基于TensorFlow 机器学习 识别手写数字.zip

# -*- coding: utf-8 -*- # __author__ = "zok" [email protected] # Date: 2019-10-16 Python: 3.7 import numpy as np import tensorflow as tf from flask import Flask, jsonify, render_template, request from mnist import model x = tf.placeholder("float", [None, 784]) sess = tf.Session() """ 拿出训练好的模型 """ with tf.variable_scope("regression"): # 线性模型 y1, variables = model.regression(x) saver = tf.train.Saver(variables) # saver.restore(sess, "mnist/data/regression.ckpt") regression_file = tf.train.latest_checkpoint("mnist/data/regression.ckpt") print(regression_file) if regression_file is not None: saver.restore(sess, regression_file) with tf.variable_scope("convolutional"): # 卷积模型 sess.run(tf.global_variables_initializer()) keep_prob = tf.placeholder("float") y2, variables = model.convolutional(x, keep_prob) sess.run(tf.global_variables_initializer()) saver = tf.train.Saver(variables) # saver.restore(sess, "mnist/data/convolutional.ckpt") convolutional_file = tf.train.latest_checkpoint("mnist/data/convolutional.ckpt") print(convolutional_file) if convolutional_file is not None: saver.restore(sess, convolutional_file) # 定义一个输入(线性、卷积) 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() # 定义app app = Flask(__name__) # 路由 @app.route('/api/mnist', methods=['post']) def mnist(): input = ((225 - np.array(request.json, dtype=np.uint8))/255.0).reshape(1, 784) output1 = regression(input) output2 = convolutional(input) return jsonify(results=[output1, output2]) @app.route("/") def main(): return render_template("index.html") if __name__ == "__main__": app.debug = True app.run(host='0.0.0.0', port=8000)