--- /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 common code shared by all inception models.
+
+Usage of arg scope:
+ with slim.arg_scope(inception_arg_scope()):
+ logits, end_points = inception.inception_v3(images, num_classes,
+ is_training=is_training)
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def inception_arg_scope(weight_decay=0.00004,
+ use_batch_norm=True,
+ batch_norm_decay=0.9997,
+ batch_norm_epsilon=0.001):
+ """Defines the default arg scope for inception models.
+
+ Args:
+ weight_decay: The weight decay to use for regularizing the model.
+ use_batch_norm: "If `True`, batch_norm is applied after each convolution.
+ batch_norm_decay: Decay for batch norm moving average.
+ batch_norm_epsilon: Small float added to variance to avoid dividing by zero
+ in batch norm.
+
+ Returns:
+ An `arg_scope` to use for the inception models.
+ """
+ batch_norm_params = {
+ # Decay for the moving averages.
+ 'decay': batch_norm_decay,
+ # epsilon to prevent 0s in variance.
+ 'epsilon': batch_norm_epsilon,
+ # collection containing update_ops.
+ 'updates_collections': tf.GraphKeys.UPDATE_OPS,
+ }
+ if use_batch_norm:
+ normalizer_fn = slim.batch_norm
+ normalizer_params = batch_norm_params
+ else:
+ normalizer_fn = None
+ normalizer_params = {}
+ # Set weight_decay for weights in Conv and FC layers.
+ with slim.arg_scope([slim.conv2d, slim.fully_connected],
+ weights_regularizer=slim.l2_regularizer(weight_decay)):
+ with slim.arg_scope(
+ [slim.conv2d],
+ weights_initializer=slim.variance_scaling_initializer(),
+ activation_fn=tf.nn.relu,
+ normalizer_fn=normalizer_fn,
+ normalizer_params=normalizer_params) as sc:
+ return sc