added deepsad base code

Deep-SAD-PyTorch/src/networks/mlp.py

@@ -0,0 +1,76 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+from base.base_net import BaseNet
+
+
+class MLP(BaseNet):
+
+    def __init__(self, x_dim, h_dims=[128, 64], rep_dim=32, bias=False):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+
+        neurons = [x_dim, *h_dims]
+        layers = [Linear_BN_leakyReLU(neurons[i - 1], neurons[i], bias=bias) for i in range(1, len(neurons))]
+
+        self.hidden = nn.ModuleList(layers)
+        self.code = nn.Linear(h_dims[-1], rep_dim, bias=bias)
+
+    def forward(self, x):
+        x = x.view(int(x.size(0)), -1)
+        for layer in self.hidden:
+            x = layer(x)
+        return self.code(x)
+
+
+class MLP_Decoder(BaseNet):
+
+    def __init__(self, x_dim, h_dims=[64, 128], rep_dim=32, bias=False):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+
+        neurons = [rep_dim, *h_dims]
+        layers = [Linear_BN_leakyReLU(neurons[i - 1], neurons[i], bias=bias) for i in range(1, len(neurons))]
+
+        self.hidden = nn.ModuleList(layers)
+        self.reconstruction = nn.Linear(h_dims[-1], x_dim, bias=bias)
+        self.output_activation = nn.Sigmoid()
+
+    def forward(self, x):
+        x = x.view(int(x.size(0)), -1)
+        for layer in self.hidden:
+            x = layer(x)
+        x = self.reconstruction(x)
+        return self.output_activation(x)
+
+
+class MLP_Autoencoder(BaseNet):
+
+    def __init__(self, x_dim, h_dims=[128, 64], rep_dim=32, bias=False):
+        super().__init__()
+
+        self.rep_dim = rep_dim
+        self.encoder = MLP(x_dim, h_dims, rep_dim, bias)
+        self.decoder = MLP_Decoder(x_dim, list(reversed(h_dims)), rep_dim, bias)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.decoder(x)
+        return x
+
+
+class Linear_BN_leakyReLU(nn.Module):
+    """
+    A nn.Module that consists of a Linear layer followed by BatchNorm1d and a leaky ReLu activation
+    """
+
+    def __init__(self, in_features, out_features, bias=False, eps=1e-04):
+        super(Linear_BN_leakyReLU, self).__init__()
+
+        self.linear = nn.Linear(in_features, out_features, bias=bias)
+        self.bn = nn.BatchNorm1d(out_features, eps=eps, affine=bias)
+
+    def forward(self, x):
+        return F.leaky_relu(self.bn(self.linear(x)))