Source code for sconce.models.basic_autoencoder

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


[docs]class BasicAutoencoder(nn.Module): """ A basic 2D image autoencoder built up of densly connected layers, two each in the encoder and the decoder. Loss: This model uses binary cross-entropy for the loss. Metrics: None Arguments: image_height (int): image height in pixels. image_width (int): image width in pixels. hidden_size (int): the number of activations in each of the 4 hidden layers. latent_size (int): the number of activations in the latent representation (encoder output). """ def __init__(self, image_height, image_width, hidden_size, latent_size): super().__init__() self.num_pixels = image_height * image_width self.bn1 = nn.BatchNorm1d(self.num_pixels) self.fc1 = nn.Linear(self.num_pixels, hidden_size) self.bn2 = nn.BatchNorm1d(hidden_size) self.fc2 = nn.Linear(hidden_size, latent_size) self.bn3 = nn.BatchNorm1d(latent_size) self.fc3 = nn.Linear(latent_size, hidden_size) self.bn4 = nn.BatchNorm1d(hidden_size) self.fc4 = nn.Linear(hidden_size, self.num_pixels) self.relu = nn.ReLU() self.sigmoid = nn.Sigmoid()
[docs] def encode(self, inputs, **kwargs): encoder_input = inputs.view(-1, self.num_pixels) x = self.bn1(encoder_input) x = self.fc1(x) x = self.relu(x) x = self.bn2(x) x = self.fc2(x) x_latent = self.relu(x) return x_latent
[docs] def decode(self, x_latent): x = self.bn3(x_latent) x = self.fc3(x) x = self.relu(x) x = self.bn4(x) x = self.fc4(x) outputs = self.sigmoid(x) return outputs
[docs] def forward(self, inputs, **kwargs): x_latent = self.encode(inputs) outputs = self.decode(x_latent) return {'outputs': outputs}
[docs] def calculate_loss(self, inputs, outputs, **kwargs): reconstruction_loss = F.binary_cross_entropy(outputs, inputs.view_as(outputs)) return {'loss': reconstruction_loss}