Tensorflow学习--tensorboard

一、在 layer 中为 Weights, biases 设置变化图表
with tf.name_scope(‘xxxx’)
tf.summary.histogram(layer_name + ‘/xxx’,xxxx)

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)          with tf.name_scope('biases'):              biases = tf.Variable(tf.zeros([1,out_size])+0.1, name='b')                         tf.summary.histogram(layer_name + '/biases', biases)          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)     return outputs

二、设置loss的变化图
使用tf.scalar_summary()

with tf.name_scope('loss'):     loss= tf.reduce_mean(tf.reduce_sum(              tf.square(ys- prediction), reduction_indices=[1]))     tf.summary.scalar('loss', loss) 

三、合并所有训练图
使用tf.summary.merge_all()

sess= tf.Session()merged = tf.summary.merge_all() # tensorflow >= 0.12writer = tf.summary.FileWriter("logs/", sess.graph)sess.run(tf.global_variables_initializer()) 

四、训练数据
merged 也需要run 才能发挥作用

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)

五、在 tensorboard 中查看效果
程序运行完毕之后, 会产生logs目录 , 在logs文件夹的化桑层目录，使用命令 tensorboard –logdir=logs,浏览器中输入使用 http://localhost:6006，查看效果

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