A machine learning engineer is training a deep neural network on a large dataset using a high-performance GPU. They observe that while using stochastic gradient descent (batch size of 1), the training progress is slow due to inefficient hardware utilization and the loss curve is extremely erratic. Conversely, attempting full-batch gradient descent results in out-of-memory errors. The engineer's goal is to achieve a stable and computationally efficient training process. Which of the following strategies directly addresses this specific trade-off?
Decrease the learning rate and add momentum to smooth out the erratic loss curve observed with stochastic updates.
Utilize mini-batch gradient descent with a batch size chosen to balance the noisy gradient estimates of stochastic updates and the memory requirements of full-batch processing, thereby enabling efficient parallel computation on the GPU.
Switch to an Adam optimizer, as its adaptive learning rate is inherently designed to handle large datasets without requiring batching.
Implement batch normalization after each layer to stabilize the erratic loss curve caused by the stochastic updates.
The correct answer is to use mini-batch gradient descent. This approach is the standard solution for the scenario described, as it strikes a crucial balance between the three main types of gradient descent. Full-batch gradient descent is not feasible due to memory constraints, and stochastic gradient descent (batch size of 1) is computationally inefficient on parallel hardware like GPUs and produces highly volatile gradient estimates. Mini-batch gradient descent processes a small subset of the data at a time, which allows for memory-efficient training and leverages the parallel processing capabilities of GPUs through vectorized operations. This also provides a more stable gradient estimate than SGD, leading to smoother convergence, while updating weights more frequently than full-batch GD, leading to faster training.
Batch normalization is a useful technique for stabilizing training and accelerating convergence, but it does not solve the fundamental problems of memory overflow from full-batch GD or the computational inefficiency of single-instance updates in SGD.
Switching to an Adam optimizer is a good practice for many models, but it does not eliminate the need for batching. Optimizers like Adam operate on batches of data (usually mini-batches) to compute gradients and update weights.
Decreasing the learning rate and adding momentum are techniques used to manage the learning process, particularly to control oscillations from noisy gradients, but they do not address the core computational and memory issues described in the scenario. Mini-batching is the more direct and fundamental solution.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is mini-batch gradient descent and why is it useful?
Open an interactive chat with Bash
How does mini-batch size influence training performance?
Open an interactive chat with Bash
How does mini-batch gradient descent compare to the Adam optimizer?