我是tensorflow1.4版本的，原代码有些api不通过，我已做了替换。
代码：
from data_inputs import *
import numpy as np
import models
import time
def main():
low_res_holder = tf.placeholder(tf.float32, shape=[BATCH_SIZE, INPUT_SIZE, INPUT_SIZE, NUM_CHENNELS])
high_res_holder = tf.placeholder(tf.float32, shape=[BATCH_SIZE, LABEL_SIZE, LABEL_SIZE, NUM_CHENNELS])
inferences = models.create_model(MODEL_NAME, low_res_holder)
training_loss = models.loss(inferences, high_res_holder, name='training_loss', weights_decay=0)
validation_loss = models.loss(inferences, high_res_holder, name='validation_loss')
tf.summary.scalar('training_loss', training_loss)
tf.summary.scalar('validation_loss', validation_loss)
global_step = tf.Variable(0, trainable=False, name='global_step')
# learning_rate = tf.train.piecewise_constant(
# global_step,
# [2000, 5000, 8000, 12000, 16000],
# [0.0005, 0.0001, 0.00005, 0.00001, 0.000005, 0.000001]
# )
learning_rate = tf.train.inverse_time_decay(0.001, global_step, 10000, 2)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(training_loss, global_step=global_step)
low_res_batch, high_res_batch = batch_queue_for_training(TRAINING_DATA_PATH)
low_res_eval, high_res_eval = batch_queue_for_testing(VALIDATION_DATA_PATH)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Start the queue runners (make batches).
tf.train.start_queue_runners(sess=sess)
# the saver will restore all model's variables during training
saver = tf.train.Saver(tf.global_variables(), max_to_keep=MAX_CKPT_TO_KEEP)
# Merge all the summaries and write them out to TRAINING_DIR
merged_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(TRAINING_SUMMARY_PATH, sess.graph)
for step in range(1, NUM_TRAINING_STEPS+1):
start_time = time.time()
low_res_images, high_res_images = sess.run([low_res_batch, high_res_batch])
feed_dict = {low_res_holder: low_res_images, high_res_holder: high_res_images}
_, batch_loss = sess.run([train_step, training_loss], feed_dict=feed_dict)
duration = time.time() - start_time
assert not np.isnan(batch_loss), 'Model diverged with loss = NaN'
if step % 100 == 0: # show training status
num_examples_per_step = BATCH_SIZE
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = 'step %d, batch_loss = %.3f (%.1f examples/sec; %.3f sec/batch)'
print(format_str % (step, batch_loss, examples_per_sec, sec_per_batch))
if step % 1000 == 0: # run validation and show its result
low_res_images, high_res_images = sess.run([low_res_eval, high_res_eval])
feed_dict = {low_res_holder: low_res_images, high_res_holder: high_res_images}
batch_loss = sess.run(validation_loss, feed_dict=feed_dict)
print('step %d, validation loss = %.3f' % (step, batch_loss))
summary = sess.run(merged_summary, feed_dict=feed_dict)
summary_writer.add_summary(summary)
# Save the model checkpoint periodically.
if step % 10000 == 0 or (step + 1) == NUM_TRAINING_STEPS:
saver.save(sess, join(CHECKPOINTS_PATH, 'model.ckpt'), global_step=step)
print('Training Finished!')
if name == '__main__':
main()
------------------------------------------------------------------------
这是报错：
WARNING:tensorflow:case: An unordered dictionary of predicate/fn pairs was provided, but exclusive=False. The order of conditional tests is deterministic but not guaranteed.
WARNING:tensorflow:case: An unordered dictionary of predicate/fn pairs was provided, but exclusive=False. The order of conditional tests is deterministic but not guaranteed.
ERROR:tensorflow:Exception in QueueRunner: Attempting to use uninitialized value matching_filenames
