Spaces:
Sleeping
Sleeping
File size: 2,038 Bytes
e2f7ccb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import torch
from torch import nn
class TinyVGG(nn.Module):
"""Creates the TinyVGG architecture.
Replicates the TinyVGG architecture from the CNN explainer website in PyTorch.
See the original architecture here: https://poloclub.github.io/cnn-explainer/
Args:
input_shape: An integer indicating number of input channels.
hidden_units: An integer indicating number of hidden units between layers.
output_shape: An integer indicating number of output units.
"""
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(hidden_units, hidden_units, kernel_size=3, padding=0),
nn.ReLU(),
nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=0),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.classifier = nn.Sequential(
nn.Flatten(),
# Where did this in_features shape come from?
# It's because each layer of our network compresses and changes the shape of our inputs data.
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
# return self.classifier(self.block_2(self.block_1(x))) # <- leverage the benefits of operator fusion |