--- a
+++ b/inception_resnet_v2.py
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+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# The content is derived from https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py
+# ==============================================================================
+
+"""Contains the definition of the Inception Resnet V2 architecture.
+
+As described in http://arxiv.org/abs/1602.07261.
+
+  Inception-v4, Inception-ResNet and the Impact of Residual Connections
+    on Learning
+  Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def block35(net, scale = 1.0, activation_fn = tf.nn.relu, scope = None, reuse = None):
+    """Builds the 35x35 resnet block."""
+    with tf.variable_scope(scope, 'Block35', [net], reuse = reuse):
+        with tf.variable_scope('Branch_0'):
+            tower_conv = slim.conv2d(net, 32, 1, scope = 'Conv2d_1x1')
+        with tf.variable_scope('Branch_1'):
+            tower_conv1_0 = slim.conv2d(net, 32, 1, scope = 'Conv2d_0a_1x1')
+            tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope = 'Conv2d_0b_3x3')
+        with tf.variable_scope('Branch_2'):
+            tower_conv2_0 = slim.conv2d(net, 32, 1, scope = 'Conv2d_0a_1x1')
+            tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope = 'Conv2d_0b_3x3')
+            tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope = 'Conv2d_0c_3x3')
+        mixed = tf.concat(axis = 3, values = [tower_conv, tower_conv1_1, tower_conv2_2])
+        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn = None,
+                         activation_fn = None, scope = 'Conv2d_1x1')
+        net += scale * up
+        if activation_fn:
+            net = activation_fn(net)
+    return net
+
+
+def block17(net, scale = 1.0, activation_fn = tf.nn.relu, scope = None, reuse = None):
+    """Builds the 17x17 resnet block."""
+    with tf.variable_scope(scope, 'Block17', [net], reuse = reuse):
+        with tf.variable_scope('Branch_0'):
+            tower_conv = slim.conv2d(net, 192, 1, scope = 'Conv2d_1x1')
+        with tf.variable_scope('Branch_1'):
+            tower_conv1_0 = slim.conv2d(net, 128, 1, scope = 'Conv2d_0a_1x1')
+            tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
+                                        scope = 'Conv2d_0b_1x7')
+            tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
+                                        scope = 'Conv2d_0c_7x1')
+        mixed = tf.concat(axis = 3, values = [tower_conv, tower_conv1_2])
+        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn = None,
+                         activation_fn = None, scope = 'Conv2d_1x1')
+        net += scale * up
+        if activation_fn:
+            net = activation_fn(net)
+    return net
+
+
+def block8(net, scale = 1.0, activation_fn = tf.nn.relu, scope = None, reuse = None):
+    """Builds the 8x8 resnet block."""
+    with tf.variable_scope(scope, 'Block8', [net], reuse = reuse):
+        with tf.variable_scope('Branch_0'):
+            tower_conv = slim.conv2d(net, 192, 1, scope = 'Conv2d_1x1')
+        with tf.variable_scope('Branch_1'):
+            tower_conv1_0 = slim.conv2d(net, 192, 1, scope = 'Conv2d_0a_1x1')
+            tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3],
+                                        scope = 'Conv2d_0b_1x3')
+            tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
+                                        scope = 'Conv2d_0c_3x1')
+        mixed = tf.concat(axis = 3, values = [tower_conv, tower_conv1_2])
+        up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn = None,
+                         activation_fn = None, scope = 'Conv2d_1x1')
+        net += scale * up
+        if activation_fn:
+            net = activation_fn(net)
+    return net
+
+
+def inception_resnet_v2(inputs, is_training = True,
+                            reuse = None,
+                            scope = 'InceptionResnetV2'):
+    """Creates the Inception Resnet V2 model.
+
+  Args:
+    inputs: a 4-D tensor of size [batch_size, height, width, 3].
+    num_classes: number of predicted classes.
+    is_training: whether is training or not.
+    dropout_keep_prob: float, the fraction to keep before final layer.
+    reuse: whether or not the network and its variables should be reused. To be
+      able to reuse 'scope' must be given.
+    scope: Optional variable_scope.
+
+  Returns:
+    logits: the logits outputs of the model.
+    end_points: the set of end_points from the inception model.
