利用tensorflow构造一个简单的神经网络

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1，定义一个“添加层”函数

import tensorflow as tfdef add_layer(inputs,in_size,out_size,activation_function=None):    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

2.添加一个隐藏层和输出层，在神经网络运行完成后输出每次训练完成后的偏差值---loss

import tensorflow as tfimport numpy as npdef 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 datax_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 networkxs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32, [None, 1])# add hidden layerl1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)# add output layerprediction = add_layer(l1, 10, 1, activation_function=None)# the error between prediciton and real dataloss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),                                    reduction_indices=[1]))train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)# important stepinit = tf.initialize_all_variables()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}))

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