tensorflow手动构建一个神经网络步骤

手动构建一个简单的神经网络步骤

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): 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, dtype=np.float32)[:, np.newaxis] noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32) y_data = np.square(x_data) - 0.5 + noise ##plt.scatter(x_data, y_data) ##plt.show() # 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 prediction 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 sess = tf.Session() # tf.initialize_all_variables() no long valid from # 2017-03-02 if using tensorflow >= 0.12 if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: init = tf.initialize_all_variables() else: init = tf.global_variables_initializer() 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}))

简单tensorflow构建及训练过程(以y=x^2+0.5为例):

定义神经网络层（输入数据，数据维度(x1,x2,x3,),输出维度，是否使用激活函数等）构造数据集添加占位符 （tensorflow程序在run之前都是在定义要执行的（op）操作，placeholder就是将要输入的数据提前写在那里了）构造神经网络，定义loss 添加隐藏层、输出层定义loss 定义训练方法 也就是定义的梯度下降的方法，这一步每次都会根据loss，更新权值初始化所有变量sess.run()进行训练

待更新…