打开Python shell，键入：
import tensorflow as tf
import numpy as np

#输入数据
x_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

#输入层
with tf.name_scope(‘input_layer’): #输入层。将这两个变量放到input_layer作用域下，tensorboard会把他们放在一个图形里面
xs = tf.placeholder(tf.float32, [None, 1], name = ‘x_input’) # xs起名x_input，会在图形上显示
ys = tf.placeholder(tf.float32, [None, 1], name = ‘y_input’) # ys起名y_input，会在图形上显示

#隐层
with tf.name_scope(‘hidden_layer’): #隐层。将隐层权重、偏置、净输入放在一起
with tf.name_scope(‘weight’): #权重
W1 = tf.Variable(tf.random_normal([1,10]))
tf.summary.histogram(‘hidden_layer/weight’, W1)
with tf.name_scope(‘bias’): #偏置
b1 = tf.Variable(tf.zeros([1,10])+0.1)
tf.summary.histogram(‘hidden_layer/bias’, b1)
with tf.name_scope(‘Wx_plus_b’): #净输入
Wx_plus_b1 = tf.matmul(xs,W1) + b1
tf.summary.histogram(‘hidden_layer/Wx_plus_b’,Wx_plus_b1)
output1 = tf.nn.relu(Wx_plus_b1)

#输出层
with tf.name_scope(‘output_layer’): #输出层。将输出层权重、偏置、净输入放在一起
with tf.name_scope(‘weight’): #权重
W2 = tf.Variable(tf.random_normal([10,1]))
tf.summary.histogram(‘output_layer/weight’, W2)
with tf.name_scope(‘bias’): #偏置
b2 = tf.Variable(tf.zeros([1,1])+0.1)
tf.summary.histogram(‘output_layer/bias’, b2)
with tf.name_scope(‘Wx_plus_b’): #净输入
Wx_plus_b2 = tf.matmul(output1,W2) + b2
tf.summary.histogram(‘output_layer/Wx_plus_b’,Wx_plus_b2)
output2 = Wx_plus_b2

#损失
with tf.name_scope(‘loss’): #损失
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-output2),reduction_indices=[1]))
tf.summary.scalar(‘loss’,loss)
with tf.name_scope(‘train’): #训练过程
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

#初始化
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
merged = tf.summary.merge_all() #将图形、训练过程等数据合并在一起
writer = tf.summary.FileWriter(‘logs’,sess.graph) #将训练日志写入到logs文件夹下

#训练
for i in range(1000):
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
if(i%50==0): #每50次写一次日志
result = sess.run(merged,feed_dict={xs:x_data,ys:y_data}) #计算需要写入的日志数据
writer.add_summary(result,i) #将日志数据写入文件

执行上述代码，会在“当前路径/logs”目录下生成一个events.out.tfevents.{time}.{machine-name}的文件。在当前目录新建“查看训练过程.bat”，里面输入。
tensorboard --logdir=logs

执行上述bat文件，打开浏览器，输入地址：http://localhost:6006，就可以查看训练过程中的各种图形。
现在前面想要画的图像的参数前加上加上:
如果要查看维度，可以：
tf.summary.distribution
分布图，一般用于显示weights分布
如果要查看inout
可以：
tf.summary.scalar
用来显示标量信息，其格式为：
例如：tf.summary.scalar(‘mean’, mean)
一般在画loss,accuary时会用到这个函数。
with tf.variable_scope(‘local3’) as scope: # Move everything into depth so we can perform a single matrix multiply. reshape = tf.reshape(pool2, [FLAGS.batch_size, -1]) dim = reshape.get_shape()[1].value weights = _variable_with_weight_decay(‘weights’, shape=[dim, 384], stddev=0.04, wd=0.004) biases = _variable_on_cpu(‘biases’, [384], tf.constant_initializer(0.1)) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) _activation_summary(local3)

local4 with tf.variable_scope(‘local4’) as scope: weights = _variable_with_weight_decay(‘weights’, shape=[384, 192], stddev=0.04, wd=0.004) biases = _variable_on_cpu(‘biases’, [192], tf.constant_initializer(0.1)) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name) _activation_summary(local4)

linear layer(WX + b), # We don’t apply softmax here because # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits # and performs the softmax internally for efficiency. with tf.variable_scope(‘softmax_linear’) as scope: weights = _variable_with_weight_decay(‘weights’, [192, NUM_CLASSES], stddev=1/192.0, wd=0.0) biases = _variable_on_cpu(‘biases’, [NUM_CLASSES], tf.constant_initializer(0.0)) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name) _activation_summary(softmax_linear)

return softmax_linear

tf.summary.scalar(tags, values, collections=None, name=None