用小批量理解渐变胶带

Question

在以下取自Keras documentation 的示例中，我想了解grads 是如何计算的。梯度grads 是否对应于使用批处理(x_batch_train, y_batch_train) 计算的平均梯度？换句话说，算法是否使用小批量中的每个样本计算每个变量的梯度，然后平均得到grads？

for epoch in range(epochs):
    print("\nStart of epoch %d" % (epoch,))

    # Iterate over the batches of the dataset.
    for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):

        # Open a GradientTape to record the operations run
        # during the forward pass, which enables auto-differentiation.
        with tf.GradientTape() as tape:

            # Run the forward pass of the layer.
            # The operations that the layer applies
            # to its inputs are going to be recorded
            # on the GradientTape.
            logits = model(x_batch_train, training=True)  # Logits for this minibatch

            # Compute the loss value for this minibatch.
            loss_value = loss_fn(y_batch_train, logits)

        # Use the gradient tape to automatically retrieve
        # the gradients of the trainable variables with respect to the loss.
        grads = tape.gradient(loss_value, model.trainable_weights)

        # Run one step of gradient descent by updating
        # the value of the variables to minimize the loss.
        optimizer.apply_gradients(zip(grads, model.trainable_weights))

Answer 1

默认值为SUM_OVER_BATCH_SIZE。

阅读this。

Answer 2

你的假设是正确的。

DachuanZhao 提供的文档也显示，批处理中元素的总和是平均的。