VGG 16

bob/learn/tensorflow/network/VGG16.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 
+
+"""
+Class that creates the lenet architecture
+"""
+
+import tensorflow as tf
+from .SequenceNetwork import SequenceNetwork
+from ..layers import Conv2D, FullyConnected, MaxPooling, Dropout
+from bob.learn.tensorflow.initialization import Xavier
+from bob.learn.tensorflow.initialization import Constant
+
+
+class VGG16(SequenceNetwork):
+
+    def __init__(self,
+                 # First convolutional block
+                 conv1_1_kernel_size=3,
+                 conv1_1_output=64,
+
+                 conv1_2_kernel_size=3,
+                 conv1_2_output=64,
+
+                 # Second convolutional block
+                 conv2_1_kernel_size=3,
+                 conv2_1_output=128,
+
+                 conv2_2_kernel_size=3,
+                 conv2_2_output=128,
+
+                 # Third convolutional block
+                 conv3_1_kernel_size=3,
+                 conv3_1_output=256,
+
+                 conv3_2_kernel_size=3,
+                 conv3_2_output=256,
+
+                 conv3_3_kernel_size=3,
+                 conv3_3_output=256,
+
+                 # Forth convolutional block
+                 conv4_1_kernel_size=3,
+                 conv4_1_output=512,
+
+                 conv4_2_kernel_size=3,
+                 conv4_2_output=512,
+
+                 conv4_3_kernel_size=3,
+                 conv4_3_output=512,
+
+                 # Forth convolutional block
+                 conv5_1_kernel_size=3,
+                 conv5_1_output=512,
+
+                 conv5_2_kernel_size=3,
+                 conv5_2_output=512,
+
+                 conv5_3_kernel_size=3,
+                 conv5_3_output=512,
+
+                 fc6_output=4096,
+                 fc7_output=4096,
+                 n_classes=10,
+
+                 default_feature_layer="fc7",
+
+                 seed=10,
+                 use_gpu=False):
+        """
+        Create all the necessary variables for this CNN
+
+        **Parameters**
+            conv1_kernel_size=5,
+            conv1_output=32,
+
+            conv2_kernel_size=5,
+            conv2_output=64,
+
+            fc1_output=400,
+            n_classes=10
+
+            seed = 10
+        """
+        super(VGG16, self).__init__(default_feature_layer=default_feature_layer,
+                                              use_gpu=use_gpu)
+
+        # First convolutional block
+        self.conv1_1_kernel_size = conv1_1_kernel_size
+        self.conv1_1_output = conv1_1_output
+
+        self.conv1_2_kernel_size = conv1_2_kernel_size
+        self.conv1_2_output = conv1_2_output
+
+        # Second convolutional block
+        self.conv2_1_kernel_size = conv2_1_kernel_size
+        self.conv2_1_output = conv2_1_output
+
+        self.conv2_2_kernel_size = conv2_2_kernel_size
+        self.conv2_2_output = conv2_2_output
+
+        # Third convolutional block
+        self.conv3_1_kernel_size = conv3_1_kernel_size
+        self.conv3_1_output = conv3_1_output
+
+        self.conv3_2_kernel_size = conv3_2_kernel_size
+        self.conv3_2_output = conv3_2_output
+
+        self.conv3_3_kernel_size = conv3_3_kernel_size
+        self.conv3_3_output = conv3_3_output
+
+        # Forth convolutional block
+        self.conv4_1_kernel_size = conv4_1_kernel_size
+        self.conv4_1_output = conv4_1_output
+
+        self.conv4_2_kernel_size = conv4_2_kernel_size
+        self.conv4_2_output = conv4_2_output
+
+        self.conv4_3_kernel_size = conv4_3_kernel_size
+        self.conv4_3_output = conv4_3_output
+
+        # Forth convolutional block
+        self.conv5_1_kernel_size = conv5_1_kernel_size
+        self.conv5_1_output = conv5_1_output
+
+        self.conv5_2_kernel_size = conv5_2_kernel_size
+        self.conv5_2_output = conv5_2_output
+
+        self.conv5_3_kernel_size = conv5_3_kernel_size
+        self.conv5_3_output = conv5_3_output
+
+        self.fc6_output = fc6_output
+        self.fc7_output = fc7_output
+        self.n_classes = n_classes
+
+        # First convolutional
+        self.add(Conv2D(name="conv1_1", kernel_size=conv1_1_kernel_size,
+                        filters=conv1_1_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(Conv2D(name="conv1_2", kernel_size=conv1_2_kernel_size,
+                        filters=conv2_1_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed,  use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling1", strides=[1, 2, 2, 1]))
+
+        # Second convolutional
+        self.add(Conv2D(name="conv2_1", kernel_size=conv2_1_kernel_size,
+                        filters=conv2_1_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        self.add(Conv2D(name="conv2_2", kernel_size=conv2_2_kernel_size,
+                        filters=conv2_2_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling2", strides=[1, 2, 2, 1]))
+
+        # Third convolutional
+        self.add(Conv2D(name="conv3_1", kernel_size=conv3_1_kernel_size,
+                        filters=conv3_1_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        self.add(Conv2D(name="conv3_2", kernel_size=conv3_2_kernel_size,
+                        filters=conv3_2_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        self.add(Conv2D(name="conv3_3", kernel_size=conv3_3_kernel_size,
+                        filters=conv3_3_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling3", strides=[1, 2, 2, 1]))
+
+
+        # Forth convolutional
+        self.add(Conv2D(name="conv4_1", kernel_size=conv4_1_kernel_size,
+                        filters=conv4_1_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        self.add(Conv2D(name="conv4_2", kernel_size=conv4_2_kernel_size,
+                        filters=conv4_2_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        self.add(Conv2D(name="conv4_3", kernel_size=conv4_3_kernel_size,
+                        filters=conv4_3_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling4", strides=[1, 2, 2, 1]))
+
+        # Fifth convolutional
+        self.add(Conv2D(name="conv5_1", kernel_size=conv5_1_kernel_size,
+                        filters=conv5_1_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        self.add(Conv2D(name="conv5_2", kernel_size=conv5_2_kernel_size,
+                        filters=conv5_2_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+
+        self.add(Conv2D(name="conv5_3", kernel_size=conv5_3_kernel_size,
+                        filters=conv5_3_output,
+                        activation=tf.nn.relu,
+                        weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                        bias_initialization=Constant(use_gpu=self.use_gpu)
+                        ))
+        self.add(MaxPooling(name="pooling5", strides=[1, 2, 2, 1]))
+
+        self.add(FullyConnected(name="fc6", output_dim=fc6_output,
+                                activation=tf.nn.relu,
+                                weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                                bias_initialization=Constant(use_gpu=self.use_gpu)
+                                ))
+        self.add(Dropout(name="dropout", keep_prob=0.5))
+        self.add(FullyConnected(name="fc7", output_dim=fc7_output,
+                                activation=tf.nn.relu,
+                                weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                                bias_initialization=Constant(use_gpu=self.use_gpu)
+                                ))
+        self.add(Dropout(name="dropout", keep_prob=0.5))
+        self.add(FullyConnected(name="fc8", output_dim=n_classes,
+                                activation=None,
+                                weights_initialization=Xavier(seed=seed, use_gpu=self.use_gpu),
+                                bias_initialization=Constant(use_gpu=self.use_gpu)))