tensorboard的使用

# coding: utf-8
from __future__ import print_function
from __future__ import division

import tensorflow as tf
import numpy as np
import os
import argparse


def dense_to_one_hot(input_data, class_num):
    data_num = input_data.shape[0]
    index_offset = np.arange(data_num) * class_num
    labels_one_hot = np.zeros((data_num, class_num))
    labels_one_hot.flat[index_offset + input_data.ravel()] = 1
    return labels_one_hot


def build_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument('--train_path', type=str, required=True)
    parser.add_argument('--test_path', type=str, required=True)
    parser.add_argument('--model_path', type=str, required=True)
    parser.add_argument('--board_dir', type=str, required=True)
    args = parser.parse_args()
    return args


def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min', tf.reduce_min(var))
        tf.summary.histogram('histogram', var)

p = build_parser()
if tf.gfile.Exists(p.board_dir):
    tf.gfile.DeleteRecursively(p.board_dir)
tf.gfile.MakeDirs(p.board_dir)

origin_train = np.genfromtxt(p.train_path, delimiter=',')
data_train = origin_train[:, 0:2]
labels_train = origin_train[:, 2]

origin_test = np.genfromtxt(p.train_path, delimiter=',')
data_test = origin_train[:, 0:2]
labels_test = origin_train[:, 2]


learning_rate = 0.001
training_epochs = 5000
display_step = 1

n_features = 2
n_class = 2
x = tf.placeholder(tf.float32, [None, n_features], "input")
y = tf.placeholder(tf.float32, [None, n_class])
with tf.name_scope('W'):
    W = tf.Variable(tf.zeros([n_features, n_class]), name="w")
    variable_summaries(W)
with tf.name_scope('b'):
    b = tf.Variable(tf.zeros([n_class]), name="b")
    variable_summaries(b)


scores = tf.nn.xw_plus_b(x, W, b, name='scores')

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=scores, labels=y))
tf.summary.scalar('cross_entropy', cost)
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(p.board_dir, 'train'))
test_writer = tf.summary.FileWriter(os.path.join(p.board_dir, 'test'))

saver = tf.train.Saver()
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        _, c = sess.run([optimizer, cost],
                        feed_dict={x: data_train,
                                   y: dense_to_one_hot(labels_train.astype(int), 2)})
        if epoch % 100 == 0:
            summary, c = sess.run([merged, cost],
                            feed_dict={x: data_train,
                                       y: dense_to_one_hot(labels_train.astype(int), 2)})
            train_writer.add_summary(summary, epoch)
            test_writer.add_summary(summary, epoch)
    saver.save(sess, p.model_path)
train_writer.close()
test_writer.close()