def main(_):
logging.set_verbosity(logging.INFO)
model_spec = AverageWordVecModelSpec()
data_path = tf.keras.utils.get_file(
fname='aclImdb',
origin='http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz',
untar=True)
train_data = TextClassifierDataLoader.from_folder(
filename=os.path.join(os.path.join(data_path, 'train')),
model_spec=model_spec,
class_labels=['pos', 'neg'])
train_data, validation_data = train_data.split(0.9)
test_data = TextClassifierDataLoader.from_folder(filename=os.path.join(
data_path, 'test'),
model_spec=model_spec,
is_training=False)
model = text_classifier.create(train_data,
model_spec=model_spec,
validation_data=validation_data)
_, acc = model.evaluate(test_data)
print('\nTest accuracy: %f' % acc)
model.export(FLAGS.tflite_filename, FLAGS.label_filename,
FLAGS.vocab_filename)
def test_average_wordvec_model_create_v1_incompatible(self):
with self.assertRaisesRegex(ValueError, 'Incompatible versions'):
model_spec = ms.AverageWordVecModelSpec(seq_len=2)
all_data = text_dataloader.TextClassifierDataLoader.from_folder(
self.text_dir, model_spec=model_spec)
_ = text_classifier.create(
all_data,
mef.ModelExportFormat.TFLITE,
model_spec=model_spec,
)
def test_average_wordvec_model(self):
model = text_classifier.create(self.data,
mef.ModelExportFormat.TFLITE,
model_name='average_wordvec',
epochs=2,
batch_size=4,
sentence_len=2,
shuffle=True)
self._test_accuracy(model)
self._test_export_to_tflite(model)
def test_average_wordvec_model(self):
model = text_classifier.create(
self.train_data,
mef.ModelExportFormat.TFLITE,
model_spec=ms.AverageWordVecModelSpec(sentence_len=2),
epochs=2,
batch_size=4,
shuffle=True)
self._test_accuracy(model)
self._test_export_to_tflite(model)
self._test_predict_top_k(model)
def test_average_wordvec_model(self):
model_spec = ms.AverageWordVecModelSpec(seq_len=2)
all_data = text_dataloader.TextClassifierDataLoader.from_folder(
self.text_dir, model_spec=model_spec)
# Splits data, 90% data for training, 10% for testing
self.train_data, self.test_data = all_data.split(0.9)
model = text_classifier.create(self.train_data,
mef.ModelExportFormat.TFLITE,
model_spec=model_spec,
epochs=2,
batch_size=4,
shuffle=True)
self._test_accuracy(model)
self._test_export_to_tflite(model)
self._test_predict_top_k(model)
def main(_):
logging.set_verbosity(logging.INFO)
data_path = tf.keras.utils.get_file(
fname='aclImdb',
origin='http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz',
untar=True)
train_data = TextClassifierDataLoader.from_folder(
filename=os.path.join(os.path.join(data_path, 'train')),
class_labels=['pos', 'neg'])
test_data = TextClassifierDataLoader.from_folder(
filename=os.path.join(data_path, 'test'))
model = text_classifier.create(
train_data, model_export_format=ModelExportFormat.TFLITE)
_, acc = model.evaluate(test_data)
print('Test accuracy: %f' % acc)
model.export(FLAGS.tflite_filename, FLAGS.label_filename,
FLAGS.vocab_filename)