Interface ModelFitDatasetArgs<T>

(Optional) Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of samples of your dataset divided by the batch size, so that fitDataset() call can utilize the entire dataset. If it is not provided, use done return value in iterator.next() as signal to finish an epoch.

List of callbacks to be called during training. Can have one or more of the following callbacks:

• onTrainBegin(logs): called when training starts.
• onTrainEnd(logs): called when training ends.
• onEpochBegin(epoch, logs): called at the start of every epoch.
• onEpochEnd(epoch, logs): called at the end of every epoch.
• onBatchBegin(batch, logs): called at the start of every batch.
• onBatchEnd(batch, logs): called at the end of every batch.
• onYield(epoch, batch, logs): called every yieldEvery milliseconds with the current epoch, batch and logs. The logs are the same as in onBatchEnd(). Note that onYield can skip batches or epochs. See also docs for yieldEvery below.

Optional object mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.

If the model has multiple outputs, a class weight can be specified for each of the outputs by setting this field an array of weight object or an object that maps model output names (e.g., model.outputNames[0]) to weight objects.

Integer number of times to iterate over the training dataset.

Epoch at which to start training (useful for resuming a previous training run). When this is used, epochs is the index of the "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.

Optional batch size for validation.

Used only if validationData is an array of tf.Tensor objects, i.e., not a dataset object.

If not specified, its value defaults to 32.

(Optional) Only relevant if validationData is specified and is a dataset object.

Total number of batches of samples to draw from validationData for validation purpose before stopping at the end of every epoch. If not specified, evaluateDataset will use iterator.next().done as signal to stop validation.

Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be any of the following:

• An array [xVal, yVal], where the two values may be tf.Tensor, an array of Tensors, or a map of string to Tensor.
• Similarly, an array [xVal, yVal, valSampleWeights] (not implemented yet).
• a Dataset object with elements of the form {xs: xVal, ys: yVal}, where xs and ys are the feature and label tensors, respectively.

If validationData is an Array of Tensor objects, each tf.Tensor will be sliced into batches during validation, using the parameter validationBatchSize (which defaults to 32). The entirety of the tf.Tensor objects will be used in the validation.

If validationData is a dataset object, and the validationBatches parameter is specified, the validation will use validationBatches batches drawn from the dataset object. If validationBatches parameter is not specified, the validation will stop when the dataset is exhausted.

The model will not be trained on this data.

Verbosity level.

Expected to be 0, 1, or 2. Default: 1.

0 - No printed message during fit() call. 1 - In Node.js (tfjs-node), prints the progress bar, together with real-time updates of loss and metric values and training speed. In the browser: no action. This is the default. 2 - Not implemented yet.

Configures the frequency of yielding the main thread to other tasks.

In the browser environment, yielding the main thread can improve the responsiveness of the page during training. In the Node.js environment, it can ensure tasks queued in the event loop can be handled in a timely manner.

The value can be one of the following:

• 'auto': The yielding happens at a certain frame rate (currently set at 125ms). This is the default.
• 'batch': yield every batch.
• 'epoch': yield every epoch.
• a number: Will yield every number milliseconds.
• 'never': never yield. (But yielding can still happen through await nextFrame() calls in custom callbacks.)