Brain dump

bob/learn/tensorflow/layers/Conv2D.py

@@ -22,7 +22,7 @@ class Conv2D(Layer):
                  seed=10
                  ):
         """
-        Base constructor
+        Constructor
 
         **Parameters**
         input: Layer input

bob/learn/tensorflow/layers/FullyConnected.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+# @date: Wed 11 May 2016 17:38 CEST
+
+import tensoflow as tf
+from bob.learn.tensorflow.util import *
+from .Layer import Layer
+
+
+class FullyConnected(Layer):
+
+    """
+    2D Convolution
+    """
+
+    def __init__(self, input, activation=None,
+                 initialization='xavier',
+                 use_gpu=False,
+                 seed=10
+                 ):
+        """
+        Constructor
+
+        **Parameters**
+        input: Layer input
+        activation: Tensor Flow activation
+        initialization: Initialization type
+        use_gpu: Store data in the GPU
+        seed: Seed for the Random number generation
+        """
+        super(FullyConnected, self).__init__(input, initialization='xavier', use_gpu=False, seed=10)
+        self.activation = activation
+
+
+        if len(input.get_shape())==4:
+            self.W = create_weight_variables([kernel_size, kernel_size, 1, filters],
+                                         seed=seed, name="conv", use_gpu=use_gpu)
+
+        if activation is not None:
+            self.b = create_bias_variables([filters], name="bias", use_gpu=self.use_gpu)
+
+    def get_graph(self):
+        with tf.name_scope('fc'):
+            conv = tf.nn.conv2d(self.input, self.W, strides=[1, 1, 1, 1], padding='SAME')
+
+        if self.activation is not None:
+            with tf.name_scope('activation'):
+                non_linearity = tf.nn.tanh(tf.nn.bias_add(conv, self.b))
+
+        return non_linearity

bob/learn/tensorflow/layers/MaxPooling.py

@@ -10,4 +10,11 @@ from .Layer import Layer
 
 class MaxPooling(Layer):
 
-    
\ No newline at end of file
+    def __init__(self, input, use_gpu=False):
+        """
+        Constructor
+        """
+        super(MaxPooling, self).__init__(input, use_gpu=False)
+
+    def get_graph(self):
+        tf.nn.max_pool(self.input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')