cifar10_eval_one.py

from datetime import datetime
import math
import time

import numpy as np
import tensorflow as tf
from tensorflow.models.image.cifar10 import cifar10

FLAGS = tf.app.flags.FLAGS
#cifar10.IMAGE_SIZE = 32
tf.app.flags.DEFINE_string('checkpoint_dir', 'D:/tensorflow/data/cifar10_train',
                           """Directory where to read model checkpoints.""")
tf.app.flags.DEFINE_integer('num_examples', 10000,
                            """Number of examples to run.""")

def evaluate_images(images):
  logits = cifar10.inference(images)

  variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
  variables_to_restore = variable_averages.variables_to_restore()
  saver = tf.train.Saver(variables_to_restore)

  load_trained_model(saver, logits)

def load_trained_model(saver, logits):
  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)
    else:
      print('No checkpoint file found')
      return

    predict = sess.run(logits)
    print(predict, '\n')

def img_read(filename):
  if not tf.gfile.Exists(filename):
    tf.logging.fatal('File does not exists %s', filename)
  image_data = tf.gfile.FastGFile(filename, 'rb').read()
  image = tf.image.decode_jpeg(image_data)
  image = tf.image.resize_images(image, [24, 24])
  return image

filename = '1.jpg'
image = img_read(filename)
evaluate_images([image])