The difference between “data time major” and “batch major” refers to how data is organized and accessed in recurrent neural networks (RNNs), particularly in frameworks like TensorFlow and PyTorch.
- Data Time Major (DTM):
- In DTM format, the time axis is the major axis. This means that the input data is organized such that each time step for a given sample is contiguous in memory.
- In practical terms, this means that the input data is organized as
(time_steps, batch_size, features), wheretime_stepsrepresents the length of the sequence,batch_sizerepresents the number of samples in the batch, andfeaturesrepresent the number of features at each time step. - TensorFlow and PyTorch support DTM by default.
- Batch Major:
- In batch major format, the batch axis is the major axis. This means that the data for each sample in the batch is contiguous in memory.
- In practical terms, this means that the input data is organized as
(batch_size, time_steps, features). - Batch major format is less common in practice but is supported by some frameworks or layers.
In summary, the main difference lies in the axis along which the data is organized. DTM organizes data along the time axis, while batch major organizes data along the batch axis. The choice between the two formats depends on the specific requirements of the neural network architecture and the capabilities of the underlying framework.