example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/cifarnet.py

   1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
   2 #
   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
   6 #
   7 #     http://www.apache.org/licenses/LICENSE-2.0
   8 #
   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 CIFAR-10 model definition."""
  16
  17 from __future__ import absolute_import
  18 from __future__ import division
  19 from __future__ import print_function
  20
  21 import tensorflow as tf
  22
  23 slim = tf.contrib.slim
  24
  25 trunc_normal = lambda stddev: tf.truncated_normal_initializer(stddev=stddev)
  26
  27
  28 def cifarnet(images, num_classes=10, is_training=False,
  29              dropout_keep_prob=0.5,
  30              prediction_fn=slim.softmax,
  31              scope='CifarNet'):
  32   """Creates a variant of the CifarNet model.
  33
  34   Note that since the output is a set of 'logits', the values fall in the
  35   interval of (-infinity, infinity). Consequently, to convert the outputs to a
  36   probability distribution over the characters, one will need to convert them
  37   using the softmax function:
  38
  39         logits = cifarnet.cifarnet(images, is_training=False)
  40         probabilities = tf.nn.softmax(logits)
  41         predictions = tf.argmax(logits, 1)
  42
  43   Args:
  44     images: A batch of `Tensors` of size [batch_size, height, width, channels].
  45     num_classes: the number of classes in the dataset.
  46     is_training: specifies whether or not we're currently training the model.
  47       This variable will determine the behaviour of the dropout layer.
  48     dropout_keep_prob: the percentage of activation values that are retained.
  49     prediction_fn: a function to get predictions out of logits.
  50     scope: Optional variable_scope.
  51
  52   Returns:
  53     logits: the pre-softmax activations, a tensor of size
  54       [batch_size, `num_classes`]
  55     end_points: a dictionary from components of the network to the corresponding
  56       activation.
  57   """
  58   end_points = {}
  59
  60   with tf.variable_scope(scope, 'CifarNet', [images, num_classes]):
  61     net = slim.conv2d(images, 64, [5, 5], scope='conv1')
  62     end_points['conv1'] = net
  63     net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
  64     end_points['pool1'] = net
  65     net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1')
  66     net = slim.conv2d(net, 64, [5, 5], scope='conv2')
  67     end_points['conv2'] = net
  68     net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2')
  69     net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
  70     end_points['pool2'] = net
  71     net = slim.flatten(net)
  72     end_points['Flatten'] = net
  73     net = slim.fully_connected(net, 384, scope='fc3')
  74     end_points['fc3'] = net
  75     net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
  76                        scope='dropout3')
  77     net = slim.fully_connected(net, 192, scope='fc4')
  78     end_points['fc4'] = net
  79     logits = slim.fully_connected(net, num_classes,
  80                                   biases_initializer=tf.zeros_initializer(),
  81                                   weights_initializer=trunc_normal(1/192.0),
  82                                   weights_regularizer=None,
  83                                   activation_fn=None,
  84                                   scope='logits')
  85
  86     end_points['Logits'] = logits
  87     end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
  88
  89   return logits, end_points
  90 cifarnet.default_image_size = 32
  91
  92
  93 def cifarnet_arg_scope(weight_decay=0.004):
  94   """Defines the default cifarnet argument scope.
  95
  96   Args:
  97     weight_decay: The weight decay to use for regularizing the model.
  98
  99   Returns:
 100     An `arg_scope` to use for the inception v3 model.
 101   """
 102   with slim.arg_scope(
 103       [slim.conv2d],
 104       weights_initializer=tf.truncated_normal_initializer(stddev=5e-2),
 105       activation_fn=tf.nn.relu):
 106     with slim.arg_scope(
 107         [slim.fully_connected],
 108         biases_initializer=tf.constant_initializer(0.1),
 109         weights_initializer=trunc_normal(0.04),
 110         weights_regularizer=slim.l2_regularizer(weight_decay),
 111         activation_fn=tf.nn.relu) as sc:
 112       return sc