image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
channel_mean = tf.constant(np.array([123.68,116.779,103.938], dtype=np.float32))
image_before_process = tf.add(image,channel_mean)

tensor.shape
#returns tensor's static shape, while the graph is being built.

tensor.shape.as_list() 
#returns the static shape as a integer list.

tensor.shape[i].value 
#returns the static shape's i-th dimension size as an integer.

tf.shape(t) 
#returns t's run-time shape as a tensor.

#An example:
x = tf.placeholder(tf.float32, shape=[None, 8]) # x shape is non-deterministic while building the graph.
print(x.shape) # Outputs static shape (?, 8).
shape_t = tf.shape(x)
with tf.Session() as sess:
    print(sess.run(shape_t, feed_dict={x: np.random.random(size=[4, 8])})) # Outputs run-time shape (4, 8).

from tensorflow.python.client import device_lib as _device_lib
print _device_lib.list_local_devices()

import os
from tensorflow.python import pywrap_tensorflow

checkpoint_path = os.path.join(model_dir, "model.ckpt")
# Read data from checkpoint file
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map = reader.get_variable_to_shape_map()
# Print tensor name and values
for key in var_to_shape_map:
    print("tensor_name: ", key)
    print(reader.get_tensor(key))

import tensorflow as tf
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
ckpt = tf.train.get_checkpoint_state('./model_dir/')                          # 通过检查点文件锁定最新的模型
saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path +'.meta')   # 载入图结构，保存在.meta文件中
var = [v for v in tf.trainable_variables()]

with tf.Session() as sess:
    saver.restore(sess,ckpt.model_checkpoint_path)
    logging.info("load parameter done")
    parameter = []
    for i in range(len(var)):
        parameter.append(sess.run(var[i]))
        logging.info(var[i].name)

import tensorflow as tf
import cv2

image = cv2.imread('test.jpg')   #BGR order
image_string = tf.read_file('test.jpg')                                                                                                                   
image_decoded = tf.image.decode_image(image_string)
with tf.Session() as sess:                                                                                                                                                        
    image_d = sess.run(image_decoded)      #RGB order