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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.
# ==============================================================================
"""MNIST model training with TensorFlow eager execution.
See:
https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html
This program demonstrates training of the convolutional neural network model
defined in mnist.py with eager execution enabled.
If you are not interested in eager execution, you should ignore this file.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
# pylint: disable=g-bad-import-order
from absl import app as absl_app
from absl import flags
import tensorflow as tf
# pylint: enable=g-bad-import-order
from official.mnist import dataset as mnist_dataset
from official.mnist import mnist
from official.utils.flags import core as flags_core
from official.utils.misc import model_helpers
tfe = tf.contrib.eager
def loss(logits, labels):
return tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels))
def compute_accuracy(logits, labels):
predictions = tf.argmax(logits, axis=1, output_type=tf.int64)
labels = tf.cast(labels, tf.int64)
batch_size = int(logits.shape[0])
return tf.reduce_sum(
tf.cast(tf.equal(predictions, labels), dtype=tf.float32)) / batch_size
def train(model, optimizer, dataset, step_counter, log_interval=None):
"""Trains model on `dataset` using `optimizer`."""
start = time.time()
for (batch, (images, labels)) in enumerate(dataset):
with tf.contrib.summary.record_summaries_every_n_global_steps(
10, global_step=step_counter):
# Record the operations used to compute the loss given the input,
# so that the gradient of the loss with respect to the variables
# can be computed.
with tf.GradientTape() as tape:
logits = model(images, training=True)
loss_value = loss(logits, labels)
tf.contrib.summary.scalar('loss', loss_value)
tf.contrib.summary.scalar('accuracy', compute_accuracy(logits, labels))
grads = tape.gradient(loss_value, model.variables)
optimizer.apply_gradients(
zip(grads, model.variables), global_step=step_counter)
if log_interval and batch % log_interval == 0:
rate = log_interval / (time.time() - start)
print('Step #%d\tLoss: %.6f (%d steps/sec)' % (batch, loss_value, rate))
start = time.time()
def test(model, dataset):
"""Perform an evaluation of `model` on the examples from `dataset`."""
avg_loss = tfe.metrics.Mean('loss', dtype=tf.float32)
accuracy = tfe.metrics.Accuracy('accuracy', dtype=tf.float32)
for (images, labels) in dataset:
logits = model(images, training=False)
avg_loss(loss(logits, labels))
accuracy(
tf.argmax(logits, axis=1, output_type=tf.int64),
tf.cast(labels, tf.int64))
print('Test set: Average loss: %.4f, Accuracy: %4f%%\n' %
(avg_loss.result(), 100 * accuracy.result()))
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar('loss', avg_loss.result())
tf.contrib.summary.scalar('accuracy', accuracy.result())
def run_mnist_eager(flags_obj):
"""Run MNIST training and eval loop in eager mode.
Args:
flags_obj: An object containing parsed flag values.
"""
tf.enable_eager_execution()
model_helpers.apply_clean(flags.FLAGS)
# Automatically determine device and data_format
(device, data_format) = ('/gpu:0', 'channels_first')
if flags_obj.no_gpu or not tf.test.is_gpu_available():
(device, data_format) = ('/cpu:0', 'channels_last')
# If data_format is defined in FLAGS, overwrite automatically set value.
if flags_obj.data_format is not None:
data_format = flags_obj.data_format
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = mnist_dataset.train(flags_obj.data_dir).shuffle(60000).batch(
flags_obj.batch_size)
test_ds = mnist_dataset.test(flags_obj.data_dir).batch(
flags_obj.batch_size)
# Create the model and optimizer
model = mnist.create_model(data_format)
optimizer = tf.train.MomentumOptimizer(flags_obj.lr, flags_obj.momentum)
# Create file writers for writing TensorBoard summaries.
if flags_obj.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(flags_obj.output_dir, 'train')
test_dir = os.path.join(flags_obj.output_dir, 'eval')
tf.gfile.MakeDirs(flags_obj.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(
train_dir, flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(flags_obj.model_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tf.train.Checkpoint(
model=model, optimizer=optimizer, step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(flags_obj.model_dir))
# Train and evaluate for a set number of epochs.
with tf.device(device):
for _ in range(flags_obj.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
flags_obj.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1,
step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
def main(_):
run_mnist_eager(flags.FLAGS)
if __name__ == '__main__':
define_mnist_eager_flags()
absl_app.run(main=main)