Tensorboard 可视化好帮手 2

制作输入源

import tensorflow as tf
import numpy as np
 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

在 layer 中为 Weights, biases 设置变化图表

添加一个参数 n_layer,用来标识层数

用变量 layer_name 代表其每层的名称

tf.summary.histogram用来绘制图片, 第一个参数是图表的名称, 第二个参数是图表要记录的变量

def add_layer(inputs , 
              in_size, 
              out_size,n_layer, 
              activation_function=None):
    ## add one more layer and return the output of this layer
    layer_name='layer%s'%n_layer
    with tf.name_scope(layer_name):
         with tf.name_scope('weights'):
              Weights= tf.Variable(tf.random_normal([in_size, out_size]),name='W')

              tf.summary.histogram(layer_name + '/weights', Weights) # tensorflow >= 0.12

         with tf.name_scope('biases'):
              biases = tf.Variable(tf.zeros([1,out_size])+0.1, name='b')

              tf.summary.histogram(layer_name + '/biases', biases)  # Tensorflow >= 0.12

         with tf.name_scope('Wx_plus_b'):
              Wx_plus_b = tf.add(tf.matmul(inputs,Weights), biases)

         if activation_function is None:
            outputs=Wx_plus_b
         else:
            outputs= activation_function(Wx_plus_b)

         tf.summary.histogram(layer_name + '/outputs', outputs) # Tensorflow >= 0.12

    return outputs

l1= add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
# add output  layer
prediction= add_layer(l1, 10, 1, n_layer=2, activation_function=None)

设置loss的变化图

loss是在tesnorBorad 的scalars下面的, 因为使用tf.summary.scalar() 方法.

with tf.name_scope('loss'):
     loss= tf.reduce_mean(tf.reduce_sum(
              tf.square(ys- prediction), reduction_indices=[1]))
     # tf.scalar_summary('loss',loss) # tensorflow < 0.12
     tf.summary.scalar('loss', loss) # tensorflow >= 0.12

给所有训练图合并

tf.summary.merge_all()方法会对所有的 summaries 合并到一起

sess= tf.Session()

# merged= tf.merge_all_summaries()    # tensorflow < 0.12
merged = tf.summary.merge_all() # tensorflow >= 0.12

# writer = tf.train.SummaryWriter('logs/', sess.graph)    # tensorflow < 0.12
writer = tf.summary.FileWriter("logs/", sess.graph) # tensorflow >=0.12

sess.run(tf.global_variables_initializer())  # 替换成这样就好

训练数据

for i in range(1000):
   sess.run(train_step, feed_dict={xs:x_data, ys:y_data})
   if i%50 == 0:
      rs = sess.run(merged,feed_dict={xs:x_data,ys:y_data})
      writer.add_summary(rs, i)