tensorflow 实现神经网络

时间：2022-09-16 11:29:48
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mport tensorflow as tf

import numpy as np

# 添加层

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

# 1.训练的数据

# 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

# 2.定义节点准备接收数据

# define placeholder for inputs to network

xs = tf.placeholder(tf.float32, [None, 1])

ys = tf.placeholder(tf.float32, [None, 1])

# 3.定义神经层：隐藏层和预测层

# add hidden layer 输入值是 xs，在隐藏层有 10 个神经元

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

# add output layer 输入值是隐藏层 l1，在预测层输出 1 个结果

prediction = add_layer(l1, 10, 1, activation_function=None)

# 4.定义 loss 表达式

# the error between prediciton and real data

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),

                     reduction_indices=[1]))

# 5.选择 optimizer 使 loss 达到最小

# 这一行定义了用什么方式去减少 loss，学习率是 0.1

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

# important step 对所有变量进行初始化

init = tf.initialize_all_variables()

sess = tf.Session()

# 上面定义的都没有运算，直到 sess.run 才会开始运算

sess.run(init)

# 迭代 1000 次学习，sess.run optimizer

for i in range(1000):

    # training train_step 和 loss 都是由 placeholder 定义的运算，所以这里要用 feed 传入参数

    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}))

tensorflow 实现神经网络

参考：http://www.jianshu.com/p/e112012a4b2d

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