def evaluate():
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
# Get images and labels for CIFAR-10.
eval_data = True
label_enqueue, images, labels = load_input.inputs(eval_data,distorted=False)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = model.rnn_model(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = {}
for v in tf.all_variables():
if v in tf.trainable_variables():
restore_name = variable_averages.average_name(v)
else:
restore_name = v.op.name
variables_to_restore[restore_name] = v
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
graph_def = tf.get_default_graph().as_graph_def()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op, label_enqueue)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def train():
with tf.Graph().as_default(), tf.device('/gpu:0'):
global_step = tf.get_variable(
'global_step',[],
initializer=tf.constant_initializer(0), trainable=False)
eval_data = False
label_enqueue, images, labels = load_input.inputs(eval_data, distorted=True)
# Build a Graph that computes the logits predictions from the
# inference model.
logits,glimpse_vars= model.rnn_model(images)
# Calculate loss.
loss = model.loss(logits, labels)
n = tf.zeros([1], dtype=tf.int32)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = model.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
pretrained_glimpse_vars = {
u'conv1/weights': glimpse_vars['conv1/weights:0'],
u'conv1/biases': glimpse_vars['conv1/biases:0'],
u'conv2/weights': glimpse_vars['conv2/weights:0'],
u'conv2/biases': glimpse_vars['conv2/biases:0'],
u'conv3/weights': glimpse_vars['conv3/weights:0'],
u'conv3/biases': glimpse_vars['conv3/biases:0'],
}
# pretrained_context_vars = {
# u'conv1/weights:': context_vars['conv1/weights:0'],
# u'conv1/biases:': context_vars['conv1/biases:0'],
# u'conv2/weights:': context_vars['conv2/weights:0'],
# u'conv2/biases:': context_vars['conv2/biases:0'],
# u'conv3/weights:': context_vars['conv3/weights:0'],
# u'conv3/biases:': context_vars['conv3/biases:0'],
# }
# print "="*50
# for var in tf.all_variables():
# print var.name, ":", var
pretrained_glimpse_saver = tf.train.Saver(pretrained_glimpse_vars)
#pretrained_context_saver = tf.train.Saver(pretrained_context_vars)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# # Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement)) as sess:
sess.run(init)
pretrained_ckpt = FLAGS.pretrained_checkpoint_path
pretrained_glimpse_saver.restore(sess, pretrained_ckpt)
#pretrained_context_saver.restore(sess, pretrained_ckpt)
coord = tf.train.Coordinator()
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
start=True))
sess.run(label_enqueue)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
graph_def=sess.graph_def)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / float(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)
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
end_epoch = False
if step > 0:
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
size = qr._queue.size().eval()
if size - FLAGS.batch_size < FLAGS.min_queue_size:
end_epoch = True
if end_epoch:
sess.run(label_enqueue)
coord.request_stop()
coord.join(threads)