(), | |
| nn.Conv2d(hidden_units, hidden_units, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1), | |
| nn.ReLU(), | |
| nn.MaxPool2d(kernel_size=2) | |
| ) | |
| self.classifier = nn.Sequential( | |
| nn.Flatten(), | |
| nn.Linear(in_features=hidden_units*28*28, | |
| out_features=output_shape) | |
| ) | |
| def forward(self, x): | |
| x = self.block_1(x) | |
| x = self.block_2(x) | |
| x = self.classifier(x) | |
| return x |