main.py 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() with tf.variable_scope("regression"): y1, variables= model.regression(x) saver = tf.train.Saver(variables) saver.restore(sess,"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) module_file = tf.train.latest_checkpoint('pycharm/data/convolutional.ckpt') with tf.Session() as sess: sess.run(tf.global_variables_initializer()) if module_file is not None: saver.restore(sess, module_file) #saver.restore(sess,"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() app = Flask(__name__) @app.route('/api/mnist',methods=['post']) #可能出错和视频的路径不一样,所以改动为pycharm def mnist(): input= ((255 - 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)
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