tensorflow之非线性方程

[python]import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltdef add_layer(inputs, in_size, out_size, activation_function=None):  # activation_function=None线性函数    Weights = tf.Variable(tf.random_normal([in_size, out_size]))  # Weight中都是随机变量    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)  # biases推荐初始值不为0    Wx_plus_b = tf.matmul(inputs, Weights) + biases  # inputs*Weight+biases    if activation_function is None:  # 线性回归        outputs = Wx_plus_b    else:  # 逻辑回归        outputs = activation_function(Wx_plus_b)    return outputsdef get_next_batch(batch_size=64):    x = np.random.choice(np.linspace(start=0, stop=1, num=200), batch_size)  # 从0-1之间选取batch_size个数,免去对数据的归一化    x = np.reshape(x, newshape=[-1, 1])    # noise=np.random.normal(0, 0.05, x.shape)*50 # 噪声    y = np.power(x, 2)    return x, yx_input = tf.placeholder(tf.float32, [None, 1])y_input = tf.placeholder(tf.float32, [None, 1])def model():        layer_1 = add_layer(x_input, 1, 4, activation_function=tf.nn.relu)  # 隐藏层    layer_2 = add_layer(layer_1, 4, 4, activation_function=tf.nn.relu)  # 隐藏层    prediction = add_layer(layer_2, 4, 1, activation_function=None)  # 输出层    return predictionmodel_dir = 'model_non_linear'  # 模型存储目录def train():    prediction = model()    # 求标准差    loss = tf.reduce_mean(        tf.reduce_sum(tf.square(y_input - prediction), reduction_indices=[1]))  # square()平方,sum()求和,mean()平均值    optimizer = tf.train.GradientDescentOptimizer(0.005).minimize(loss)  # 0.05学习效率,minimize(loss)减小loss误差    sess = tf.Session()    saver = tf.train.Saver()    checkpoint = tf.train.latest_checkpoint(model_dir)    if checkpoint:        saver.restore(sess, tf.train.latest_checkpoint(model_dir))  # 从模型中读取参数    else:        sess.run(tf.global_variables_initializer())  # 变量初始化    # 训练20万次    for step in range(200000):        x_train, y_train = get_next_batch(batch_size=256)        loss_, optimizer_ = sess.run([loss, optimizer], feed_dict={x_input: x_train, y_input: y_train})        if step % 50 == 0:            print(step, loss_)    # 模型保存    saver.save(sess, '{}/non_linear_equation'.format(model_dir), global_step=step)def test():    predition = model()    sess = tf.Session()    saver = tf.train.Saver()    saver.restore(sess, tf.train.latest_checkpoint(model_dir))    x_test, y_test = get_next_batch(200)    y_pred = sess.run(predition, feed_dict={x_input: x_test})    # 数据可视化    fig = plt.figure()    ax = fig.add_subplot(2, 1, 1)    ax.scatter(x_test, y_test)    bx = fig.add_subplot(2, 1, 2)    bx.scatter(x_test, y_pred, c='r')    plt.xlabel('X')    plt.ylabel('Y')    plt.show()if __name__ == '__main__':    train()    # test()

训练20万次后得到下图