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"""Defines the LeNet network."""
from typing import Callable, Optional, Tuple
import torch
from torch import nn
class Flatten(nn.Module):
"""Flattens a tensor."""
def forward(self, x: int) -> torch.Tensor:
"""Flattens a tensor for input to a nn.Linear layer."""
return torch.flatten(x, start_dim=1)
class LeNet(nn.Module):
"""LeNet network."""
def __init__(
self,
channels: Tuple[int, ...],
kernel_sizes: Tuple[int, ...],
hidden_size: Tuple[int, ...],
dropout_rate: float,
output_size: int,
activation_fn: Optional[Callable] = None,
) -> None:
"""The LeNet network.
Args:
channels (Tuple[int, ...]): Channels in the convolutional layers.
kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers.
hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
dropout_rate (float): The dropout rate.
output_size (int): Number of classes.
activation_fn (Optional[Callable]): The non-linear activation function. Defaults to
nn.ReLU(inplace).
"""
super().__init__()
if activation_fn is None:
activation_fn = nn.ReLU(inplace=True)
self.layers = [
nn.Conv2d(
in_channels=channels[0],
out_channels=channels[1],
kernel_size=kernel_sizes[0],
),
activation_fn,
nn.Conv2d(
in_channels=channels[1],
out_channels=channels[2],
kernel_size=kernel_sizes[1],
),
activation_fn,
nn.MaxPool2d(kernel_sizes[2]),
nn.Dropout(p=dropout_rate),
Flatten(),
nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]),
activation_fn,
nn.Dropout(p=dropout_rate),
nn.Linear(in_features=hidden_size[1], out_features=output_size),
]
self.layers = nn.Sequential(*self.layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""The feedforward."""
return self.layers(x)
# def test():
# x = torch.randn([1, 1, 28, 28])
# channels = [1, 32, 64]
# kernel_sizes = [3, 3, 2]
# hidden_size = [9216, 128]
# output_size = 10
# dropout_rate = 0.2
# activation_fn = nn.ReLU()
# net = LeNet(
# channels=channels,
# kernel_sizes=kernel_sizes,
# dropout_rate=dropout_rate,
# hidden_size=hidden_size,
# output_size=output_size,
# activation_fn=activation_fn,
# )
# from torchsummary import summary
#
# summary(net, (1, 28, 28), device="cpu")
# out = net(x)
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