Tensorflow进行简单的神经网络的尝试（建造神经网络）

Bertha 。 2022-06-06 12:25 215阅读 0赞

# 通过对一个简单的函数的轨迹的预测来进行一个较为简单的神经网络的构建 #

#导入程序所需要的包
    import tensorflow as tf
    import numpy as np
    
    #该函数用来进行添加神经网络的层数
    def add_layer(inputs, in_size, out_size, activation_function=None):
        # add one more layer and return the output of this layer
        Weights = tf.Variable(tf.random_normal([in_size, out_size]))
        biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
        Wx_plus_b = tf.matmul(inputs, Weights) + biases
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs
    
    # Make up some real data
    x_data = np.linspace(-1,1,300)[:, np.newaxis]
    noise = np.random.normal(0, 0.05, x_data.shape)
    y_data = np.square(x_data) - 0.5 + noise
    
    # define placeholder for inputs to network
    xs = tf.placeholder(tf.float32, [None, 1])
    ys = tf.placeholder(tf.float32, [None, 1])
    # add hidden layer
    l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
    # add output layer
    prediction = add_layer(l1, 10, 1, activation_function=None)
    
    # the error between prediction and real data
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                         reduction_indices=[1]))
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    
    # important step
    # tf.initialize_all_variables() no long valid from
    # 2017-03-02 if using tensorflow >= 0.12
    if int((tf.__version__).split('.')[1]) < 12:
        init = tf.initialize_all_variables()
    else:
        init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init)
    
    for i in range(1000):
        # training
        sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
        if i % 50 == 0:
            # to see the step improvement
            print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))

运行的结果如下：  
![这里写图片描述][SouthEast]

主要的注意事项：了解tensorflow的Session会话，懂得各个神经网络层间的数据关系

[SouthEast]: /images/20220606/cd0df443e53742908aa7c198dbfbe210.png