119 lines
5.1 KiB
Python
119 lines
5.1 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the model definition for the OverFeat network.
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The definition for the network was obtained from:
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OverFeat: Integrated Recognition, Localization and Detection using
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Convolutional Networks
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Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
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Yann LeCun, 2014
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http://arxiv.org/abs/1312.6229
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Usage:
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with slim.arg_scope(overfeat.overfeat_arg_scope()):
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outputs, end_points = overfeat.overfeat(inputs)
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@@overfeat
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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slim = tf.contrib.slim
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trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
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def overfeat_arg_scope(weight_decay=0.0005):
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with slim.arg_scope([slim.conv2d, slim.fully_connected],
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activation_fn=tf.nn.relu,
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weights_regularizer=slim.l2_regularizer(weight_decay),
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biases_initializer=tf.zeros_initializer):
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with slim.arg_scope([slim.conv2d], padding='SAME'):
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with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
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return arg_sc
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def overfeat(inputs,
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num_classes=1000,
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is_training=True,
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dropout_keep_prob=0.5,
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spatial_squeeze=True,
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scope='overfeat'):
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"""Contains the model definition for the OverFeat network.
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The definition for the network was obtained from:
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OverFeat: Integrated Recognition, Localization and Detection using
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Convolutional Networks
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Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
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Yann LeCun, 2014
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http://arxiv.org/abs/1312.6229
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Note: All the fully_connected layers have been transformed to conv2d layers.
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To use in classification mode, resize input to 231x231. To use in fully
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convolutional mode, set spatial_squeeze to false.
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Args:
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inputs: a tensor of size [batch_size, height, width, channels].
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num_classes: number of predicted classes.
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is_training: whether or not the model is being trained.
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dropout_keep_prob: the probability that activations are kept in the dropout
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layers during training.
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spatial_squeeze: whether or not should squeeze the spatial dimensions of the
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outputs. Useful to remove unnecessary dimensions for classification.
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scope: Optional scope for the variables.
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Returns:
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the last op containing the log predictions and end_points dict.
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"""
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with tf.variable_scope(scope, 'overfeat', [inputs]) as sc:
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end_points_collection = sc.name + '_end_points'
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# Collect outputs for conv2d, fully_connected and max_pool2d
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with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
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outputs_collections=end_points_collection):
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net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
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scope='conv1')
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net = slim.max_pool2d(net, [2, 2], scope='pool1')
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net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
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net = slim.max_pool2d(net, [2, 2], scope='pool2')
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net = slim.conv2d(net, 512, [3, 3], scope='conv3')
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net = slim.conv2d(net, 1024, [3, 3], scope='conv4')
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net = slim.conv2d(net, 1024, [3, 3], scope='conv5')
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net = slim.max_pool2d(net, [2, 2], scope='pool5')
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with slim.arg_scope([slim.conv2d],
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weights_initializer=trunc_normal(0.005),
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biases_initializer=tf.constant_initializer(0.1)):
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# Use conv2d instead of fully_connected layers.
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net = slim.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout6')
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net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
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net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
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scope='dropout7')
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net = slim.conv2d(net, num_classes, [1, 1],
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activation_fn=None,
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normalizer_fn=None,
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biases_initializer=tf.zeros_initializer,
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scope='fc8')
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# Convert end_points_collection into a end_point dict.
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end_points = dict(tf.get_collection(end_points_collection))
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if spatial_squeeze:
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net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
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end_points[sc.name + '/fc8'] = net
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return net, end_points
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overfeat.default_image_size = 231
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