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, "mnist/data/regression.ckpt")
module_file = tf.train.latest_checkpoint('mnist/data/regression.ckpt')
with tf.Session().as_default() as sess:
sess.run(tf.global_variables_initializer())
if module_file is not None:
saver.restore(sess, module_file)
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/convalutional.ckpt")
module_file = tf.train.latest_checkpoint('mnist/data/convolutional.ckpt')
with tf.Session().as_default() as sess:
sess.run(tf.global_variables_initializer())
if module_file is not None:
saver.restore(sess, module_file)
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'])
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=0000)
