TensorBoard-01-网络结构

TensorBoard-01-网络结构

官方的例子，for me， 一个小白，还是有点复杂，于是，我只好把它拆分一下，自我学习，大佬们就直接跳过把

本篇不进行训练，只搭建网络结构。并在 tensorboard 中展示

本文打算搭建一个2个隐层的神经网络结构

import tensorflow as tf

# 函数准备def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):    with tf.name_scope(layer_name):        with tf.name_scope('weight'):            w = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1))        with tf.name_scope('bias'):            b = tf.Variable(tf.truncated_normal([output_dim], stddev=0.1))        with tf.name_scope('wx_plus_b'):            out = tf.add(tf.matmul(input_tensor,w), b)        with tf.name_scope('activation'):            out = act(out)        return out

with tf.name_scope('input'):    x = tf.placeholder(tf.float32, [None, 784], name='x_input')    y = tf.placeholder(tf.float32, [None, 10], name='y_input')# 搭建网络# first layerhidden_1 = nn_layer(input_tensor=x, input_dim=784, output_dim=128, layer_name='layer_01')# Dropper layerwith tf.name_scope('dropout'):    dropped = tf.nn.dropout(hidden_1, keep_prob=1.0)# second layer => outputpre = nn_layer(input_tensor=dropped, input_dim=128, output_dim=10, layer_name='layer_02')with tf.name_scope('loss'):    loss = tf.reduce_mean(tf.square(pre - y), name='my_loss')with tf.name_scope('train'):    optm = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)sess = tf.Session()summary_writer = tf.summary.FileWriter(logdir='logs/', graph=sess.graph)sess.run(tf.global_variables_initializer())

输出的结果如图所示：

Reference

1. https://www.youtube.com/watch?v=SDeQRRRMUHU