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('data/convolutional.ckpt')
def regression(input): # 如果要防止time报错就要把下面的函数
return sess.run(y1, feed_dict={x: input}).flatten().tolist()
def convolutional(input): #一直报错,先取消掉
return sess.run(y2, feed_dict={x: input, keep_prob: 1.0}).flatten.tolist()
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")
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)
return jsonify(results=[output1, output2])
@app.route('/')
def main():
return render_template('index.html')
if __name__ == "__main__":
app.debug = True
app.run(host="127.0.0.1", port=5000)
添加回答
举报
0/150
提交
取消