ae46f6e2086e76b566ad23fd6099e84d96a3575f,anago/callbacks.py,F1score,on_epoch_end,#F1score#Any#Any#,58

Before Change

            y_pred = self.model.predict_on_batch(data)
            y_pred = np.argmax(y_pred, -1)

            y_pred = [self.p.inverse_transform(y[:l]) for y, l in zip(y_pred, sequence_lengths)]
            y_true = [self.p.inverse_transform(y[:l]) for y, l in zip(y_true, sequence_lengths)]

After Change

        self.p = preprocessor

    def on_epoch_end(self, epoch, logs={}):
        label_true = []
        label_pred = []
        for i, (data, label) in enumerate(self.valid_batches):
            if i == self.valid_steps:
                break
            y_true = label
            y_true = np.argmax(y_true, -1)
            sequence_lengths = data[-1]  // shape of (batch_size, 1)
            sequence_lengths = np.reshape(sequence_lengths, (-1,))
            y_pred = self.model.predict_on_batch(data)
            y_pred = np.argmax(y_pred, -1)

            y_true = self.p(y_true)
            y_pred = self.p(y_pred)

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

        score = f1_score(label_true, label_pred)
        print(" - f1: {:04.2f}".format(score * 100))

Instances