main.pyimport numpy as npimport tensorflow as tffrom flask import Flask,jsonify,render_template,requestfrom mnist import modelx= 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pycharmdef 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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