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# Copyright 2018 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.
# ==============================================================================
"""Helper functions used for training AutoAugment models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
def setup_loss(logits, labels):
"""Returns the cross entropy for the given `logits` and `labels`."""
predictions = tf.nn.softmax(logits)
cost = tf.losses.softmax_cross_entropy(onehot_labels=labels,
logits=logits)
return predictions, cost
def decay_weights(cost, weight_decay_rate):
"""Calculates the loss for l2 weight decay and adds it to `cost`."""
costs = []
for var in tf.trainable_variables():
costs.append(tf.nn.l2_loss(var))
cost += tf.multiply(weight_decay_rate, tf.add_n(costs))
return cost
def eval_child_model(session, model, data_loader, mode):
"""Evaluates `model` on held out data depending on `mode`.
Args:
session: TensorFlow session the model will be run with.
model: TensorFlow model that will be evaluated.
data_loader: DataSet object that contains data that `model` will
evaluate.
mode: Will `model` either evaluate validation or test data.
Returns:
Accuracy of `model` when evaluated on the specified dataset.
Raises:
ValueError: if invalid dataset `mode` is specified.
"""
if mode == 'val':
images = data_loader.val_images
labels = data_loader.val_labels
elif mode == 'test':
images = data_loader.test_images
labels = data_loader.test_labels
else:
raise ValueError('Not valid eval mode')
assert len(images) == len(labels)
tf.logging.info('model.batch_size is {}'.format(model.batch_size))
assert len(images) % model.batch_size == 0
eval_batches = int(len(images) / model.batch_size)
for i in range(eval_batches):
eval_images = images[i * model.batch_size:(i + 1) * model.batch_size]
eval_labels = labels[i * model.batch_size:(i + 1) * model.batch_size]
_ = session.run(
model.eval_op,
feed_dict={
model.images: eval_images,
model.labels: eval_labels,
})
return session.run(model.accuracy)
def cosine_lr(learning_rate, epoch, iteration, batches_per_epoch, total_epochs):
"""Cosine Learning rate.
Args:
learning_rate: Initial learning rate.
epoch: Current epoch we are one. This is one based.
iteration: Current batch in this epoch.
batches_per_epoch: Batches per epoch.
total_epochs: Total epochs you are training for.
Returns:
The learning rate to be used for this current batch.
"""
t_total = total_epochs * batches_per_epoch
t_cur = float(epoch * batches_per_epoch + iteration)
return 0.5 * learning_rate * (1 + np.cos(np.pi * t_cur / t_total))
def get_lr(curr_epoch, hparams, iteration=None):
"""Returns the learning rate during training based on the current epoch."""
assert iteration is not None
batches_per_epoch = int(hparams.train_size / hparams.batch_size)
lr = cosine_lr(hparams.lr, curr_epoch, iteration, batches_per_epoch,
hparams.num_epochs)
return lr
def run_epoch_training(session, model, data_loader, curr_epoch):
"""Runs one epoch of training for the model passed in.
Args:
session: TensorFlow session the model will be run with.
model: TensorFlow model that will be evaluated.
data_loader: DataSet object that contains data that `model` will
evaluate.
curr_epoch: How many of epochs of training have been done so far.
Returns:
The accuracy of 'model' on the training set
"""
steps_per_epoch = int(model.hparams.train_size / model.hparams.batch_size)
tf.logging.info('steps per epoch: {}'.format(steps_per_epoch))
curr_step = session.run(model.global_step)
assert curr_step % steps_per_epoch == 0
# Get the current learning rate for the model based on the current epoch
curr_lr = get_lr(curr_epoch, model.hparams, iteration=0)
tf.logging.info('lr of {} for epoch {}'.format(curr_lr, curr_epoch))
for step in xrange(steps_per_epoch):
curr_lr = get_lr(curr_epoch, model.hparams, iteration=(step + 1))
# Update the lr rate variable to the current LR.
model.lr_rate_ph.load(curr_lr, session=session)
if step % 20 == 0:
tf.logging.info('Training {}/{}'.format(step, steps_per_epoch))
train_images, train_labels = data_loader.next_batch()
_, step, _ = session.run(
[model.train_op, model.global_step, model.eval_op],
feed_dict={
model.images: train_images,
model.labels: train_labels,
})
train_accuracy = session.run(model.accuracy)
tf.logging.info('Train accuracy: {}'.format(train_accuracy))
return train_accuracy
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