flask发布手写字识别模型

TensorFlow 卷积

 2018/11/26   Share

模型模块

import tensorflow as tf


# 定义线性模型 Y = W * X + b
def regression(x):
    W = tf.Variable(tf.zeros([784, 10]), name='W')
    b = tf.Variable(tf.zeros([10]), name='b')
    y = tf.nn.softmax(tf.matmul(x, W) + b)

    return y, [W, b]


# 定义卷积模型
def convolutional(x, keep_prob):
    # 定义卷积层
    def conv2d(x, W):
        return tf.nn.conv2d(x, W, [1, 1, 1, 1], padding='SAME')

    # 定义池化层
    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

    def weight_variable(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def bias_variable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    x_image = tf.reshape(x, [-1, 28, 28, 1])
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    # full connection
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # full connection
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    return y, [W_conv1, b_conv1, W_conv2, b_conv2, W_fc1, b_fc1, W_fc2, b_fc2]

模型驱动

import numpy as np
import tensorflow as tf
from flask import Flask,jsonify,render_template,request
import json
from mnist import model
x = tf.placeholder('float', [None, 784])
sess = tf.Session()

with tf.variable_scope('regression'):
    y1, variables = model.regression(x)
saver = tf.train.Saver(variables)
saver.restore(sess,"mnist/data/regression.ckpt")

with tf.variable_scope("convolutional"):
    keep_prob = tf.placeholder('float')
    y2, variables = model.convolutional(x,keep_prob)
saver = tf.train.Saver(variables)
saver.restore(sess, "mnist/data/convolutional.ckpt")

def regression(input):
    return sess.run(y1, feed_dict={x:input}).flatten().tolist()
def convolutional(input):
    return sess.run(y2, feed_dict={x:input,keep_prob:1.0}).flatten().tolist()

框架驱动



app = Flask(__name__)
@app.route('/api/mnist', methods=['post'])
def mnist():
    input = ((255 - np.array(request.json, dtype=np.uint8)) / 255.0).reshape(1,784)
    output1 = regression(input)
    output2 =  convolutional(input)

    output = {}
    output["output1"] = output1
    output["output2"] = output2
    res = []
    res.append(output)
    a = {}
    a['site'] = res
    mydata = json.dumps(a, ensure_ascii=False).encode("utf8")
    return mydata
@app.route('/')
def main():
    return render_template('index.html')

if __name__ == "__main__":
    app.debug = True
    app.run(port=9000)

静态文件

https://github.com/byerHu/mnist_web

Next Post

TensorFlow结合mnist进行卷积模型训练
Previous Post

TensorFlow结合mnist进行线性模型训练

CATALOG

1. 模型模块
2. 模型驱动
3. 框架驱动
4. 静态文件
1. 4.1. https://github.com/byerHu/mnist_web



缺失模块。
1、请确保node版本大于6.2
2、在博客根目录（注意不是archer根目录）执行以下命令：
npm i hexo-generator-json-content --save
3、在根目录_config.yml里添加配置：

jsonContent:
  meta: false
  pages: false
  posts:
    title: true
    date: true
    path: true
    text: false
    raw: false
    content: false
    slug: false
    updated: false
    comments: false
    link: false
    permalink: false
    excerpt: false
    categories: true
    tags: true