TensorFlow基础笔记 Tensorboard可视化显示

Posted 2020-10-14 Image Process

 参考: http://blog.csdn.net/l18930738887/article/details/55000008

http://www.jianshu.com/p/19bb60b52dad

http://blog.csdn.net/sinat_33761963/article/details/62433234

import tensorflow as tf
import numpy as np
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
# Make up some real data
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
# define placeholder for inputs to network
with tf.name_scope(\'inputs\'):
    xs = tf.placeholder(tf.float32, [None, 1],name=\'input_x\')
    ys = tf.placeholder(tf.float32, [None, 1],name=\'input_y\')

# add hidden layer
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)


# the error between prediciton and real data
with tf.name_scope(\'loss\'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                                        reduction_indices=[1]))
    tf.summary.scalar(\'loss\', loss)
with tf.name_scope(\'train\'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()
merged = tf.summary.merge_all()
# save the logs
writer = tf.summary.FileWriter("logs/", sess.graph)
sess.run(tf.global_variables_initializer())
for i in range(1000):
    # training
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
        # to see the step improvement
        result = sess.run(merged,
                          feed_dict={xs: x_data, ys: y_data})
        writer.add_summary(result, i)

 

到运行python的所在目录下，打一下命令：

$ tensorboard --logdir="logs/"

再在网页中输入链接：127.0.1.1:6006 即可获得展示： 推荐使用friefox浏览器，我电脑上chrom浏览器打不开

 

 

 

比如，从他人处获得一个Graph，想看看它的结构，怎么弄？

Google提供了一个工具，TensorBoard，它能以图表的方式分析你在训练过程中汇总的各种数据，其中包括Graph结构。

所以我们可以简单的写几行Pyhton，加载Graph，只在logdir里，输出Graph结构数据，并可以查看其图结构。

可参考：http://www.tensorfly.cn/tfdoc/how_tos/summaries_and_tensorboard.html

https://www.tensorflow.org/get_started/summaries_and_tensorboard

代码如下：

 

import tensorflow as tf
from tensorflow.python.platform import gfile

# 这是从二进制格式的pb文件加载模型
graph = tf.get_default_graph()
graphdef = graph.as_graph_def()
graphdef.ParseFromString(gfile.FastGFile("/data/TensorFlowandroidMNIST/app/src/main/expert-graph.pb", "rb").read())
_ = tf.import_graph_def(graphdef, name="")

 

 

import tensorflow as tf
from tensorflow.python.platform import gfile
#这是从文件格式的meta文件加载模型
graph = tf.get_default_graph()
graphdef = graph.as_graph_def()
# graphdef.ParseFromString(gfile.FastGFile("/data/TensorFlowAndroidMNIST/app/src/main/expert-graph.pb", "rb").read())
# _ = tf.import_graph_def(graphdef, name="")
_ = tf.train.import_meta_graph("./InsightFace_iter_best_1950000.ckpt.meta")
summary_write = tf.summary.FileWriter("./" , graph)

 

然后再启动tensorboard：

tensorboard --logdir /data/TensorFlowAndroidMNIST/logdir --host 你的ip --port 你端口(默认6006)

 

一个打开pb文件的实例

import tensorflow as tf
from tensorflow.python.platform import gfile

graph = tf.get_default_graph()
graphdef = graph.as_graph_def()
graphdef.ParseFromString(gfile.FastGFile("./log/mtcnn.pb", "rb").read())
_ = tf.import_graph_def(graphdef, name="")

summary_write = tf.summary.FileWriter("./log" , graph)