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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