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train_model.py
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train_model.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os.path
import math
import time
import numpy as np
from six.moves import xrange
import tensorflow as tf
import model
import tfrecord
import config
import eval_model
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir',
config.get_configs('global.conf', 'model', 'model_dir'),
"""Train model checkpoint dir.""")
tf.app.flags.DEFINE_string('eval_data_dir',
config.get_configs('global.conf', 'eval', 'eval_tfrecord_dir'),
"""Evaluation data log.""")
tf.app.flags.DEFINE_integer('max_steps',
int(config.get_configs('global.conf', 'train', 'max_steps')),
"""Max training steps.""")
tf.app.flags.DEFINE_boolean('log_device_placement',
False,
"""Log device placement.""")
tf.app.flags.DEFINE_string('train_data',
config.get_configs('global.conf', 'train', 'train_tfrecord_dir'),
"""Train data dir.""")
tf.app.flags.DEFINE_integer('train_num',
int(config.get_configs('global.conf', 'train', 'train_data_count')),
"""Total number of train data.""")
tf.app.flags.DEFINE_integer('eval_num',
int(config.get_configs('global.conf', 'eval', 'eval_data_count')),
"""Total number of train data.""")
tf.app.flags.DEFINE_float('keep_prob',
float(config.get_configs('global.conf', 'model', 'keep_prob')),
"""Keep probability in dropout computing.""")
tf.app.flags.DEFINE_boolean('create_train_eval_data',
False,
"""Create train data (80%) and evaluation data(20%) from original data.""")
tf.app.flags.DEFINE_integer('image_height',
int(config.get_configs('global.conf', 'dataset', 'resize_image_height')),
"""Resized image height.""")
tf.app.flags.DEFINE_integer('image_width',
int(config.get_configs('global.conf', 'dataset', 'resize_image_width')),
"""Resized image width.""")
tf.app.flags.DEFINE_integer('image_channels',
int(config.get_configs('global.conf', 'dataset', 'channels')),
"""Image channels.""")
tf.app.flags.DEFINE_integer('num_class',
int(config.get_configs('global.conf', 'dataset', 'num_class')),
"""Number of classes.""")
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# Get train images and labels
float_image, label = tfrecord.train_data_read(tfrecord_path=FLAGS.train_data)
images, labels = tfrecord.create_batch(float_image,label, count_num=FLAGS.train_num)
# Get evaluate images and labels
eval_float_image, eval_label = tfrecord.eval_data_read(tfrecord_path=FLAGS.eval_data_dir)
eval_images, eval_labels = tfrecord.create_batch(eval_float_image, eval_label, count_num=FLAGS.eval_num)
# Model inference
x = tf.placeholder(tf.float32, [FLAGS.batch_size, FLAGS.image_width, FLAGS.image_height, FLAGS.image_channels], name='x_input')
y_ = tf.placeholder(tf.int32, [FLAGS.batch_size, None], name='y_input')
keep_prob = tf.placeholder(tf.float32)
# logits = model.inference(images, FLAGS.keep_prob)
logits = model.inference(x, keep_prob)
# loss computing
loss = model.loss(logits, y_)
# accuracy compution
#accuracy = model.accuracy(model.inference(eval_images, 1), eval_labels)
accuracy = model.accuracy(logits, y_)
# train model
train_op = model.train(loss, global_step)
# save model
saver = tf.train.Saver(tf.global_variables())
# merge all summaries
summary_op = tf.summary.merge_all()
# initialize all variables
init = tf.initialize_all_variables()
# Run session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# start queue runners
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
graph_def=sess.graph_def)
accuracy_list = []
for step in xrange(FLAGS.max_steps):
if step % 10 == 0:
imgs, lbls = sess.run([eval_images, eval_labels])
summary_str, acc = sess.run([summary_op, accuracy],
feed_dict = {x: imgs,
y_: np.reshape(lbls, (FLAGS.batch_size, -1)),
keep_prob: 1.0})
summary_writer.add_summary(summary_str, step)
print('%s: step %d, accuracy = %.3f' % (datetime.now(), step, acc))
accuracy_list.append(acc)
else:
imgs, lbls = sess.run([images, labels])
if step % 100 == 99 or (step + 1) == FLAGS.max_steps:
summary_str, _ = sess.run([summary_op, train_op],
feed_dict = {x: imgs,
y_: np.reshape(lbls, (FLAGS.batch_size, -1)),
keep_prob: FLAGS.keep_prob})
summary_writer.add_summary(summary_str, step)
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
else:
start_time = time.time()
_, loss_value = sess.run([train_op, loss],
feed_dict = {x: imgs,
y_: np.reshape(lbls, (FLAGS.batch_size, -1)),
keep_prob: FLAGS.keep_prob})
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f ''sec/batch)')
print (format_str % (datetime.now(), step, loss_value, examples_per_sec, sec_per_batch))
for hoge in accuracy_list:
print(hoge)
def main(argv=None):
if FLAGS.create_train_eval_data:
tfrecord.create_train_eval_data()
train()
if __name__ == '__main__':
tf.app.run()