A simple two-layer MLP model

bob/learn/pytorch/architectures/TwoLayerMLP.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: Olegs Nikisins
+"""
+#==============================================================================
+# Import here:
+
+from torch import nn
+
+#==============================================================================
+# Define the network:
+
+
+class TwoLayerMLP(nn.Module):
+    """
+    A simple two-layer MLP for binary classification.
+
+    Attributes
+    ----------
+    in_features : int
+        Dimensionality of the input feature vectors.
+
+    n_hidden_relu : int
+        Number of ReLU units in the hidden layer of the MLP
+    """
+
+    def __init__(self, in_features, n_hidden_relu):
+        super(TwoLayerMLP, self).__init__()
+        """
+        Init method.
+        """
+
+        self.in_features = in_features
+
+        self.n_hidden_relu = n_hidden_relu
+
+        self.fc1 = nn.Linear(in_features = self.in_features, out_features = self.n_hidden_relu, bias=True)
+
+        self.fc2 = nn.Linear(in_features = self.n_hidden_relu, out_features = 1, bias=True)
+
+    def forward(self, x):
+        """
+        The forward method.
+        """
+
+        # input is a batch of the size: [batch_size, 1, self.in_features],
+        # convert it to the size [batch_size, self.in_features] as expected by FC layer:
+        x = x.squeeze()
+
+        # first fully connected activated by ReLu:
+        x = self.fc1(x)
+        x = F.relu(x)
+
+        # second fully connected activated by sigmoid:
+        x = self.fc2(x)
+        x = F.sigmoid(x)
+
+        return x
+
+

bob/learn/pytorch/architectures/__init__.py

@@ -12,6 +12,7 @@ from .ConditionalGAN import ConditionalGAN_generator
 from .ConditionalGAN import ConditionalGAN_discriminator
 
 from .ConvAutoencoder import ConvAutoencoder
+from .TwoLayerMLP import TwoLayerMLP
 
 from .utils import weights_init