src.model.custom.siamese_reps module

SiameseModel

Use this model to create the siamese model

class src.model.custom.siamese_reps.SiameseModelRepeat(*args, **kwargs)

Bases: Model, object

call(inputs, **kwargs)
fit(*args, **kwargs)

Trains the model for a fixed number of epochs (dataset iterations).

Parameters:

  • x – Input data. It can be: - A NumPy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A backend-native tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A keras.utils.PyDataset returning (inputs, targets) or (inputs, targets, sample_weights). - A tf.data.Dataset yielding (inputs, targets) or (inputs, targets, sample_weights). - A torch.utils.data.DataLoader yielding (inputs, targets) or (inputs, targets, sample_weights). - A Python generator function yielding (inputs, targets) or (inputs, targets, sample_weights).

  • y – Target data. Like the input data x, it can be either NumPy array(s) or backend-native tensor(s). If x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or a Python generator function, y should not be specified since targets will be obtained from x.

  • batch_size – Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your input data x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function since they generate batches.

  • epochs – Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided (unless the steps_per_epoch flag is set to something other than None). Note that in conjunction with initial_epoch, epochs is to be understood as “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.

  • verbose – “auto”, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. “auto” becomes 1 for most cases. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g., in a production environment). Defaults to “auto”.

  • callbacks – List of keras.callbacks.Callback instances. List of callbacks to apply during training. See keras.callbacks. Note keras.callbacks.ProgbarLogger and keras.callbacks.History callbacks are created automatically and need not be passed to model.fit(). keras.callbacks.ProgbarLogger is created or not based on the verbose argument in model.fit().

  • validation_split – Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is only supported when x and y are made of NumPy arrays or tensors. If both validation_data and validation_split are provided, validation_data will override validation_split.

  • validation_data – 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. Thus, note the fact that the validation loss of data provided using validation_split or validation_data is not affected by regularization layers like noise and dropout. validation_data will override validation_split. It can be: - A tuple (x_val, y_val) of NumPy arrays or tensors. - A tuple (x_val, y_val, val_sample_weights) of NumPy arrays. - A keras.utils.PyDataset, a tf.data.Dataset, a torch.utils.data.DataLoader yielding (inputs, targets) or a Python generator function yielding (x_val, y_val) or (inputs, targets, sample_weights).

  • shuffle – Boolean, whether to shuffle the training data before each epoch. This argument is ignored when x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function.

  • class_weight – Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to “pay more attention” to samples from an under-represented class. When class_weight is specified and targets have a rank of 2 or greater, either y must be one-hot encoded, or an explicit final dimension of 1 must be included for sparse class labels.

  • sample_weight – Optional NumPy array or tensor of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) NumPy array or tensor with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D NumPy array or tensor with shape (samples, sequence_length) to apply a different weight to every timestep of every sample. This argument is not supported when x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function. Instead, provide sample_weights as the third element of x. Note that sample weighting does not apply to metrics specified via the metrics argument in compile(). To apply sample weighting to your metrics, you can specify them via the weighted_metrics in compile() instead.

  • initial_epoch – Integer. Epoch at which to start training (useful for resuming a previous training run).

  • steps_per_epoch – Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors or NumPy arrays, the default None means that the value used is the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps_per_epoch argument, otherwise the training will run indefinitely.

  • validation_steps – Integer or None. Only relevant if validation_data is provided. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If validation_steps is None, validation will run until the validation_data dataset is exhausted. In the case of an infinitely repeating dataset, it will run indefinitely. If validation_steps is specified and only part of the dataset is consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time.

  • validation_batch_size – Integer or None. Number of samples per validation batch. If unspecified, will default to batch_size. Do not specify the validation_batch_size if your data is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function since they generate batches.

  • validation_freq – Only relevant if validation data is provided. Specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs.

Unpacking behavior for iterator-like inputs:

A common pattern is to pass an iterator like object such as a tf.data.Dataset or a keras.utils.PyDataset to fit(), which will in fact yield not only features (x) but optionally targets (y) and sample weights (sample_weight). Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length-one tuple, effectively treating everything as x. When yielding dicts, they should still adhere to the top-level tuple structure, e.g. ({“x0”: x0, “x1”: x1}, y). Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: namedtuple(“example_tuple”, [“y”, “x”]) it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: namedtuple(“other_tuple”, [“x”, “y”, “z”]) where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to x.

Returns:

A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

get_distances(data, **kwargs) → Tuple[Any, Any]
predict_step(data)
test_step(data)
train_step(data)