（tensorflow）使用循环神经网络模型预测正弦函数

约定不等于承诺〃 2022-05-14 11:17 236阅读 0赞

一：代码

# -*- coding: utf-8 -*-
    import numpy as np
    import tensorflow as tf
    import matplotlib as mpl
    mpl.use('Agg') #设置只保存绘制图片，不以窗口形式显示
    from matplotlib import pyplot as plt
    
    from tensorflow.contrib import rnn
    
    HIDDEN_SIZE = 30
    NUM_LAYERS = 2
    TIME_STEPS = 10 #循环神经网络的截断长度
    LEARNING_RATE = 1.0
    BATCH_SIZE = 32
    TRAIN_STEPS = 10000
    
    TRAINING_EXAMPLES = 10000
    TESTING_EXAMPLES = 1000
    SAMPLE_GAP = 0.01      #采样间隔
    
    learn = tf.contrib.learn
    
    def generate_data(seq):
        X = []
        y = []
        #序列的第i项和后面的TIME_STEPS-1项合在一起作为输入；第i+TIME_STEPS项作为输出。
        #即用sin函数前面的TIME_STEPS个点信息，预测第i+TIME_STEPS个点的函数值
        for i in range(len(seq) - TIME_STEPS - 1):
            X.append([seq[i:i+TIME_STEPS]])
            y.append([seq[i+TIME_STEPS]])
        return np.array(X, dtype=np.float32), np.array(y, dtype=np.float32)
    
    
    def lstm_model(X, y):
        stacked_rnn = []
        #使用多层LSTMCell结构
        for iiLyr in range(3):
            stacked_rnn.append(tf.nn.rnn_cell.LSTMCell(num_units=HIDDEN_SIZE, state_is_tuple=True))
    
        cell = tf.nn.rnn_cell.MultiRNNCell(cells=stacked_rnn, state_is_tuple=True)
        x_ = tf.unstack(X, axis=1)
        # 将多层LSTMCell结构连接成RNN网络并计算前向传播结果
        output, _ = rnn.static_rnn(cell, x_, dtype=tf.float32)
        #这里只关注最后一个时刻的输出结果，该结果为下一时刻的预测值
        output = output[-1]
    
        prediction, loss = learn.models.linear_regression(output, y)
        train_op = tf.contrib.layers.optimize_loss(
            loss,
            tf.contrib.framework.get_global_step(),
            optimizer='Adagrad', learning_rate=0.1
        )
    
        return prediction, loss, train_op
    
    regressor = learn.Estimator(model_fn=lstm_model)
    
    test_start = TRAINING_EXAMPLES * SAMPLE_GAP
    test_end = (TRAINING_EXAMPLES + TESTING_EXAMPLES) * SAMPLE_GAP
    
    #用正弦函数生成训练，预测数据集合
    train_x, train_y = generate_data(np.sin(np.linspace(0, test_start, TRAINING_EXAMPLES, dtype=np.float32)))
    test_x, test_y = generate_data(np.sin(np.linspace(test_start, test_end, TESTING_EXAMPLES, dtype=np.float32)))
    
    regressor.fit(train_x, train_y, batch_size=BATCH_SIZE, steps=TRAIN_STEPS)
    
    predicted = [[pred] for pred in regressor.predict(test_x)]
    
    #使用rmse作为预测指标
    rmse = np.sqrt(((predicted - test_y) ** 2).mean(axis=0))
    print('mean square error is: %f' % rmse[0])
    
    #对预测的sin函数曲线进行绘图
    fig = plt.figure()
    plot_predicted = plt.plot(predicted, label='predicted')
    plot_test = plt.plot(test_y, label='real_sin')
    plt.legend()
    fig.savefig('sin.png')

二：结果

mean square error is: 0.002049

![这里写图片描述][70]

图中可以看出，预测的sin曲线与正弦曲线基本重合

[70]: /images/20220514/a4caa82339e645ad9190e26f98f76ae7.png