[Tensorflow]激励函数tf.nn.relu样例

代码：

import tensorflow as tf
import numpy as np
### 定义添加神经网络层函数 START ###

def add_layer(inputs,in_size,out_size,activation_function=None):
    """描述：  添加神经网络层函数.
    :param inputs:  输入神经层
    :param in_size: 输入神经层的神经元个数
    :param out_size:    输出神经层的神经元个数
    :param activation_function: 激励函数
    """

    # 定义一个"in_size行，out_size列"的随机矩阵变量
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))

    # 定义一个"1行，out_size列"的0值矩阵基准变量
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)

    # 定义一个矩阵乘法函数公式
    Wx_plus_b = tf.matmul(inputs,Weights)+biases

    # 判断是否使用激励函数
    if activation_function is None:
        outputs=Wx_plus_b
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs
### 定义添加神经网络层函数 END ###

### 定义变量结构 START###

# 定义起始输入：在指定的-1到1的间隔内返回300个均匀间隔的1行300列的数组，再将数组转化为1列300行的矩阵
#   例如：
#   x1 = np.array([1, 2, 3, 4, 5])
#   #   the shape of x1 is (5,)
#   x1_new = x1[:, np.newaxis]
#   #   now, the shape of x1_new is (5, 1)
#   array([[1],
#          [2],
#          [3],
#          [4],
#          [5]])
#   x1_new = x1[np.newaxis,:]
#   #   now, the shape of x1_new is (1, 5)
#   array([[1, 2, 3, 4, 5]])

x_data=np.linspace(-1,1,300)[:,np.newaxis]

# 定义噪点 ：使用高斯分布的概率密度函数定义一个均值为0，标准差为0.05的高斯随机数，个数为x_data的矩阵元素数
noise =np.random.normal(0,0.05,x_data.shape)

# 定义起始输出：x_data的平方减去0.5，再加上噪点
y_data=np.square(x_data)-0.5+noise

# 定义运行时参数变量
xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])

### 定义神经网络结构    START###

# 定义隐藏层神经网络层layer01
layer01=add_layer(xs,1,10,activation_function=tf.nn.relu)
# 定义隐藏层神经网络层layer02
layer02=add_layer(layer01,10,10,activation_function=tf.nn.sigmoid)
# 定义预测输出层 prediction
prediction =add_layer(layer02,10,1,activation_function=None)
# 计算损失
# 1.计算起始输出与预测输出的偏差的平方
loss_square=tf.square(y_data - prediction)
# 2.计算一个张量的各个维度上元素的总和.
reduce_sum_square=tf.reduce_sum(loss_square,reduction_indices=[1])
# 3.计算损失：张量的各个维度上的元素的平均值
loss=tf.reduce_mean(reduce_sum_square)

#使用梯度下降算法训练所有样本
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# 定义初始化变量
init=tf.initialize_all_variables()
# 创建会话
sess=tf.Session()
# 运行初始化变量指针
sess.run(init)

### 定义神经网络结构    END###

###定义变量结构  END###

for i in range(2000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i%50==0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

输出结果：

> Executing task: python d:\Work\002_WorkSpace\VSCode\Tensorflow\cnn.py <

WARNING:tensorflow:From C:\Program Files\Python\Python37\lib\site-packages\tensorflow\python\framework
\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From C:\Program Files\Python\Python37\lib\site-packages\tensorflow\python\ops
\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
WARNING:tensorflow:From C:\Program Files\Python\Python37\lib\site-packages\tensorflow\python\util
\tf_should_use.py:193: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
2019-06-16 18:23:25.445771: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
 instructions that this TensorFlow binary was not compiled to use: AVX2
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