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import torch.nn as nn
import torch.nn.functional as F
from torch import sigmoid
class Decoder(nn.Module):
"""# Decoder Neural Network
X_dim: Output dimension shape
N: Inner neuronal layer size
z_dim: Input dimension shape
"""
def __init__(self, X_dim, N, z_dim, dropout=0.0, negative_slope=0.3):
super(Decoder, self).__init__()
self.lin1 = nn.Linear(z_dim, N)
self.lin2 = nn.Linear(N, N)
self.lin3 = nn.Linear(N, X_dim)
self.p = dropout
self.negative_slope = negative_slope
def forward(self, x):
x = self.lin1(x)
if self.p > 0.0:
x = F.dropout(x, p=self.p, training=self.training)
x = F.leaky_relu(x, negative_slope=self.negative_slope)
x = self.lin2(x)
if self.p > 0.0:
x = F.dropout(x, p=self.p, training=self.training)
x = F.leaky_relu(x, negative_slope=self.negative_slope)
x = self.lin3(x)
return sigmoid(x)
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