+++ /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 the model definition for the OverFeat network.
-
-The definition for the network was obtained from:
- OverFeat: Integrated Recognition, Localization and Detection using
- Convolutional Networks
- Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
- Yann LeCun, 2014
- http://arxiv.org/abs/1312.6229
-
-Usage:
- with slim.arg_scope(overfeat.overfeat_arg_scope()):
- outputs, end_points = overfeat.overfeat(inputs)
-
-@@overfeat
-"""
-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 overfeat_arg_scope(weight_decay=0.0005):
- with slim.arg_scope([slim.conv2d, slim.fully_connected],
- activation_fn=tf.nn.relu,
- weights_regularizer=slim.l2_regularizer(weight_decay),
- biases_initializer=tf.zeros_initializer()):
- with slim.arg_scope([slim.conv2d], padding='SAME'):
- with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
- return arg_sc
-
-
-def overfeat(inputs,
- num_classes=1000,
- is_training=True,
- dropout_keep_prob=0.5,
- spatial_squeeze=True,
- scope='overfeat'):
- """Contains the model definition for the OverFeat network.
-
- The definition for the network was obtained from:
- OverFeat: Integrated Recognition, Localization and Detection using
- Convolutional Networks
- Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
- Yann LeCun, 2014
- http://arxiv.org/abs/1312.6229
-
- Note: All the fully_connected layers have been transformed to conv2d layers.
- To use in classification mode, resize input to 231x231. To use in fully
- convolutional mode, set spatial_squeeze to false.
-
- 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, 'overfeat', [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, [2, 2], scope='pool1')
- net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
- net = slim.max_pool2d(net, [2, 2], scope='pool2')
- net = slim.conv2d(net, 512, [3, 3], scope='conv3')
- net = slim.conv2d(net, 1024, [3, 3], scope='conv4')
- net = slim.conv2d(net, 1024, [3, 3], scope='conv5')
- net = slim.max_pool2d(net, [2, 2], scope='pool5')
- with slim.arg_scope([slim.conv2d],
- weights_initializer=trunc_normal(0.005),
- biases_initializer=tf.constant_initializer(0.1)):
- # Use conv2d instead of fully_connected layers.
- net = slim.conv2d(net, 3072, [6, 6], 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
-overfeat.default_image_size = 231