环境：python 3 , tensorflow 1.1

#coding:utf-8 """ python 3 tensorflow 1.1 """ import tensorflow as tf import numpy as np #设置随机种子 tf.set_random_seed(100) np.random.seed(100) #数据 x = np.linspace(-1,1,100)[:,np.newaxis] noise = np.random.normal(0,0.1,x.shape) y = np.power(x,3) + noise #输入可视化(tf.name_scope()) with tf.variable_scope('inputs'): xs = tf.placeholder(tf.float32,x.shape,name='x') ys = tf.placeholder(tf.float32,y.shape,name='y') #神经网络可视化 with tf.variable_scope('neural_network'): l1 = tf.layers.dense(xs,10,tf.nn.relu,name='hidden_layer') output = tf.layers.dense(l1,1,name='output_layer') #变量值统计 tf.summary.histogram('layer1',l1) tf.summary.histogram('output',output) #计算误差scope = 'loss' loss = tf.losses.mean_squared_error(y,output,scope='loss') #梯度下降 train = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss) #loss的统计用tf.summary.scalar() tf.summary.scalar('loss',loss) with tf.Session() as sess: init = tf.global_variables_initializer() sess.run(init) #tf.summary.merge_all()将所有统计信息合并(tf.summary.histogram,tf.summary_scalar) merged = tf.summary.merge_all() #tf.summary.FileWriter()将所有信息写入文件 writer = tf.summary.FileWriter('./tensorflow1.1_logs',sess.graph) for step in range(100): #merged也要训练 _,result = sess.run([train,merged],feed_dict={xs:x,ys:y}) writer.add_summary(result,step)

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