MLP class and config to train it

This MR contains an MLP class, and config used to train it for autoencoder-based face PAD experiment.

bob/learn/pytorch/architectures/TwoLayerMLP.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: Olegs Nikisins
+"""
+#==============================================================================
+# Import here:
+
+from torch import nn
+
+import torch.nn.functional as F
+
+
+#==============================================================================
+# Define the network:
+
+class TwoLayerMLP(nn.Module):
+    """
+    A simple two-layer MLP for binary classification. The output activation
+    function is sigmoid.
+
+    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.
+
+    apply_sigmoid : bool
+        If set to ``True`` the sigmoid will be applied to the output of the
+        hidden FC layer. If ``False`` the sigmoid is not applied.
+    """
+
+    def __init__(self, in_features, n_hidden_relu, apply_sigmoid = True):
+        super(TwoLayerMLP, self).__init__()
+        """
+        Init method.
+
+        Parameters
+        ----------
+        in_features : int
+            Dimensionality of the input feature vectors.
+
+        n_hidden_relu : int
+            Number of ReLU units in the hidden layer of the MLP.
+
+        apply_sigmoid : bool
+            If set to ``True`` the sigmoid will be applied to the output of the
+            hidden FC layer. If ``False`` the sigmoid is not applied.
+            Default: ``True``.
+        """
+
+        self.in_features = in_features
+
+        self.n_hidden_relu = n_hidden_relu
+
+        self.apply_sigmoid = apply_sigmoid
+
+        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.
+
+        Parameters
+        ----------
+        x : :py:class:`torch.Tensor`
+            The batch to forward through the network. Size of the input batch
+            is [batch_size, 1, self.in_features].
+
+        Returns
+        -------
+        x : :py:class:`torch.Tensor`
+            Output of the MLP, class probability.
+        """
+
+        # 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)
+
+        if not self.apply_sigmoid:
+
+            return x
+
+        x = F.sigmoid(x)
+
+        return x
+
+