MLP-Autoencoder/model.py at main · noahserhan/MLP-Autoencoder · GitHub

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import torch
from torch import nn
import torch.nn.functional as F


class autoencoderMLP4Layer(nn.Module):

    def __init__(self, N_input=784, N_bottleneck=8, N_output=784):
        super(autoencoderMLP4Layer, self).__init__()
        N2 = 392
        self.fc1 = nn.Linear(N_input, N2)
        self.fc2 = nn.Linear(N2, N_bottleneck)
        self.fc3 = nn.Linear(N_bottleneck, N2)
        self.fc4 = nn.Linear(N2, N_output)
        self.type = 'MLP4'
        self.input_shape = (1, 28 * 28)

    def encode(self, X):
        X = self.fc1(X)
        X = F.relu(X)
        X = self.fc2(X)
        X = F.relu(X)

        return X

    def decode(self, X):
        X = self.fc3(X)
        X = F.relu(X)
        X = self.fc4(X)
        X = torch.sigmoid(X)

        return X

    def forward(self, X):
        return self.decode(self.encode(X))