TensorBoard: Visualize TensorFlow Training

To visualize the training process of a TensorFlow model using TensorBoard, you need to follow these steps:

1. Add a TensorBoard callback in the code: This allows you to collect metrics data, such as loss value and accuracy, during the training process of a TensorFlow model. You can add the TensorBoard callback by using the tf.keras.callbacks.TensorBoard() function when creating the model. See example code below:

import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard

# 创建TensorBoard回调函数
tensorboard_callback = TensorBoard(log_dir="logs")

# 创建并编译模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 训练模型并添加TensorBoard回调函数
model.fit(train_images, train_labels, epochs=10, callbacks=[tensorboard_callback])

1. Start the TensorBoard server: During model training, TensorBoard collects metric data and saves it in the specified log directory. To view this data, the TensorBoard server needs to be launched. The following code can be executed through the command line to start the TensorBoard server:

tensorboard --logdir=logs

1. To access the TensorBoard interface, simply enter http://localhost:6006/ in your browser. Here, you can view metrics such as loss and accuracy during the training process, as well as visualize them through graphs like curves and histograms.

By following the above steps, you can utilize TensorBoard to visualize the training process of a TensorFlow model, helping to gain a more intuitive understanding of the model’s training progress.