本篇文章主要介绍了tensorflow建立一个简单的神经网络的方法，小编觉得挺不错的，现在分享给大家，也给大家做个参考。一起跟随小编过来看看吧
本笔记目的是通过tensorflow实现一个两层的神经网络。目的是实现一个二次函数的拟合。
如何添加一层网络
代码如下：
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
注意该函数中是xW+b，而不是Wx+b。所以要注意乘法的顺序。x应该定义为[类别数量， 数据数量]， W定义为[数据类别，类别数量]。
创建一些数据
# 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
numpy的linspace函数能够产生等差数列。start,stop决定等差数列的起止值。endpoint参数指定包不包括终点值。
numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)[source] Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop].
noise函数为添加噪声所用，这样二次函数的点不会与二次函数曲线完全重合。
numpy的newaxis可以新增一个维度而不需要重新创建相应的shape在赋值，非常方便，如上面的例子中就将x_data从一维变成了二维。
添加占位符，用作输入
# 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)
计算误差，并用梯度下降使得误差最小
# the error between prediciton 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)
完整代码如下：
from __future__ import print_function import tensorflow as tf import numpy as np import matplotlib.pyplot as plt 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 prediciton 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 init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) # plot the real data fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.scatter(x_data, y_data) plt.ion() plt.show() for i in range(1000): # training sess.run(train_step, Feed_dict={xs: x_data, ys: y_data}) if i % 50 == 0: # to visualize the result and improvement try: ax.lines.remove(lines[0]) except Exception: pass prediction_value = sess.run(prediction, Feed_dict={xs: x_data}) # plot the prediction lines = ax.plot(x_data, prediction_value, 'r-', lw=5) plt.pause(0.1)
运行结果：
