1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
7 # http://www.apache.org/licenses/LICENSE-2.0
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 # ==============================================================================
15 """Contains a variant of the LeNet model definition."""
17 from __future__ import absolute_import
18 from __future__ import division
19 from __future__ import print_function
21 import tensorflow as tf
23 slim = tf.contrib.slim
26 def lenet(images, num_classes=10, is_training=False,
27 dropout_keep_prob=0.5,
28 prediction_fn=slim.softmax,
30 """Creates a variant of the LeNet model.
32 Note that since the output is a set of 'logits', the values fall in the
33 interval of (-infinity, infinity). Consequently, to convert the outputs to a
34 probability distribution over the characters, one will need to convert them
35 using the softmax function:
37 logits = lenet.lenet(images, is_training=False)
38 probabilities = tf.nn.softmax(logits)
39 predictions = tf.argmax(logits, 1)
42 images: A batch of `Tensors` of size [batch_size, height, width, channels].
43 num_classes: the number of classes in the dataset.
44 is_training: specifies whether or not we're currently training the model.
45 This variable will determine the behaviour of the dropout layer.
46 dropout_keep_prob: the percentage of activation values that are retained.
47 prediction_fn: a function to get predictions out of logits.
48 scope: Optional variable_scope.
51 logits: the pre-softmax activations, a tensor of size
52 [batch_size, `num_classes`]
53 end_points: a dictionary from components of the network to the corresponding
58 with tf.variable_scope(scope, 'LeNet', [images, num_classes]):
59 net = slim.conv2d(images, 32, [5, 5], scope='conv1')
60 net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
61 net = slim.conv2d(net, 64, [5, 5], scope='conv2')
62 net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
63 net = slim.flatten(net)
64 end_points['Flatten'] = net
66 net = slim.fully_connected(net, 1024, scope='fc3')
67 net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
69 logits = slim.fully_connected(net, num_classes, activation_fn=None,
72 end_points['Logits'] = logits
73 end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
75 return logits, end_points
76 lenet.default_image_size = 28
79 def lenet_arg_scope(weight_decay=0.0):
80 """Defines the default lenet argument scope.
83 weight_decay: The weight decay to use for regularizing the model.
86 An `arg_scope` to use for the inception v3 model.
89 [slim.conv2d, slim.fully_connected],
90 weights_regularizer=slim.l2_regularizer(weight_decay),
91 weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
92 activation_fn=tf.nn.relu) as sc: