Effect of Batch Size on Training Process and Results by Gradient Accumulation

The plots show that small batch results generally in rapid learning but a volatile learning process with higher variance in the classification accuracy. Larger batch sizes slow down the learning process but the final stages result in a convergence to a more stable model exemplified by lower variance in classification accuracy. They show this hypothesis on several different network architectures with different learning rate schedules. This was a very comprehensive paper and I would suggest reading this paper. They came up with several steps that they used to severely cut down model training time without completely destroying performance. I then computed the L_2 distance between the final weights and the initial weights.

Effect of batch size on training dynamics

It is generally accepted that there is some “sweet spot” for batch size between 1 and the entire training dataset that will provide the best generalization. This “sweet spot” Effect of batch size on training dynamics usually depends on the dataset and the model at question. The reason for better generalization is vaguely attributed to the existence to “noise” in small batch size training.

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Goyal et al. (2017) have shown that the accuracy of ResNet-50 training on ImageNet for batch size 256 could be maintained with batch sizes of up to 8192 by using a gradual warm-up scheme, and with a BN batch size of 32 whatever the SGD batch size. This warm-up strategy has been also applied to the CIFAR-10 and CIFAR-100 cases investigated here. In our experiments, the use of a gradual warm-up did improve the performance of the large batch sizes, but did not fully recover the performance achieved with the smallest batch sizes. We investigate the batch size in the context of image classification, taking MNIST dataset to experiment.

Conversely, to keep the scaling of the covariance of the weight update vector ηΔθ constant would require scaling η with the square root of the batch size m (Krizhevsky, 2014; Hoffer et al., 2017). Hoffer et al. (2017) have shown empirically that it is possible to maintain generalization performance with large batch training by performing the same number of SGD updates. However, this implies a computational overhead proportional to the mini-batch size, which negates the effect of improved hardware efficiency due to increased parallelism.

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It is well known in the Machine Learning community the difficulty of making general statements about the effects of hyperparameters as behaviour often varies from dataset to dataset and model to model. Therefore, the conclusions we make can only serve as a signposts rather than general statements about the batch size. As I mentioned at the start, training dynamics depends heavily on the dataset and model so these conclusions are signposts rather than the last word in understanding the effects of batch size. Here is a plot of the distance from initial weights versus training epoch for batch size 64. It’s hard to see the other 3 lines because they’re overlapping but it turns out it doesn’t matter because all three cases we recover the 98% asymptotic test accuracy!

Effect of batch size on training dynamics

It should not be surprising that there is a lot of research into how different Batch Sizes affect aspects of your ML pipelines. This article will summarize some of the relevant research when it comes to batch sizes and supervised learning. To get a complete picture of the process, we will look at how batch size affects performance, training costs, and generalization. Each cell shows the distance from the final weights to the initial weights (W), the distance from the final biases to the initial biases (B) and the test accuracy (A). It’s hard to see, but at the particular value along the horizontal axis I’ve highlighted we see something interesting. Larger batch sizes has many more large gradient values (about 10⁵ for batch size 1024) than smaller batch sizes (about 10² for batch size 2).

What Is a Batch?¶

We deduce that for SGD the weights are initialized to approximately the magnitude you want them to be (about 44) and most of the learning is shuffling the weights along the hyper-sphere of the initial radius (about 44). Assuming the weights are also initialized with magnitude about 44, the weights travel https://accounting-services.net/is-compound-interest-the-most-powerful-force-in/ to a final distance of 258. In some ways, applying the analyse tools of mathematics to neural networks is analogous to trying to apply physics to the study of biological systems. Biological systems and neural networks, are much too complex to describe at the individual particle or neuron level.

