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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.
# ==============================================================================
"""Train and Eval the MNIST network.
This version is like fully_connected_feed.py but uses data converted
to a TFRecords file containing tf.train.Example protocol buffers.
See:
https://www.tensorflow.org/programmers_guide/reading_data#reading_from_files
for context.
YOU MUST run convert_to_records before running this (but you only need to
run it once).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import mnist
# Basic model parameters as external flags.
FLAGS = None
# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'
def decode(serialized_example):
"""Parses an image and label from the given `serialized_example`."""
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape((mnist.IMAGE_PIXELS))
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.int32)
return image, label
def augment(image, label):
"""Placeholder for data augmentation."""
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
return image, label
def normalize(image, label):
"""Convert `image` from [0, 255] -> [-0.5, 0.5] floats."""
image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
return image, label
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
This function creates a one_shot_iterator, meaning that it will only iterate
over the dataset once. On the other hand there is no special initialization
required.
"""
if not num_epochs:
num_epochs = None
filename = os.path.join(FLAGS.train_dir, TRAIN_FILE
if train else VALIDATION_FILE)
with tf.name_scope('input'):
# TFRecordDataset opens a binary file and reads one record at a time.
# `filename` could also be a list of filenames, which will be read in order.
dataset = tf.data.TFRecordDataset(filename)
# The map transformation takes a function and applies it to every element
# of the dataset.
dataset = dataset.map(decode)
dataset = dataset.map(augment)
dataset = dataset.map(normalize)
# The shuffle transformation uses a finite-sized buffer to shuffle elements
# in memory. The parameter is the number of elements in the buffer. For
# completely uniform shuffling, set the parameter to be the same as the
# number of elements in the dataset.
dataset = dataset.shuffle(1000 + 3 * batch_size)
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
iterator = dataset.make_one_shot_iterator()
return iterator.get_next()
def run_training():
"""Train MNIST for a number of steps."""
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Input images and labels.
image_batch, label_batch = inputs(
train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(image_batch, FLAGS.hidden1, FLAGS.hidden2)
# Add to the Graph the loss calculation.
loss = mnist.loss(logits, label_batch)
# Add to the Graph operations that train the model.
train_op = mnist.training(loss, FLAGS.learning_rate)
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# Create a session for running operations in the Graph.
with tf.Session() as sess:
# Initialize the variables (the trained variables and the
# epoch counter).
sess.run(init_op)
try:
step = 0
while True: # Train until OutOfRangeError
start_time = time.time()
# Run one step of the model. The return values are
# the activations from the `train_op` (which is
# discarded) and the `loss` op. To inspect the values
# of your ops or variables, you may include them in
# the list passed to sess.run() and the value tensors
# will be returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
# Print an overview fairly often.
if step % 100 == 0:
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
duration))
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs,
step))
def main(_):
run_training()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--learning_rate',
type=float,
default=0.01,
help='Initial learning rate.')
parser.add_argument(
'--num_epochs',
type=int,
default=2,
help='Number of epochs to run trainer.')
parser.add_argument(
'--hidden1',
type=int,
default=128,
help='Number of units in hidden layer 1.')
parser.add_argument(
'--hidden2',
type=int,
default=32,
help='Number of units in hidden layer 2.')
parser.add_argument('--batch_size', type=int, default=100, help='Batch size.')
parser.add_argument(
'--train_dir',
type=str,
default='/tmp/data',
help='Directory with the training data.')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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