TensorBoard: Visualize Gradient Parameters

Viewing gradient parameters in TensorBoard can help us understand how the parameters of a model change during the training process, ultimately leading to better model optimization. Here are the steps to view gradient parameters in TensorBoard:

1. tf.summary.scalar is a function used for logging scalar values during training in TensorFlow.

# 在优化器中设置记录梯度参数
optimizer = tf.keras.optimizers.Adam()
grad_summary_writer = tf.summary.create_file_writer(log_dir)

@tf.function
def train_step(inputs, targets):
    with tf.GradientTape() as tape:
        predictions = model(inputs)
        loss = loss_function(targets, predictions)

    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    # 记录梯度参数
    with grad_summary_writer.as_default():
        for i, grad in enumerate(gradients):
            tf.summary.scalar('gradient_' + model.trainable_variables[i].name, tf.norm(grad), step=optimizer.iterations)

1. Start TensorBoard and specify the log directory with a command like “tensorboard –logdir=path/to/log_dir”.
2. Open the TensorBoard website in your browser and select the “graphs” tab.
3. In the graphs tab, you can view the displayed gradient parameters recorded in the computation graph. You can further examine the gradient values of each parameter as they change with training steps.

By following the steps above, we can visualize gradient parameters in TensorBoard and understand how the parameters change during the model training process, in order to optimize the model more effectively.