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"""Defines the MLP network."""
from typing import Callable, Optional
import torch
from torch import nn
class MLP(nn.Module):
"""Multi layered perceptron network."""
def __init__(
self,
input_size: int,
output_size: int,
hidden_size: int,
num_layers: int,
dropout_rate: float,
activation_fn: Optional[Callable] = None,
) -> None:
"""Initialization of the MLP network.
Args:
input_size (int): The input shape of the network.
output_size (int): Number of classes in the dataset.
hidden_size (int): The number of `neurons` in each hidden layer.
num_layers (int): The number of hidden layers.
dropout_rate (float): The dropout rate at each layer.
activation_fn (Optional[Callable]): The activation function in the hidden layers, (default:
nn.ReLU()).
"""
super().__init__()
if activation_fn is None:
activation_fn = nn.ReLU(inplace=True)
self.layers = [
nn.Linear(in_features=input_size, out_features=hidden_size),
activation_fn,
]
for _ in range(num_layers):
self.layers += [
nn.Linear(in_features=hidden_size, out_features=hidden_size),
activation_fn,
]
if dropout_rate:
self.layers.append(nn.Dropout(p=dropout_rate))
self.layers.append(nn.Linear(in_features=hidden_size, out_features=output_size))
self.layers = nn.Sequential(*self.layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""The feedforward."""
x = torch.flatten(x, start_dim=1)
return self.layers(x)
# def test():
# x = torch.randn([1, 28, 28])
# input_size = torch.flatten(x).shape[0]
# output_size = 10
# hidden_size = 128
# num_layers = 5
# dropout_rate = 0.25
# activation_fn = nn.GELU()
# net = MLP(
# input_size=input_size,
# output_size=output_size,
# hidden_size=hidden_size,
# num_layers=num_layers,
# dropout_rate=dropout_rate,
# activation_fn=activation_fn,
# )
# from torchsummary import summary
#
# summary(net, (1, 28, 28), device="cpu")
#
# out = net(x)
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