- An integer defining the batch size in data items (frames, words, subwords, etc.) per batch.
A mini-batch has at least a time-dimension and a batch-dimension (or sequence-dimension),
and depending on dense or sparse, also a feature-dimension.
batch_sizeis the upper limit for
time * sequencesduring creation of the mini-batches.
Defines the default value for
seq_orderingacross all datasets. It is recommended to not use this parameter, but rather define
seq_orderingexplicitely in the datasets for better readability. Possible values are:
default: Keep the sequences as is
reverse: Use the default sequences in reversed order
random: Shuffle the data with a predefined fixed seed
random:<seed>: Shuffle the data with the seed given
sorted: Sort by length (only if available), beginning with shortest sequences
sorted_reverse: Sort by length, beginning with longest sequences
laplace:<n_buckets>: Sort by length with n laplacian buckets (one bucket means going from shortest to longest and back with 1/n of the data).
laplace:.<n_sequences>: sort by length with n sequences per laplacian bucket.
Note that not all sequence order modes are available for all datasets, and some datasets may provide additional modes.
- You can chunk sequences of your data into parts, which will greatly reduce the amount of needed zero-padding.
This option is a string of two numbers, separated by a comma, i.e.
chunk_sizeis the size of a chunk, and
chunk_stepis the step after which we create the next chunk. I.e. the chunks will overlap by
chunk_size - chunk_stepframes. Set this to
0to disable it, or for example
100:75to enable it.
If set to
True, checkpoints are removed based on their score on the dev set. Per default, 2 recent, 4 best, and the checkpoints 20,40,80,160,240 are kept. Can be set as a dictionary to specify additional options.
keep_last_n: integer defining how many recent checkpoints to keep
keep_best_n: integer defining how many best checkpoints to keep
keep: list or set of integers defining which checkpoints to keep
- A dict with string:integer pairs. The string must be a valid data key,
and the integer specifies the upper bound for this data object.
During batch construction any sequence where the specified data object exceeds the upper bound are discarded.
Note that some datasets (e.g
OggZipDataset) load and process the data to determine the length, so even for discarded sequences data processing might be performed.
- An integer specifying the upper limit of sequences in a batch (can be used in addition to
- An integer specifying the number of epochs to train.
- An integer specifying after how many epochs the model is saved.
- An integer or string specifying the epoch to start the training at. The default is ‘auto’.
- If set to
False, the training will not be interupted if a single update step has a loss with NaN of Inf