+++ /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 variant of the CIFAR-10 model definition."""
-
-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(stddev=stddev)
-
-
-def cifarnet(images, num_classes=10, is_training=False,
- dropout_keep_prob=0.5,
- prediction_fn=slim.softmax,
- scope='CifarNet'):
- """Creates a variant of the CifarNet model.
-
- Note that since the output is a set of 'logits', the values fall in the
- interval of (-infinity, infinity). Consequently, to convert the outputs to a
- probability distribution over the characters, one will need to convert them
- using the softmax function:
-
- logits = cifarnet.cifarnet(images, is_training=False)
- probabilities = tf.nn.softmax(logits)
- predictions = tf.argmax(logits, 1)
-
- Args:
- images: A batch of `Tensors` of size [batch_size, height, width, channels].
- num_classes: the number of classes in the dataset.
- is_training: specifies whether or not we're currently training the model.
- This variable will determine the behaviour of the dropout layer.
- dropout_keep_prob: the percentage of activation values that are retained.
- prediction_fn: a function to get predictions out of logits.
- scope: Optional variable_scope.
-
- Returns:
- logits: the pre-softmax activations, a tensor of size
- [batch_size, `num_classes`]
- end_points: a dictionary from components of the network to the corresponding
- activation.
- """
- end_points = {}
-
- with tf.variable_scope(scope, 'CifarNet', [images, num_classes]):
- net = slim.conv2d(images, 64, [5, 5], scope='conv1')
- end_points['conv1'] = net
- net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
- end_points['pool1'] = net
- net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1')
- net = slim.conv2d(net, 64, [5, 5], scope='conv2')
- end_points['conv2'] = net
- net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2')
- net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
- end_points['pool2'] = net
- net = slim.flatten(net)
- end_points['Flatten'] = net
- net = slim.fully_connected(net, 384, scope='fc3')
- end_points['fc3'] = net
- net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
- scope='dropout3')
- net = slim.fully_connected(net, 192, scope='fc4')
- end_points['fc4'] = net
- logits = slim.fully_connected(net, num_classes,
- biases_initializer=tf.zeros_initializer(),
- weights_initializer=trunc_normal(1/192.0),
- weights_regularizer=None,
- activation_fn=None,
- scope='logits')
-
- end_points['Logits'] = logits
- end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
-
- return logits, end_points
-cifarnet.default_image_size = 32
-
-
-def cifarnet_arg_scope(weight_decay=0.004):
- """Defines the default cifarnet argument scope.
-
- Args:
- weight_decay: The weight decay to use for regularizing the model.
-
- Returns:
- An `arg_scope` to use for the inception v3 model.
- """
- with slim.arg_scope(
- [slim.conv2d],
- weights_initializer=tf.truncated_normal_initializer(stddev=5e-2),
- activation_fn=tf.nn.relu):
- with slim.arg_scope(
- [slim.fully_connected],
- biases_initializer=tf.constant_initializer(0.1),
- weights_initializer=trunc_normal(0.04),
- weights_regularizer=slim.l2_regularizer(weight_decay),
- activation_fn=tf.nn.relu) as sc:
- return sc