def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images, labels = cifar10_model.distorted_inputs()
logits = cifar10_model.inference(images)
loss = cifar10_model.loss(logits, labels)
train_op = cifar10_model.train(loss, global_step)
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
tf.train.start_queue_runners(sess=sess)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
if step % 100 == 0:
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))
def train():
# 读取图片并带入网络计算
images, labels = cifar10_input.distorted_inputs(DATA_DIR, BATCH_SIZE)
t_logits = cifar10_model.inference(images)
# 损失值
t_loss = cifar10_model.loss(t_logits, labels)
tf.summary.scalar('loss_value', t_loss)
# 优化器
global_step = tf.Variable(0, trainable=False)
t_optimizer = cifar10_model.train_step(t_loss, global_step)
# 准确值
t_accuracy = cifar10_model.accuracy(t_logits, labels) # 训练集正确率计算
tf.summary.scalar('accuracy_value', t_accuracy)
merged = tf.summary.merge_all()
saver = tf.train.Saver()
Accuracy_value = []
Loss_value = []
# 设定定量的GPU显存使用量
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5
with tf.Session(config=config) as session:
session.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=session, coord=coord)
train_writer = tf.summary.FileWriter('./signal_GPU/logs',
session.graph)
for index in range(EPOCHES):
_, loss_value, accuracy_value, summary = session.run(
[t_optimizer, t_loss, t_accuracy, merged])
Accuracy_value.append(accuracy_value)
Loss_value.append(loss_value)
if index % 1000 == 0:
print('index:', index, ' loss_value:', loss_value,
' accuracy_value:', accuracy_value)
train_writer.add_summary(summary, index)
saver.save(session, os.path.join('./signal_GPU/saver/', 'model.ckpt'))
# accuracy value
plt.figure(figsize=(20, 10))
plt.plot(range(EPOCHES), Accuracy_value)
plt.xlabel('training step')
plt.ylabel('accuracy value')
plt.title('the accuracy value of training data')
plt.savefig('./signal_GPU/accuracy.png')
# loss value
plt.figure()
plt.plot(range(EPOCHES), Loss_value)
plt.xlabel('training value')
plt.ylabel('loss value')
plt.title('the value of the loss function of the training data')
plt.savefig('./signal_GPU/loss.png')
#
train_writer.close()
coord.request_stop()
coord.join(threads)
def model_fn(features, labels, mode, params):
logits = cifar10_model.inference(image_batch=features,
batch_size=params.get('batch_size'))
loss = cifar10_model.loss(logits, labels)
train_op = cifar10_model.train(loss, batch_size=params.get('batch_size'))
if mode == tf.estimator.ModeKeys.TRAIN:
logging_hook = tf.train.LoggingTensorHook({'loss': loss},
every_n_iter=1000)
return tf.estimator.EstimatorSpec(mode,
loss=loss,
train_op=train_op,
training_hooks=[logging_hook])
def run_training():
cifar10_data = Cifar10Data('./input_data')
images_pl = tf.placeholder(tf.float32, [
None, cifar10_model.IMAGE_BATCH_HEIGHT,
cifar10_model.IMAGE_BATCH_WIDTH, cifar10_model.IMAGE_BATCH_DEPTH
])
labels_pl = tf.placeholder(tf.int32)
keep_prob_pl = tf.placeholder(tf.float32)
learning_rate_pl = tf.placeholder(tf.float32)
is_test_pl = tf.placeholder(tf.bool)
iter_pl = tf.placeholder(tf.int32)
with tf.Session() as sess:
logits, update_ema = cifar10_model.inference(images_pl, iter_pl,
is_test_pl, keep_prob_pl)
total_loss = cifar10_model.loss(logits, labels_pl)
train_op = cifar10_model.train(total_loss, learning_rate_pl)
eval_op = cifar10_eval.evaluation(logits, labels_pl)
saver = tf.train.Saver()
summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(LOG_DIR, sess.graph)
sess.run(tf.global_variables_initializer())
# learning rate decay
max_learning_rate = 0.02 # 0.003
min_learning_rate = 0.0001
decay_speed = 1600.0 # 2000.0
for step in xrange(MAX_STEPS):
print('step %d/%d' % (step, MAX_STEPS))
start_time = time.time()
images_feed, labels_feed = cifar10_data.random_training_batch(
cifar10_model.BATCH_SIZE)
learning_rate = min_learning_rate + (max_learning_rate -
min_learning_rate) * math.exp(
-step / decay_speed)
images_feed = sess.run(
cifar10_model.random_distort_images(images_feed))
feed_dict = {
images_pl: images_feed,
labels_pl: labels_feed,
keep_prob_pl: 0.75,
learning_rate_pl: learning_rate,
is_test_pl: False,
iter_pl: step
}
sess.run(train_op, feed_dict=feed_dict)
feed_dict = {
images_pl: images_feed,
labels_pl: labels_feed,
keep_prob_pl: 1.0,
learning_rate_pl: learning_rate,
is_test_pl: False,
iter_pl: step
}
sess.run(update_ema, feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if (step + 1) % 100 == 0 or (step + 1) == MAX_STEPS:
train_eval_val, loss_value = sess.run([eval_op, total_loss],
feed_dict=feed_dict)
print('Step %d: loss = %.2f, lr = %f (%.3f sec)' %
(step + 1, loss_value, learning_rate, duration))
print('Training Data Eval: %.4f' % train_eval_val)
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# Evaluate the model periodically.
# feed_dict = {images_pl: data_sets.testing_image,
# labels_pl: data_sets.testing_label}
# test_eval_val = sess.run(eval_op, feed_dict=feed_dict)
test_eval_val, test_loss_val = cifar10_eval.mass_evaluation(
cifar10_data, sess, eval_op, total_loss, images_pl,
labels_pl, keep_prob_pl, is_test_pl)
print('Testing Data Eval: ' + str(test_eval_val) + ' loss: ' +
str(test_loss_val))
# Save a checkpoint periodically.
if (step + 1) % 1000 == 0 or (step + 1) == MAX_STEPS:
checkpoint_file = os.path.join(LOG_DIR, 'model.ckpt')
saver.save(sess, checkpoint_file, global_step=step)
summary_writer.close()
def train():
"""
train cifar10 for a number of steps
"""
with tf.Graph().as_default():
global_step = tf.train.get_checkpoint_state()
# get images and labels for cifar-10
# force input pipeline to CPU:0 to avoid operations sometimes ending up
# on GPU and resulting in a show down.
with tf.device('/cpu:0'):
images, labels = cifar10_model.distorted_inputs()
# build a graph that computes the logits predictions from
# the inference model.
logits = cifar10_model.inference(images)
# calculate loss.
loss = cifar10_model.loss(logits, labels)
# build a graph that trains the model with one batch of examples
# and updates the model parameters
train_op = cifar10_model.train(loss, global_step)
class _LoggerHook(tf.train.SessionRunHook):
"""
Logs loss and runtime.
"""
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs(loss) # asks for loss value.
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value = run_values.results
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[
tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()
],
config=tf.ConfigProto(log_device_placement=FLAGS.
log_device_placement)) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op)