Module ktrain.text.ner.anago.trainer

Training-related module.

"""Training-related module.
"""

from ....imports import *
from .callbacks import F1score
from .utils import AnagoNERSequence


class Trainer(object):
    """A trainer that train the model.

    Attributes:
        _model: Model.
        _preprocessor: Transformer. Preprocessing data for feature extraction.
    """

    def __init__(self, model, preprocessor=None):
        self._model = model
        self._preprocessor = preprocessor

    def train(
        self,
        x_train,
        y_train,
        x_valid=None,
        y_valid=None,
        epochs=1,
        batch_size=32,
        verbose=1,
        callbacks=None,
        shuffle=True,
    ):
        """Trains the model for a fixed number of epochs (iterations on a dataset).

        Args:
            x_train: list of training data.
            y_train: list of training target (label) data.
            x_valid: list of validation data.
            y_valid: list of validation target (label) data.
            batch_size: Integer.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
            epochs: Integer. Number of epochs to train the model.
            verbose: Integer. 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch). `shuffle` will default to True.
        """

        train_seq = AnagoNERSequence(
            x_train, y_train, batch_size, self._preprocessor.transform
        )

        if x_valid and y_valid:
            valid_seq = AnagoNERSequence(
                x_valid, y_valid, batch_size, self._preprocessor.transform
            )
            f1 = F1score(valid_seq, preprocessor=self._preprocessor)
            callbacks = [f1] + callbacks if callbacks else [f1]

        self._model.fit_generator(
            generator=train_seq,
            epochs=epochs,
            callbacks=callbacks,
            verbose=verbose,
            shuffle=shuffle,
        )

Classes

class Trainer (model, preprocessor=None)

A trainer that train the model.

Attributes

_model

Model.

_preprocessor

Transformer. Preprocessing data for feature extraction.

Expand source code

class Trainer(object):
    """A trainer that train the model.

    Attributes:
        _model: Model.
        _preprocessor: Transformer. Preprocessing data for feature extraction.
    """

    def __init__(self, model, preprocessor=None):
        self._model = model
        self._preprocessor = preprocessor

    def train(
        self,
        x_train,
        y_train,
        x_valid=None,
        y_valid=None,
        epochs=1,
        batch_size=32,
        verbose=1,
        callbacks=None,
        shuffle=True,
    ):
        """Trains the model for a fixed number of epochs (iterations on a dataset).

        Args:
            x_train: list of training data.
            y_train: list of training target (label) data.
            x_valid: list of validation data.
            y_valid: list of validation target (label) data.
            batch_size: Integer.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
            epochs: Integer. Number of epochs to train the model.
            verbose: Integer. 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch). `shuffle` will default to True.
        """

        train_seq = AnagoNERSequence(
            x_train, y_train, batch_size, self._preprocessor.transform
        )

        if x_valid and y_valid:
            valid_seq = AnagoNERSequence(
                x_valid, y_valid, batch_size, self._preprocessor.transform
            )
            f1 = F1score(valid_seq, preprocessor=self._preprocessor)
            callbacks = [f1] + callbacks if callbacks else [f1]

        self._model.fit_generator(
            generator=train_seq,
            epochs=epochs,
            callbacks=callbacks,
            verbose=verbose,
            shuffle=shuffle,
        )

Methods

def train(self, x_train, y_train, x_valid=None, y_valid=None, epochs=1, batch_size=32, verbose=1, callbacks=None, shuffle=True)

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

Args

x_train

list of training data.

y_train

list of training target (label) data.

x_valid

list of validation data.

y_valid

list of validation target (label) data.

batch_size

Integer. Number of samples per gradient update. If unspecified, batch_size will default to 32.

epochs

Integer. Number of epochs to train the model.

verbose

Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.

callbacks

List of keras.callbacks.Callback instances. List of callbacks to apply during training.

shuffle

Boolean (whether to shuffle the training data before each epoch). shuffle will default to True.

Expand source code

def train(
    self,
    x_train,
    y_train,
    x_valid=None,
    y_valid=None,
    epochs=1,
    batch_size=32,
    verbose=1,
    callbacks=None,
    shuffle=True,
):
    """Trains the model for a fixed number of epochs (iterations on a dataset).

    Args:
        x_train: list of training data.
        y_train: list of training target (label) data.
        x_valid: list of validation data.
        y_valid: list of validation target (label) data.
        batch_size: Integer.
            Number of samples per gradient update.
            If unspecified, `batch_size` will default to 32.
        epochs: Integer. Number of epochs to train the model.
        verbose: Integer. 0, 1, or 2. Verbosity mode.
            0 = silent, 1 = progress bar, 2 = one line per epoch.
        callbacks: List of `keras.callbacks.Callback` instances.
            List of callbacks to apply during training.
        shuffle: Boolean (whether to shuffle the training data
            before each epoch). `shuffle` will default to True.
    """

    train_seq = AnagoNERSequence(
        x_train, y_train, batch_size, self._preprocessor.transform
    )

    if x_valid and y_valid:
        valid_seq = AnagoNERSequence(
            x_valid, y_valid, batch_size, self._preprocessor.transform
        )
        f1 = F1score(valid_seq, preprocessor=self._preprocessor)
        callbacks = [f1] + callbacks if callbacks else [f1]

    self._model.fit_generator(
        generator=train_seq,
        epochs=epochs,
        callbacks=callbacks,
        verbose=verbose,
        shuffle=shuffle,
    )