[TensorFlow学习手记] 5

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Code

import tensorflow as tf import matplotlib.pyplot as plt import numpy as np '''Add layer定义添加神经层的函数def add_layer(),它有四个参数：输入值、输入的大小、输出的大小和激励函数，我们设定默认的激励函数是None'''def add_layer(inputs, in_size, out_size, activation_function=None):    Weights = tf.Variable(tf.random_normal([in_size,out_size])) # Normal distribution 生成随机变量矩阵    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) # biases初始值不推荐为0    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 outputsx_data = np.linspace(-1,1,300)[:, np.newaxis]'''np.newaxis的功能是插入新维度，可以看出np.newaxis分别是在行或列上增加维度，原来是（10,）的数组，在行上增加维度变成（1,10）的二维数组，在列上增加维度变为(10,1)的二维数组看下面的例子：In [1]: np.linspace(1, 10, 10)Out[1]: array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])In [2]: np.linspace(1, 10, 10)[np.newaxis,:]Out[2]: array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]])In [3]: np.linspace(1, 10, 10)[:,np.newaxis]Out[3]:array([[ 1.],[ 2.],[ 3.],[ 4.],[ 5.],[ 6.],[ 7.],[ 8.],[ 9.],[ 10.]])In [4]: np.linspace(1, 10, 10).shapeOut[4]: (10,)In [5]: np.linspace(1, 10, 10)[np.newaxis,:].shapeOut[5]: (1, 10)In [6]: np.linspace(1, 10, 10)[:,np.newaxis].shapeOut[6]: (10, 1)'''noise = np.random.normal(0,0.05,x_data.shape) # 增加噪声点y_data = np.square(x_data) - 0.5 + noise # x^2 + 0.5 +noise# define placeholder for inputs to networkxs = tf.placeholder(tf.float32, [None,1])ys = tf.placeholder(tf.float32, [None,1])'''利用占位符定义我们所需的神经网络的输入。 tf.placeholder()就是代表占位符，这里的None代表无论输入有多少都可以，因为输入只有一个特征，所以这里是1。'''l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu) # 添加隐藏层predition = add_layer(l1, 10, 1, activation_function=None) # 添加输出层loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - predition), reduction_indices=[1]))'''tf.reduce_*(    input_tensor, 输入    axis=None, # 取哪一维度    keep_dims=False, # 保持维度    name=None,    reduction_indices=None # 兼容)'''train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)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}))

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Result

1.099570.009718250.007456280.006760080.006166130.005635080.005251190.0049690.004732650.004506780.004315450.004129440.003992980.003881780.003768110.003671370.003599630.003539080.003487950.00344684