tensorflow--代码学习2(tensorboard)_供学习的tensorflow代码-优快云博客

本文链接：https://blog.youkuaiyun.com/newas3/article/details/90145446

本文介绍了如何使用TensorFlow的TensorBoard进行可视化，重点在于理解`tf.name_scope()`的作用。通过TensorBoard，可以直观地查看和理解模型的结构和训练过程。

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#可视化 tensorboard
import tensorflow as tf
import numpy as np

def add_layer(inputs,in_size,out_size,activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('Wieght'):
            Weight = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name='biases')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs,Weight),biases,name='Wx_plus_b')
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs
#creat net
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

with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32,[None,1],name='x_input')
    ys = tf.placeholder(tf.float32,[None,1],name='y_input')

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1,activation_function=None)

with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]),name='loss')
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

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/',tf.get_default_graph())

附：
tesorboard为tensorflow实现可视化，主要为tf.name_scope()函数的使用
tensorboard使用参看：
tensorflow–可视化tensorboard