在Tensorflow中，有時想要使用tensorboard來監視一些指標的變化。下面給出一個小小的例子。我們用到的函式有tf.summary.scalar()，tf.summary.FileWriter(), file_writer.add_summary()

---

程式碼

tf.summary.scalar()

with tf.name_scope("loss"):
    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,
                                                              logits=logits)
    loss = tf.reduce_mean(xentropy, name="loss")
    loss_summary = tf.summary.scalar('log_loss', loss)

def log_dir(prefix=""):
    now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
    root_logdir = "tf_logs"
    if prefix:
        prefix += "-"
    name = prefix + "run-" + now
    return "{}/{}/".format(root_logdir, name)
logdir = log_dir("logreg")
file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
with tf.Session() as sess:
            accuracy_val, loss_val, accuracy_summary_str, loss_summary_str = sess.run([
        accuracy, loss, accuracy_summary, loss_summary], feed_dict={
            X: mnist.validation.images, y: mnist.validation.labels})
        file_writer.add_summary(accuracy_summary_str, epoch)

tensorboard –logdir=tf_logs

這邊是上述程式碼片段的來源，這是一個可以跑通的手寫體識別程式碼

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
import tensorflow as tf
from datetime import datetime
import os
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("../data/")
X_train = mnist.train.images
X_test = mnist.test.images
y_train = mnist.train.labels.astype("int")
y_test = mnist.test.labels.astype("int")

n_inputs = 28 * 28  # MNIST
n_hidden1 = 300
n_hidden2 = 100
n_outputs = 10

X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
y = tf.placeholder(tf.int64, shape=(None), name="y")


with tf.name_scope("dnn"):
    he_init = tf.contrib.layers.variance_scaling_initializer()
    xavier = tf.contrib.layers.xavier_initializer()
    hidden1 = tf.layers.dense(X, n_hidden1, name="hidden1", kernel_initializer=he_init,
                              activation=tf.nn.relu)
    hidden2 = tf.layers.dense(hidden1, n_hidden2, name="hidden2", kernel_initializer=he_init,
                              activation=tf.nn.relu)
    logits = tf.layers.dense(
        hidden2, n_outputs, name="outputs", kernel_initializer=he_init)

with tf.name_scope("loss"):
    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,
                                                              logits=logits)
    loss = tf.reduce_mean(xentropy, name="loss")
    loss_summary = tf.summary.scalar('log_loss', loss)

learning_rate = 0.01
with tf.name_scope("train"):
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    training_op = optimizer.minimize(loss)


with tf.name_scope("eval"):
    correct = tf.nn.in_top_k(logits, y, 1)
    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
    accuracy_summary = tf.summary.scalar('accuracy', accuracy)

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

n_epochs = 40
batch_size = 50


def log_dir(prefix=""):
    now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
    root_logdir = "tf_logs"
    if prefix:
        prefix += "-"
    name = prefix + "run-" + now
    return "{}/{}/".format(root_logdir, name)


logdir = log_dir("logreg")

file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())

checkpoint_path = "./tmp/my_logreg_model.ckpt"
checkpoint_epoch_path = checkpoint_path + ".epoch"
final_model_path = "./my_logreg_model"

best_loss = np.infty
epochs_without_progress = 0
max_epochs_without_progress = 50

with tf.Session() as sess:
    if os.path.isfile(checkpoint_epoch_path):
        # if the checkpoint file exists, restore the model and load the epoch
        # number
        with open(checkpoint_epoch_path, "rb") as f:
            start_epoch = int(f.read())
        print("Training was interrupted. Continuing at epoch", start_epoch)
        saver.restore(sess, checkpoint_path)
    else:
        start_epoch = 0
        sess.run(init)

    init.run()
    for epoch in range(n_epochs):
        for iteration in range(mnist.train.num_examples // batch_size):
            X_batch, y_batch = mnist.train.next_batch(batch_size)
            sess.run([training_op, loss_summary],
                     feed_dict={X: X_batch, y: y_batch})
        accuracy_val, loss_val, accuracy_summary_str, loss_summary_str = sess.run([
            accuracy, loss, accuracy_summary, loss_summary], feed_dict={
            X: mnist.validation.images, y: mnist.validation.labels})
        file_writer.add_summary(accuracy_summary_str, epoch)
        file_writer.add_summary(loss_summary_str, epoch)

        if epoch % 5 == 0:
            print("epoch:", epoch, "\tVal accuracy:{:.3f}%".format(
                accuracy_val * 100), "\tLoss:{:.5f}".format(loss_val))
            saver.save(sess, checkpoint_path)
            with open(checkpoint_epoch_path, "wb") as f:
                f.write(b"%d" % (epoch + 1))
            if loss_val < best_loss:
                saver.save(sess, final_model_path)
                best_loss = loss_val
            else:
                epochs_without_progress += 5
                if epochs_without_progress > max_epochs_without_progress:
                    print("Early stopping")
                    break
    os.remove(checkpoint_epoch_path)
# tensorboard --logdir=tf_logs

然後在命令列裡敲入 tensorboard –logdir=tf_logs