转载自 csdn

import tensorflow as tf
import numpy as npdef add_layer(inputs, in_size, out_size, n_layer, activation_function=None):  # activation_function=None线性函数layer_name = "layer%s" % n_layerwith tf.name_scope(layer_name):with tf.name_scope('weights'):Weights = tf.Variable(tf.random_normal([in_size, out_size]))  # Weight中都是随机变量tf.summary.histogram(layer_name + "/weights", Weights)  # 可视化观看变量with tf.name_scope('biases'):biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)  # biases推荐初始值不为0tf.summary.histogram(layer_name + "/biases", biases)  # 可视化观看变量with tf.name_scope('Wx_plus_b'):Wx_plus_b = tf.matmul(inputs, Weights) + biases  # inputs*Weight+biasestf.summary.histogram(layer_name + "/Wx_plus_b", Wx_plus_b)  # 可视化观看变量if activation_function is None:outputs = Wx_plus_belse:outputs = activation_function(Wx_plus_b)tf.summary.histogram(layer_name + "/outputs", outputs)  # 可视化观看变量return outputs# 创建数据x_data，y_datax_data = np.linspace(-1, 1, 300)[:, np.newaxis]  # [-1,1]区间，300个单位，np.newaxis增加维度(后面多一个1)
noise = np.random.normal(0, 0.05, x_data.shape)  # 噪点
y_data = np.square(x_data) - 0.5 + noisewith tf.name_scope('inputs'):  # 结构化xs = tf.placeholder(tf.float32, [None, 1], name='x_input')ys = tf.placeholder(tf.float32, [None, 1], name='y_input')# 三层神经，输入层（1个神经元），隐藏层（10神经元），输出层（1个神经元）
l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)  # 隐藏层
prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)  # 输出层# predition值与y_data差别
with tf.name_scope('loss'):loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))  # square()平方,sum()求和,mean()平均值tf.summary.scalar('loss', loss)  # 可视化观看常量
with tf.name_scope('train'):train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  # 0.1学习效率,minimize(loss)减小loss误差init = tf.initialize_all_variables()
sess = tf.Session()
# 合并到Summary中
merged = tf.summary.merge_all()
# 选定可视化存储目录
writer = tf.summary.FileWriter("Desktop/", sess.graph)
sess.run(init)  # 先执行init# 训练1k次
for i in range(1000):sess.run(train_step, feed_dict={xs: x_data, ys: y_data})if i % 50 == 0:result = sess.run(merged, feed_dict={xs: x_data, ys: y_data})  # merged也是需要run的writer.add_summary(result, i)  # result是summary类型的，需要放入writer中，i步数（x轴）

然后在terminal中：

tensorboard --logdir=/path/to/log-directory