+  """
+    end_points = { }
+
+    with tf.variable_scope(scope, 'InceptionResnetV2', [inputs], reuse = reuse):
+        with slim.arg_scope([slim.batch_norm, slim.dropout],
+                            is_training = is_training):
+            with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+                                stride = 1, padding = 'SAME'):
+                # 149 x 149 x 32
+                net = slim.conv2d(inputs, 32, 3, stride = 2, padding = 'VALID',
+                                  scope = 'Conv2d_1a_3x3')
+                end_points['Conv2d_1a_3x3'] = net
+                # 147 x 147 x 32
+                net = slim.conv2d(net, 32, 3, padding = 'VALID',
+                                  scope = 'Conv2d_2a_3x3')
+                end_points['Conv2d_2a_3x3'] = net
+                # 147 x 147 x 64
+                net = slim.conv2d(net, 64, 3, scope = 'Conv2d_2b_3x3')
+                end_points['Conv2d_2b_3x3'] = net
+                # 73 x 73 x 64
+                net = slim.max_pool2d(net, 3, stride = 2, padding = 'VALID',
+                                      scope = 'MaxPool_3a_3x3')
+                end_points['MaxPool_3a_3x3'] = net
+                # 73 x 73 x 80
+                net = slim.conv2d(net, 80, 1, padding = 'VALID',
+                                  scope = 'Conv2d_3b_1x1')
+                end_points['Conv2d_3b_1x1'] = net
+                # 71 x 71 x 192
+                net = slim.conv2d(net, 192, 3, padding = 'VALID',
+                                  scope = 'Conv2d_4a_3x3')
+                end_points['Conv2d_4a_3x3'] = net
+                # 35 x 35 x 192
+                net = slim.max_pool2d(net, 3, stride = 2, padding = 'VALID',
+                                      scope = 'MaxPool_5a_3x3')
+                end_points['MaxPool_5a_3x3'] = net
+
+                # 35 x 35 x 320
+                with tf.variable_scope('Mixed_5b'):
+                    with tf.variable_scope('Branch_0'):
+                        tower_conv = slim.conv2d(net, 96, 1, scope = 'Conv2d_1x1')
+                    with tf.variable_scope('Branch_1'):
+                        tower_conv1_0 = slim.conv2d(net, 48, 1, scope = 'Conv2d_0a_1x1')
+                        tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
+                                                    scope = 'Conv2d_0b_5x5')
+                    with tf.variable_scope('Branch_2'):
+                        tower_conv2_0 = slim.conv2d(net, 64, 1, scope = 'Conv2d_0a_1x1')
+                        tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
+                                                    scope = 'Conv2d_0b_3x3')
+                        tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
+                                                    scope = 'Conv2d_0c_3x3')
+                    with tf.variable_scope('Branch_3'):
+                        tower_pool = slim.avg_pool2d(net, 3, stride = 1, padding = 'SAME',
+                                                     scope = 'AvgPool_0a_3x3')
+                        tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
+                                                   scope = 'Conv2d_0b_1x1')
+                    net = tf.concat(axis = 3, values = [tower_conv, tower_conv1_1,
+                                        tower_conv2_2, tower_pool_1])
+
+                end_points['Mixed_5b'] = net
+                net = slim.repeat(net, 10, block35, scale = 0.17)
+
+                # 17 x 17 x 1024
+                with tf.variable_scope('Mixed_6a'):
+                    with tf.variable_scope('Branch_0'):
+                        tower_conv = slim.conv2d(net, 384, 3, stride = 2, padding = 'VALID',
+                                                 scope = 'Conv2d_1a_3x3')
+                    with tf.variable_scope('Branch_1'):
+                        tower_conv1_0 = slim.conv2d(net, 256, 1, scope = 'Conv2d_0a_1x1')
+                        tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
+                                                    scope = 'Conv2d_0b_3x3')
+                        tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
+                                                    stride = 2, padding = 'VALID',
+                                                    scope = 'Conv2d_1a_3x3')
+                    with tf.variable_scope('Branch_2'):
+                        tower_pool = slim.max_pool2d(net, 3, stride = 2, padding = 'VALID',
+                                                     scope = 'MaxPool_1a_3x3')
+                    net = tf.concat(axis = 3, values = [tower_conv, tower_conv1_2, tower_pool])
+
+                end_points['Mixed_6a'] = net
+                net = slim.repeat(net, 20, block17, scale = 0.10)
+
+                end_points['BeforeAux'] = net
+
+                # Auxiliary tower
+                with tf.variable_scope('AuxLogits'):
+                    aux = slim.avg_pool2d(net, 5, stride = 1, padding = 'SAME',
+                                          scope = 'Conv2d_1a_3x3')
+                    aux = slim.conv2d(aux, 128, 1, scope = 'Conv2d_1b_1x1')
+                    aux = slim.conv2d(aux, 768, 5,
+                                      padding = 'SAME', scope = 'Conv2d_2a_5x5')
+
+                    end_points['AuxBeforeScoring'] = aux
+
+        return aux, end_points
+
+inception_resnet_v2.default_image_size = 299
+
+
+def inception_resnet_v2_arg_scope(weight_decay = 0.00004,
+                                  batch_norm_decay = 0.9997,
+                                  batch_norm_epsilon = 0.001):
+    """Yields the scope with the default parameters for inception_resnet_v2.
+
+  Args:
+    weight_decay: the weight decay for weights variables.
+    batch_norm_decay: decay for the moving average of batch_norm momentums.
+    batch_norm_epsilon: small float added to variance to avoid dividing by zero.
+
+  Returns:
+    a arg_scope with the parameters needed for inception_resnet_v2.
+  """
+    # Set weight_decay for weights in conv2d and fully_connected layers.
+
+    with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                        weights_regularizer = slim.l2_regularizer(weight_decay),
+                        biases_regularizer = slim.l2_regularizer(weight_decay)):
+        batch_norm_params = {
+            'decay': batch_norm_decay,
+            'epsilon': batch_norm_epsilon
+        }
+        # Set activation_fn and parameters for batch_norm.
+        with slim.arg_scope([slim.conv2d], activation_fn = tf.nn.relu,
+                            normalizer_fn = slim.batch_norm,
+                            normalizer_params = batch_norm_params) as scope:
+            return scope