这篇文章主要讲解了tensorflow pb to tflite精度下降的问题，内容清晰明了，对此有兴趣的小伙伴可以学习一下，相信大家阅读完之后会有帮助。

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之前希望在手机端使用深度模型做OCR，于是尝试在手机端部署tensorflow模型，用于图像分类。

思路主要是想使用tflite部署到安卓端，但是在使用tflite的时候发现模型的精度大幅度下降，已经不能支持业务需求了，最后就把OCR模型调用写在服务端了，但是精度下降的原因目前也没有找到，现在这里记录一下。

工作思路：

1.训练图像分类模型；2.模型固化成pb；3.由pb转成tflite文件；

但是使用python 的tf interpreter 调用tflite文件就已经出现精度下降的问题，android端部署也是一样。

1.网络结构

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
 
import tensorflow as tf
slim = tf.contrib.slim
 
def ttnet(images, num_classes=10, is_training=False,
   dropout_keep_prob=0.5,
   prediction_fn=slim.softmax,
   scope='TtNet'):
 end_points = {}
 
 with tf.variable_scope(scope, 'TtNet', [images, num_classes]):
 net = slim.conv2d(images, 32, [3, 3], scope='conv1')
 # net = slim.conv2d(images, 64, [3, 3], scope='conv1_2')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
 net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='bn1')
 # net = slim.conv2d(net, 128, [3, 3], scope='conv2_1')
 net = slim.conv2d(net, 64, [3, 3], scope='conv2')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
 net = slim.conv2d(net, 128, [3, 3], scope='conv3')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool3')
 net = slim.conv2d(net, 256, [3, 3], scope='conv4')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool4')
 net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='bn2')
 # net = slim.conv2d(net, 512, [3, 3], scope='conv5')
 # net = slim.max_pool2d(net, [2, 2], 2, scope='pool5')
 net = slim.flatten(net)
 end_points['Flatten'] = net
 
 # net = slim.fully_connected(net, 1024, scope='fc3')
 net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
      scope='dropout3')
 logits = slim.fully_connected(net, num_classes, activation_fn=None,
         scope='fc4') 
 end_points['Logits'] = logits
 end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
 
 return logits, end_points
ttnet.default_image_size = 28
 
def ttnet_arg_scope(weight_decay=0.0):
 with slim.arg_scope(
  [slim.conv2d, slim.fully_connected],
  weights_regularizer=slim.l2_regularizer(weight_decay),
  weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
  activation_fn=tf.nn.relu) as sc:
 return sc

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