--- /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