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72 lines
2.6 KiB
72 lines
2.6 KiB
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains common code shared by all inception models.
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Usage of arg scope:
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with slim.arg_scope(inception_arg_scope()):
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logits, end_points = inception.inception_v3(images, num_classes,
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is_training=is_training)
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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slim = tf.contrib.slim
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def inception_arg_scope(weight_decay=0.00004,
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use_batch_norm=True,
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batch_norm_decay=0.9997,
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batch_norm_epsilon=0.001):
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"""Defines the default arg scope for inception models.
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Args:
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weight_decay: The weight decay to use for regularizing the model.
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use_batch_norm: "If `True`, batch_norm is applied after each convolution.
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batch_norm_decay: Decay for batch norm moving average.
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batch_norm_epsilon: Small float added to variance to avoid dividing by zero
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in batch norm.
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Returns:
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An `arg_scope` to use for the inception models.
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"""
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batch_norm_params = {
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# Decay for the moving averages.
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'decay': batch_norm_decay,
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# epsilon to prevent 0s in variance.
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'epsilon': batch_norm_epsilon,
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# collection containing update_ops.
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'updates_collections': tf.GraphKeys.UPDATE_OPS,
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}
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if use_batch_norm:
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normalizer_fn = slim.batch_norm
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normalizer_params = batch_norm_params
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else:
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normalizer_fn = None
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normalizer_params = {}
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# Set weight_decay for weights in Conv and FC layers.
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with slim.arg_scope([slim.conv2d, slim.fully_connected],
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weights_regularizer=slim.l2_regularizer(weight_decay)):
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with slim.arg_scope(
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[slim.conv2d],
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weights_initializer=slim.variance_scaling_initializer(),
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activation_fn=tf.nn.relu,
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normalizer_fn=normalizer_fn,
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normalizer_params=normalizer_params) as sc:
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return sc
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