TensorBoard cheatsheet

step 1 构建模型

import tensorflow as tf
import matplotlib.pyplot as plt
import os

fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

train_images = train_images / 255.0
test_images = test_images / 255.0

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

step 2 设置log的根目录，以及每次模型保存的目录

root_logdir = './my_log'

def get_run_logdir():
    import time
    run_id = time.strftime('run_%Y_%m_%d-%H_%M_%S')
    return os.path.join(root_logdir, run_id)

step 3 构建Callback

run_logdir = get_run_logdir()
tensorboard_cb = tf.keras.callbacks.TensorBoard(run_logdir)

step 4训练

history = model.fit(train_images, train_labels, epochs=10, validation_split=0.3, callbacks=[tensorboard_cb])

最后，在对应runid的目录启动tensorboard

tensorboard --logdir=./ --port 6006