"""Run Eval once.

with tf.Session() as sess:

ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)

if ckpt and ckpt.model_checkpoint_path:

# Restores from checkpoint

saver.restore(sess, ckpt.model_checkpoint_path)

# Assuming model_checkpoint_path looks something like:

# /my-favorite-path/cifar10_train/model.ckpt-0,

# extract global_step from it.

global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]

else:

print('No checkpoint file found')

return

# Start the queue runners.

coord = tf.train.Coordinator()

try:

threads = []

for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):

threads.extend(qr.create_threads(sess, coord=coord, daemon=True,

start=True))

num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))

true_count = 0 # Counts the number of correct predictions.

total_sample_count = num_iter * FLAGS.batch_size

step = 0

output = [] # output用来保存最终预测的label

while step < num_iter and not coord.should_stop():

# ----------------------------------------------

predictions, predict_label_ = sess.run([top_k_op, predict_label]) # run，来输出label

# ----------------------------------------------

true_count += np.sum(predictions)

step += 1

print(predict_label_) # 输出来看看

# ----------------------------------------------

output.append(predict_label_) # 把每一个batch 预测的label append到output

# ----------------------------------------------

# ----------------------------------------------

save_path = r'./cifar10_train'

np.savetxt(os.path.join(save_path, "predict_label.txt"), output, fmt="%d", delimiter="\n") # 以列向量的形式保存，注意之前要import os

# ----------------------------------------------

# Compute precision @ 1.

precision = true_count / total_sample_count

print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))

summary = tf.Summary()

summary.ParseFromString(sess.run(summary_op))

summary.value.add(tag='Precision @ 1', simple_value=precision)

summary_writer.add_summary(summary, global_step)

except Exception as e: # pylint: disable=broad-except

coord.request_stop(e)

coord.request_stop()

"""Eval CIFAR-10 for a number of steps."""

with tf.Graph().as_default() as g:

# Get images and labels for CIFAR-10.

eval_data = FLAGS.eval_data == 'test'

images, labels = cifar10.inputs(eval_data=eval_data)

# Build a Graph that computes the logits predictions from the

# inference model.

logits = cifar10.inference(images)

# Calculate predictions.

top_k_op = tf.nn.in_top_k(logits, labels, 1)

# ----------------------------------------------

predict_label = tf.argmax(logits, axis=1) # 预测的label是logits中最大的那个值所在的下标

# ----------------------------------------------

# Restore the moving average version of the learned variables for eval.

variable_averages = tf.train.ExponentialMovingAverage(

cifar10.MOVING_AVERAGE_DECAY)

variables_to_restore = variable_averages.variables_to_restore()

saver = tf.train.Saver(variables_to_restore)

# Build the summary operation based on the TF collection of Summaries.

summary_op = tf.summary.merge_all()

summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)

while True:

# ----------------------------------------------

eval_once(saver, summary_writer, top_k_op, summary_op, predict_label) # 把predict_label作为参数传入

# ----------------------------------------------

if FLAGS.run_once:

break

#cifar10.maybe_download_and_extract()

if tf.gfile.Exists(FLAGS.eval_dir):

pass

else:

tf.gfile.MakeDirs(FLAGS.eval_dir)