Transfer learning on TensorFlow-Slim image classification model library
# 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_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow.compat.v1 as tfimport tf_slim as slimdef inception_arg_scope( weight_decay=0.00004, use_batch_norm=True, batch_norm_decay=0.9997, batch_norm_epsilon=0.001, activation_fn=tf.nn.relu, batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS, batch_norm_scale=False): """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. activation_fn: Activation function for conv2d. batch_norm_updates_collections: Collection for the update ops for batch norm. batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the activations in the batch normalization layer. 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': batch_norm_updates_collections, # use fused batch norm if possible. 'fused': None, 'scale': batch_norm_scale, } 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=activation_fn, normalizer_fn=normalizer_fn, normalizer_params=normalizer_params) as sc: return sc