pcb defect detetcion application

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / networks / slim_nets / cifarnet.py

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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+# 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