将一个训练好的模型分别保存为.ckpt和.pb格式，然后利用这两种保存的模型分别进行预测，得到的结果不同？而且对于同一张图片，每次预测的结果也都稍有区别，请教下这是为什么？

# coding: utf-8

import tensorflow as tf
from tensorflow.python.framework import graph_util
import os
import numpy as np
import cv2

os.environ["CUDA_VISIBLE_DEVICES"] = "0"


def restore_pb(img_data, pb_file_path=None):
    '''restore model from .pb to infer
    '''
    if not pb_file_path:
        pb_file_path = "test/inceptionV3_0.pb"
    with tf.Graph().as_default():
        graph_def = tf.GraphDef()
        with tf.gfile.FastGFile(pb_file_path, "rb") as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            return_elems = ['tower_0/InceptionV3/predictions/Softmax:0']
            predictions = tf.import_graph_def(graph_def, return_elements=return_elems)

        with tf.Session() as sess:
            # print(tf.contrib.graph_editor.get_tensors(tf.get_default_graph()))
            # for node in sess.graph.node:
            #     print(node.name)
            sess.run(tf.global_variables_initializer())
            image = sess.graph.get_tensor_by_name('import/x:0')
            #label = sess.graph.get_tensor_by_name('y:0')

            predictions = sess.run(predictions, feed_dict={image: img_data})
            print('pb predictions:', predictions[0])

def restore_ckpt(img_data):
    '''restore model from .ckpt to infer
    '''
    cpkt_meta = 'test/inception_mom1to2_rand5.ckpt-0.meta'
    ckpt = 'test/inception_mom1to2_rand5.ckpt-0'
    with tf.Graph().as_default():
        graph = tf.Graph()
        config = tf.ConfigProto(allow_soft_placement=True)
        with tf.Session(graph=graph, config=config) as sess:
            saver = tf.train.import_meta_graph(cpkt_meta)
            saver.restore(sess, ckpt)
            # print(tf.contrib.graph_editor.get_tensors(tf.get_default_graph()))
            image = graph.get_tensor_by_name('x:0')
            preds = graph.get_tensor_by_name('tower_0/InceptionV3/predictions/Softmax:0')
            predictions = sess.run(preds, feed_dict={image: img_data})
            print('ckpt predictions:', predictions)

def ckpt2pb():
    '''convert .ckpt to .pb file
    '''
    # some path
    output_nodes = ['tower_0/InceptionV3/predictions/Softmax']
    cpkt_meta = 'test/inception_mom1to2_rand5.ckpt-0.meta'
    ckpt = 'test/inception_mom1to2_rand5.ckpt-0'
    pb_file_path = 'test/inceptionV3_ckpt2pb.pb'

    with tf.Graph().as_default():
        graph = tf.Graph()
        config = tf.ConfigProto(allow_soft_placement=True)

        with tf.Session(graph=graph, config=config) as sess:
            # restored model from ckpt
            saver = tf.train.import_meta_graph(cpkt_meta)
            saver.restore(sess, ckpt)

            # save freeze graph into .pb file
            graph_def = tf.get_default_graph().as_graph_def()
            constant_graph = graph_util.convert_variables_to_constants(sess, graph_def, output_nodes)
            with tf.gfile.FastGFile(pb_file_path, mode='wb') as f:
                f.write(constant_graph.SerializeToString())

分别对一张图片进行预测：

img_data = cv2.imread('tumor_009_19327_175114_0.jpeg')
img_data = cv2.cvtColor(img_data, cv2.COLOR_BGR2RGB)
img_data = (np.array(img_data).astype(np.float32)) / 256.0
img_data = np.reshape(img_data, [-1, 256, 256, 3])

restore_pb(img_data)
restore_ckpt(img_data)
ckpt2pb()
restore_pb(img_data, pb_file_path='test/inceptionV3_ckpt2pb.pb')

结果如下：

2019-05-10 16:56:42.793083: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-05-10 16:56:44.553334: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: Tesla P40 major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:04:00.0
totalMemory: 22.38GiB freeMemory: 22.21GiB
2019-05-10 16:56:44.553413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2019-05-10 16:56:44.804127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-05-10 16:56:44.804196: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2019-05-10 16:56:44.804204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2019-05-10 16:56:44.804687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 21557 MB memory) -> physical GPU (device: 0, name: Tesla P40, pcibus id: 0000:04:00.0, compute capability: 6.1)
pb predictions: [[0.31244308 0.6875569 ]]
2019-05-10 16:56:47.791268: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2019-05-10 16:56:47.791313: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-05-10 16:56:47.791321: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2019-05-10 16:56:47.791326: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2019-05-10 16:56:47.791480: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 21557 MB memory) -> physical GPU (device: 0, name: Tesla P40, pcibus id: 0000:04:00.0, compute capability: 6.1)
ckpt predictions: [[0.27373236 0.7262676 ]]
2019-05-10 16:56:52.778329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2019-05-10 16:56:52.778398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-05-10 16:56:52.778406: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2019-05-10 16:56:52.778414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2019-05-10 16:56:52.778624: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 21557 MB memory) -> physical GPU (device: 0, name: Tesla P40, pcibus id: 0000:04:00.0, compute capability: 6.1)
Converted 190 variables to const ops.
2019-05-10 16:56:58.946692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2019-05-10 16:56:58.946815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-05-10 16:56:58.946827: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2019-05-10 16:56:58.946834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2019-05-10 16:56:58.947005: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 21557 MB memory) -> physical GPU (device: 0, name: Tesla P40, pcibus id: 0000:04:00.0, compute capability: 6.1)
pb predictions: [[0.28257495 0.71742505]]

只看预测结果，也就是：

pb predictions: [[0.31244308 0.6875569 ]] # this model gerated from last running

ckpt predictions: [[0.27373236 0.7262676 ]]

pb predictions: [[0.28257495 0.71742505]]

可以看到，上面得到的3个结果都是略有区别的，而且每次运行都略有不同。请问这是为什么？