Question: I am about to train a big LSTM network with 2-3 million articles and am struggling with Memory Errors (I use AWS EC2 g2x2large).

I found out that one solution is to reduce the `batch_size`. However, I am not sure if this parameter is only related to memory efficiency issues or if it will effect my results. As a matter of fact, I also noticed that `batch_size` used in examples is usually as a power of two, which I don't understand either.

I don't mind if my network takes longer to train, but I would like to know if reducing the `batch_size` will decrease the quality of my predictions.

Thanks.

Answer: 
After one and a half years, I come back to my answer because my previous answer was wrong. 

Batch size impacts learning significantly. What happens when you put a batch through your network is that you average the gradients. The concept is that if your batch size is big enough, this will provide a stable enough estimate of what the gradient of the full dataset would be. By taking samples from your dataset, you estimate the gradient while reducing computational cost significantly. The lower you go, the less accurate your esttimate will be, however in some cases these noisy gradients can actually help escape local minima. When it is too low, your network weights can just jump around if your data is noisy and it might be unable to learn or it converges very slowly, thus negatively impacting total computation time.

Another advantage of batching is for GPU computation, GPUs are very good at parallelizing the calculations that happen in neural networks if part of the computation is the same (for example, repeated matrix multiplication over the same weight matrix of your network). This means that a batch size of 16 will take less than twice the amount of a batch size of 8.

In the case that you do need bigger batch sizes but it will not fit on your GPU, you can feed a small batch, save the gradient estimates and feed one or more batches, and then do a weight update. This way you get a more stable gradient because you increased your virtual batch size.
I feel the accepted answer is possibly wrong. There are variants in *Gradient Descent Algorithms*.

1. **Vanilla Gradient Descent**: Here the Gradient is being calculated on all the data points at a single shot and the average is taken. Hence we have a smoother version of the gradient takes longer time to learn.
2. **Stochastic Gradient Descent** : Here one-data point at a time hence the gradient is aggressive (noisy gradients) hence there is going to be lot of oscillations ( we use Momentum parameters - e.g Nesterov to control this). So there is a chance that your oscillations can make the algorithm not reach a local minimum.(diverge).
3. **Mini-Batch Gradient Descent**: Which takes the perks of both the previous ones averages gradients of a small batch. Hence not too aggressive like SGD and allows Online Learning which Vanilla GD never allowed.

The smaller the Mini-Batch the better would be the performance of your model (not always) and of course it has got to do with your epochs too faster learning. If you are training on large dataset you want faster convergence with good performance hence we pick Batch-GD's. 

SGD had fixed learning parameter hence we start other Adaptive Optimizers like Adam, AdaDelta, RMS Prop etc which changes the learning parameter based on the history of Gradients.