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diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/cifarnet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/cifarnet.py
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--- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/cifarnet.py
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@@ -1,112 +0,0 @@
-# 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