--- /dev/null
+# 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.
+# ==============================================================================
+"""Contains a model definition for AlexNet.
+
+This work was first described in:
+ ImageNet Classification with Deep Convolutional Neural Networks
+ Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton
+
+and later refined in:
+ One weird trick for parallelizing convolutional neural networks
+ Alex Krizhevsky, 2014
+
+Here we provide the implementation proposed in "One weird trick" and not
+"ImageNet Classification", as per the paper, the LRN layers have been removed.
+
+Usage:
+ with slim.arg_scope(alexnet.alexnet_v2_arg_scope()):
+ outputs, end_points = alexnet.alexnet_v2(inputs)
+
+@@alexnet_v2
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def alexnet_v2_arg_scope(weight_decay=0.0005):
+ with slim.arg_scope([slim.conv2d, slim.fully_connected],
+ activation_fn=tf.nn.relu,
+ biases_initializer=tf.constant_initializer(0.1),
+ weights_regularizer=slim.l2_regularizer(weight_decay)):
+ with slim.arg_scope([slim.conv2d], padding='SAME'):
+ with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
+ return arg_sc
+
+
+def alexnet_v2(inputs,
+ num_classes=1000,
+ is_training=True,
+ dropout_keep_prob=0.5,
+ spatial_squeeze=True,
+ scope='alexnet_v2'):
+ """AlexNet version 2.
+
+ Described in: http://arxiv.org/pdf/1404.5997v2.pdf
+ Parameters from:
+ github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
+ layers-imagenet-1gpu.cfg
+
+ Note: All the fully_connected layers have been transformed to conv2d layers.
+ To use in classification mode, resize input to 224x224. To use in fully
+ convolutional mode, set spatial_squeeze to false.
+ The LRN layers have been removed and change the initializers from
+ random_normal_initializer to xavier_initializer.
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ num_classes: number of predicted classes.
+ is_training: whether or not the model is being trained.
+ dropout_keep_prob: the probability that activations are kept in the dropout
+ layers during training.
+ spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+ outputs. Useful to remove unnecessary dimensions for classification.
+ scope: Optional scope for the variables.
+
+ Returns:
+ the last op containing the log predictions and end_points dict.
+ """
+ with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
+ end_points_collection = sc.name + '_end_points'
+ # Collect outputs for conv2d, fully_connected and max_pool2d.
+ with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+ outputs_collections=[end_points_collection]):
+ net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
+ scope='conv1')
+ net = slim.max_pool2d(net, [3, 3], 2, scope='pool1')
+ net = slim.conv2d(net, 192, [5, 5], scope='conv2')
+ net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
+ net = slim.conv2d(net, 384, [3, 3], scope='conv3')
+ net = slim.conv2d(net, 384, [3, 3], scope='conv4')
+ net = slim.conv2d(net, 256, [3, 3], scope='conv5')
+ net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')
+
+ # Use conv2d instead of fully_connected layers.
+ with slim.arg_scope([slim.conv2d],
+ weights_initializer=trunc_normal(0.005),
+ biases_initializer=tf.constant_initializer(0.1)):
+ net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
+ scope='fc6')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout6')
+ net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+ net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+ scope='dropout7')
+ net = slim.conv2d(net, num_classes, [1, 1],
+ activation_fn=None,
+ normalizer_fn=None,
+ biases_initializer=tf.zeros_initializer(),
+ scope='fc8')
+
+ # Convert end_points_collection into a end_point dict.
+ end_points = slim.utils.convert_collection_to_dict(end_points_collection)
+ if spatial_squeeze:
+ net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+ end_points[sc.name + '/fc8'] = net
+ return net, end_points
+alexnet_v2.default_image_size = 224