# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
import tensorflow as tf
from nlp_architect.utils.metrics import get_conll_scores
[docs]class ConllCallback(tf.keras.callbacks.Callback):
"""
A Tensorflow(Keras) Conlleval evaluator.
Runs the conlleval script for given x and y inputs.
Prints Conlleval F1 score on the end of each epoch.
Args:
x: features matrix
y: labels matrix
y_vocab (dict): int-to-str labels lexicon
batch_size (:obj:`int`, optional): batch size
"""
def __init__(self, x, y, y_vocab, batch_size=1):
super(ConllCallback, self).__init__()
self.x = x
self.y = y
self.y_vocab = {v: k for k, v in y_vocab.items()}
self.bsz = batch_size
[docs] def on_epoch_end(self, epoch, logs=None):
predictions = self.model.predict(self.x, batch_size=self.bsz)
stats = get_conll_scores(predictions, self.y, self.y_vocab)
print()
print("Conll eval: \n{}".format(stats))