3.19

铜期货价格的预测代码

今日完成LSTM模型的搭建和运行

代码部分分为两部分：模型的训练和预测

训练模型部分代码：

def train_lstm(batch_size=5,time_step=2,train_begin=0,train_end=150):
    X=tf.placeholder(tf.float32, shape=[None,time_step,input_size])
    Y=tf.placeholder(tf.float32, shape=[None,time_step,output_size])
    batch_index,train_x,train_y=get_train_data(batch_size,time_step,train_begin,train_end)
    pred,_=lstm(X)
    #损失函数
    loss=tf.reduce_mean(tf.square(tf.reshape(pred,[-1])-tf.reshape(Y, [-1])))
    train_op=tf.train.AdamOptimizer(lr).minimize(loss)
    saver=tf.train.Saver(tf.global_variables(),max_to_keep=15)
    module_file = tf.train.latest_checkpoint('./')
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        #saver.restore(sess, module_file)
        #重复训练10000次
        for i in range(150):
            for step in range(len(batch_index)-1):
                _,loss_=sess.run([train_op,loss],feed_dict={X:train_x[batch_index[step]:batch_index[step+1]],Y:train_y[batch_index[step]:batch_index[step+1]]})
            print(i,loss_)
            if i % 15==0:
                print("保存模型：",saver.save(sess,'future.model',global_step=i))

　　预测部分代码：

def prediction(time_step=2):
    X=tf.placeholder(tf.float32, shape=[None,time_step,input_size])
    #Y=tf.placeholder(tf.float32, shape=[None,time_step,output_size])
    mean,std,test_x,test_y=get_test_data(time_step)
    pred,_=lstm(X)
    saver=tf.train.Saver(tf.global_variables())
    with tf.Session() as sess:
        #参数恢复
        module_file = tf.train.latest_checkpoint('./')
        saver.restore(sess, module_file)
        test_predict=[]
        for step in range(len(test_x)-1):
          prob=sess.run(pred,feed_dict={X:[test_x[step]]})
          predict=prob.reshape((-1))
          test_predict.extend(predict)
        test_y=np.array(test_y)*std[7]+mean[7]
        test_predict=np.array(test_predict)*std[7]+mean[7]
        acc=np.average(np.abs(test_predict-test_y[:len(test_predict)])/test_y[:len(test_predict)])  #偏差
        #以折线图表示结果
        plt.figure()
        plt.plot(list(range(len(test_predict))), test_predict, color='b')
        plt.plot(list(range(len(test_y))), test_y,  color='r')
        plt.show()
        print(acc)

　　结果：