tensorboard可视化实例

“针对tensorflow1.x版本”
e.g.1:对于一个简单的加法而言：

import tensorflow as tf
a = tf.constant([1.0,2.0,3.0],name='input1') b = tf.Variable(tf.random_uniform([3]),name='input2') add = tf.add_n([a,b],name='addOP') with tf.Session() as sess: sess.run(tf.global_variables_initializer()) writer = tf.summary.FileWriter("./tflogs",sess.graph) print(sess.run(add)) writer.close() 

结果如图

e.g.2:
输入层(1 个神经元),隐藏层(10 神经元),输出层(1 个神经元),来拟合一个二次函数曲线 y = x^2 − 0.5

 # -*- coding: utf-8 -*- import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #构建满足一元二次方程的函数 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 # 看一下分布如何 plt.plot(x_data, y_data, 'ro') plt.show() 

构建网络模型,对于模型的参数加到summary中让其显示

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+'/home/april/PycharmProjects/untitled/tflogs2/weights',Weights) with tf.name_scope('biases'): biases = tf.Variable(tf.zeros([1, out_size]) +0.1, name='b') tf.summary.histogram(layer_name + '/home/april/PycharmProjects/untitled/tflogs2/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 + '/home/april/PycharmProjects/untitled/tflogs2/outputs', outputs) return outputs 

显示loss函数

# 构建隐藏层，假设隐藏层有 10 个神经元 l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu) # 构建输出层，假设输出层和输入层一样，有 1 个神经元 prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None) # 构建损失函数 with tf.name_scope('loss'): loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1])) tf.summary.scalar('loss', loss) train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) 

也要记录日志

# 初始化所有变量 init = tf.global_variables_initializer() sess = tf.Session() merged = tf.summary.merge_all() writer = tf.summary.FileWriter("/home/april/PycharmProjects/untitled/tfpics/", sess.graph) sess.run(init) for i in range(1000): # 训练 1000 次 sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) if i % 50 == 0: # 每 50 次打印出一次损失值 result = sess.run(merged, feed_dict={xs: x_data, ys: y_data}) writer.add_summary(result, i) # print(sess.run(loss, feed_dict={xs: x_data, ys: y_data})) 

最后显示结果为
loss:
网络结构图：

DISTRIBUTIONS 面板和 HISTOGRAMS 面板所用到的数据源相同,只是从不同的视角、不同的方式表征数据的情况。

本文地址：https://blog.csdn.net/zimengxueying/article/details/109032974