New networks

bob/learn/tensorflow/network/LightCNN29.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+# @date: Wed 11 May 2016 09:39:36 CEST 
+
+import tensorflow as tf
+from bob.learn.tensorflow.layers import maxout
+
+class LightCNN29(object):
+    """Creates the graph for the Light CNN-9 in 
+
+       Wu, Xiang, et al. "A light CNN for deep face representation with noisy labels." arXiv preprint arXiv:1511.02683 (2015).
+    """
+    def __init__(self,
+                 seed=10,
+                 n_classes=10,
+                 device="/cpu:0",
+                 batch_norm=False):
+
+            self.seed = seed
+            self.device = device
+            self.batch_norm = batch_norm
+            self.n_classes = n_classes
+
+    def __call__(self, inputs, reuse=False):
+        slim = tf.contrib.slim
+
+        #with tf.device(self.device):
+
+        initializer = tf.contrib.layers.xavier_initializer(uniform=False, dtype=tf.float32, seed=self.seed)
+                    
+        graph = slim.conv2d(inputs, 96, [5, 5], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv1',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=48,
+                       name='Maxout1')
+
+        graph = slim.max_pool2d(graph, [2, 2], stride=2, padding="SAME", scope='Pool1')
+
+        ####
+
+        graph = slim.conv2d(graph, 96, [1, 1], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv2a',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=48,
+                       name='Maxout2a')
+
+        graph = slim.conv2d(graph, 192, [3, 3], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv2',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=96,
+                       name='Maxout2')
+
+        graph = slim.max_pool2d(graph, [2, 2], stride=2, padding="SAME", scope='Pool2')
+
+        #####
+
+        graph = slim.conv2d(graph, 192, [1, 1], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv3a',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=96,
+                       name='Maxout3a')
+
+        graph = slim.conv2d(graph, 384, [3, 3], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv3',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=192,
+                       name='Maxout3')
+
+        graph = slim.max_pool2d(graph, [2, 2], stride=2, padding="SAME", scope='Pool3')
+
+        #####
+
+        graph = slim.conv2d(graph, 384, [1, 1], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv4a',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=192,
+                       name='Maxout4a')
+
+        graph = slim.conv2d(graph, 256, [3, 3], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv4',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=128,
+                       name='Maxout4')
+
+        #####
+
+        graph = slim.conv2d(graph, 256, [1, 1], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv5a',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=128,
+                       name='Maxout5a')
+
+        graph = slim.conv2d(graph, 256, [3, 3], activation_fn=tf.nn.relu,
+                            stride=1,
+                            weights_initializer=initializer,
+                            scope='Conv5',
+                            reuse=reuse)
+
+        graph = maxout(graph,
+                       num_units=128,
+                       name='Maxout5')
+
+        graph = slim.max_pool2d(graph, [2, 2], stride=2, padding="SAME", scope='Pool4')
+
+        graph = slim.flatten(graph, scope='flatten1')
+
+        #graph = slim.dropout(graph, keep_prob=0.3, scope='dropout1')
+
+        graph = slim.fully_connected(graph, 512,
+                                     weights_initializer=initializer,
+                                     activation_fn=tf.nn.relu,
+                                     scope='fc1',
+                                     reuse=reuse)
+        graph = maxout(graph,
+                       num_units=256,
+                       name='Maxoutfc1')
+        
+        graph = slim.dropout(graph, keep_prob=0.3, scope='dropout1')
+
+        graph = slim.fully_connected(graph, self.n_classes,
+                                     weights_initializer=initializer,
+                                     activation_fn=None,
+                                     scope='fc2',
+                                     reuse=reuse)
+
+        return graph