  • We trained 6 different models, each with a different batch size of 32, 64, 128, 256, 512, and 1024 samples, keeping all other hyperparameters same in all the models.
  • For perspective let’s find the distance of the final weights to the origin.
  • The use of small batch sizes also has the advantage of requiring a significantly smaller memory footprint, which affords an opportunity to design processors which gain efficiency by exploiting memory locality.
  • This technique has been shown to significantly improve training performance and has now become a standard component of many state-of-the-art networks.
  • I didn’t take more data because storing the gradient tensors is actually very expensive (I kept the tensors of each trial to compute higher order statistics later on).
  • Perhaps if the samples are split into two batches, then competition is reduced as the model can find weights that will fit both samples well if done in sequence.

This linear scaling rule has been widely adopted, e.g., in Krizhevsky (2014), Chen et al. (2016), Bottou et al. (2016), Smith et al. (2017) and Jastrzebski et al. (2017). When we run the exact same code on the Nvidia RTX 2080Ti 11GB, we are able to run with a batch size of 16 and a GPU memory utilization of 90.3%. One thing to keep in mind is the nature of BatchNorm layers which will still function per batch. You need to replace them with GroupNorm layers to be effective while performing gradient accumulation. Our experiment uses Convolutional Neural Networks (CNN) to classify the images in MNIST dataset (containing images of handwritten digits 0 to 9) to corresponding digit labels “0” to “9″. Fig. below showing the images of handwritten digits.

As expected, the gradient is larger early on during training (blue points are higher than green points). Contrary to our hypothesis, the mean gradient norm increases with batch size! We expected the gradients to be smaller for larger batch size due to competition amongst data samples.

  • This intrinsically ties the empirical loss (1) that is minimized to the choice of batch size.
  • Therefore, training with large batch sizes tends to move further away from the starting weights after seeing a fixed number of samples than training with smaller batch sizes.
  • We deduce that for SGD the weights are initialized to approximately the magnitude you want them to be (about 44) and most of the learning is shuffling the weights along the hyper-sphere of the initial radius (about 44).
  • In the following experiment, I seek to answer why increasing the learning rate can compensate for larger batch sizes.
  • If a model is using double the batch size, it will by definition go through the dataset with half the updates.

In conclusion, starting with a large batch size doesn’t “get the model stuck” in some neighbourhood of bad local optimums. The model can switch to a lower batch size or higher learning rate anytime to achieve better test accuracy. The neon yellow curves serve as a control to make sure we aren’t doing better on the test accuracy because we’re simply training more. If you pay careful attention to the x-axis, the epochs are enumerated from 0 to 30. For experiments with greater than 30 epochs of training in total, the first x − 30 epochs have been omitted. The comparison of (7), (8) highlights how, under the assumption of constant ~η, large batch training can be considered to be an approximation of small batch methods that trades increased parallelism for stale gradients.

Adaptive subgradient methods for online learning and stochastic optimization

Where the bars represent normalized values and i denotes a certain batch size. For each of the 1000 trials, I compute the Euclidean norm of the summed gradient tensor (black arrow in our picture). I then compute the mean and standard deviation of these norms across the 1000 trials.

  • Gradient accumulation is a mechanism to split the batch of samples, used for training a neural network, into several mini-batches of samples that will be run sequentially.
  • The model can switch to a lower batch size or higher learning rate anytime to achieve better test accuracy.
  • In experiments, we aim to investigate in detail the effectiveness of the proposed method by analyzing the transition for the value of loss function and prediction accuracy.
  • The scikit-learn class provides the make_blobs() function that can be used to create a multi-class classification problem with the prescribed number of samples, input variables, classes, and variance of samples within a class.
  • Just as with our previous conclusion, take this conclusion with a grain of salt.
  • We observe that the calculation of the mean and variance across the batch makes the loss calculated for a particular example dependent on other examples of the same batch.

For reference, here are the raw distributions of the gradient norms (same plots as previously but without the μ_1024/μ_i normalization). The problem can be configured to have two input variables (to represent the $x$ and $y$ coordinates of the points) and a standard deviation of $2.0$ for points within each group. We will use the same random state (seed for the pseudorandom number generator) to ensure that we always get the same data points. Indeed the model is able to find the far away solution and achieve the better test accuracy. This is a longer blogpost where I discuss results of experiments I ran myself.

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