101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
# 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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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 deep_cnn
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import input # pylint: disable=redefined-builtin
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import metrics
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import tensorflow.compat.v1 as tf
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tf.flags.DEFINE_string('dataset', 'svhn', 'The name of the dataset to use')
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tf.flags.DEFINE_integer('nb_labels', 10, 'Number of output classes')
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tf.flags.DEFINE_string('data_dir','/tmp','Temporary storage')
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tf.flags.DEFINE_string('train_dir','/tmp/train_dir',
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'Where model ckpt are saved')
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tf.flags.DEFINE_integer('max_steps', 3000, 'Number of training steps to run.')
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tf.flags.DEFINE_integer('nb_teachers', 50, 'Teachers in the ensemble.')
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tf.flags.DEFINE_integer('teacher_id', 0, 'ID of teacher being trained.')
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tf.flags.DEFINE_boolean('deeper', False, 'Activate deeper CNN model')
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FLAGS = tf.flags.FLAGS
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def train_teacher(dataset, nb_teachers, teacher_id):
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"""
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This function trains a teacher (teacher id) among an ensemble of nb_teachers
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models for the dataset specified.
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:param dataset: string corresponding to dataset (svhn, cifar10)
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:param nb_teachers: total number of teachers in the ensemble
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:param teacher_id: id of the teacher being trained
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:return: True if everything went well
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"""
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# If working directories do not exist, create them
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assert input.create_dir_if_needed(FLAGS.data_dir)
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assert input.create_dir_if_needed(FLAGS.train_dir)
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# Load the dataset
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if dataset == 'svhn':
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train_data,train_labels,test_data,test_labels = input.ld_svhn(extended=True)
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elif dataset == 'cifar10':
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train_data, train_labels, test_data, test_labels = input.ld_cifar10()
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elif dataset == 'mnist':
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train_data, train_labels, test_data, test_labels = input.ld_mnist()
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else:
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print("Check value of dataset flag")
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return False
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# Retrieve subset of data for this teacher
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data, labels = input.partition_dataset(train_data,
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train_labels,
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nb_teachers,
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teacher_id)
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print("Length of training data: " + str(len(labels)))
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# Define teacher checkpoint filename and full path
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if FLAGS.deeper:
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filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt'
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else:
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filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt'
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ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + filename
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# Perform teacher training
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assert deep_cnn.train(data, labels, ckpt_path)
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# Append final step value to checkpoint for evaluation
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ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)
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# Retrieve teacher probability estimates on the test data
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teacher_preds = deep_cnn.softmax_preds(test_data, ckpt_path_final)
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# Compute teacher accuracy
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precision = metrics.accuracy(teacher_preds, test_labels)
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print('Precision of teacher after training: ' + str(precision))
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return True
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def main(argv=None): # pylint: disable=unused-argument
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# Make a call to train_teachers with values specified in flags
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assert train_teacher(FLAGS.dataset, FLAGS.nb_teachers, FLAGS.teacher_id)
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if __name__ == '__main__':
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tf.app.run()
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