Module ktrain.text.ner.anago.callbacks

Custom callbacks.

Expand source code
"""
Custom callbacks.
"""
from ....imports import *
from .. import metrics

class F1score(keras.callbacks.Callback):

    def __init__(self, seq, preprocessor=None):
        super(F1score, self).__init__()
        self.seq = seq
        self.p = preprocessor

    def get_lengths(self, y_true):
        lengths = []
        for y in np.argmax(y_true, -1):
            try:
                i = list(y).index(0)
            except ValueError:
                i = len(y)
            lengths.append(i)

        return lengths

    def on_epoch_end(self, epoch, logs={}):
        label_true = []
        label_pred = []
        for i in range(len(self.seq)):
            x_true, y_true = self.seq[i]
            lengths = self.get_lengths(y_true)
            y_pred = self.model.predict_on_batch(x_true)

            y_true = self.p.inverse_transform(y_true, lengths)
            y_pred = self.p.inverse_transform(y_pred, lengths)

            label_true.extend(y_true)
            label_pred.extend(y_pred)

        score = metrics.f1_score(label_true, label_pred)
        print(' - f1: {:04.2f}'.format(score * 100))
        print(metrics.classification_report(label_true, label_pred))
        logs['f1'] = score

Classes

class F1score (seq, preprocessor=None)

Abstract base class used to build new callbacks.

Attributes

params
Dict. Training parameters (eg. verbosity, batch size, number of epochs…).
model
Instance of keras.models.Model. Reference of the model being trained.

The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings).

Expand source code
class F1score(keras.callbacks.Callback):

    def __init__(self, seq, preprocessor=None):
        super(F1score, self).__init__()
        self.seq = seq
        self.p = preprocessor

    def get_lengths(self, y_true):
        lengths = []
        for y in np.argmax(y_true, -1):
            try:
                i = list(y).index(0)
            except ValueError:
                i = len(y)
            lengths.append(i)

        return lengths

    def on_epoch_end(self, epoch, logs={}):
        label_true = []
        label_pred = []
        for i in range(len(self.seq)):
            x_true, y_true = self.seq[i]
            lengths = self.get_lengths(y_true)
            y_pred = self.model.predict_on_batch(x_true)

            y_true = self.p.inverse_transform(y_true, lengths)
            y_pred = self.p.inverse_transform(y_pred, lengths)

            label_true.extend(y_true)
            label_pred.extend(y_pred)

        score = metrics.f1_score(label_true, label_pred)
        print(' - f1: {:04.2f}'.format(score * 100))
        print(metrics.classification_report(label_true, label_pred))
        logs['f1'] = score

Ancestors

  • tensorflow.python.keras.callbacks.Callback

Methods

def get_lengths(self, y_true)
Expand source code
def get_lengths(self, y_true):
    lengths = []
    for y in np.argmax(y_true, -1):
        try:
            i = list(y).index(0)
        except ValueError:
            i = len(y)
        lengths.append(i)

    return lengths
def on_epoch_end(self, epoch, logs={})

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Arguments

epoch: Integer, index of epoch. logs: Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_.

Expand source code
def on_epoch_end(self, epoch, logs={}):
    label_true = []
    label_pred = []
    for i in range(len(self.seq)):
        x_true, y_true = self.seq[i]
        lengths = self.get_lengths(y_true)
        y_pred = self.model.predict_on_batch(x_true)

        y_true = self.p.inverse_transform(y_true, lengths)
        y_pred = self.p.inverse_transform(y_pred, lengths)

        label_true.extend(y_true)
        label_pred.extend(y_pred)

    score = metrics.f1_score(label_true, label_pred)
    print(' - f1: {:04.2f}'.format(score * 100))
    print(metrics.classification_report(label_true, label_pred))
    logs['f1'] = score