def test_efficientnetb0_model(self):
model = image_classifier.create(self.data,
mef.ModelExportFormat.TFLITE,
model_spec.efficientnet_b0_spec,
epochs=5,
batch_size=4,
shuffle=True)
self._test_accuracy(model)
self._test_export_to_tflite(model)
def test_resnet_50_model(self):
model = image_classifier.create(self.train_data,
mef.ModelExportFormat.TFLITE,
model_spec.resnet_50_spec,
epochs=2,
batch_size=4,
shuffle=True)
self._test_accuracy(model)
self._test_export_to_tflite(model)
def test_mobilenetv2_model(self):
model = image_classifier.create(self.data,
mef.ModelExportFormat.TFLITE,
model_spec.mobilenet_v2_spec,
epochs=2,
batch_size=4,
shuffle=True)
self._test_accuracy(model)
self._test_export_to_tflite(model)
self._test_predict_topk(model)
def main(_):
logging.set_verbosity(logging.INFO)
image_path = tf.keras.utils.get_file(
'flower_photos',
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
data = ImageClassifierDataLoader.from_folder(image_path)
model = image_classifier.create(
data,
model_export_format=ModelExportFormat.TFLITE,
model_spec=efficientnet_b0_spec)
_, acc = model.evaluate()
print('Test accuracy: %f' % acc)
model.export(FLAGS.tflite_filename, FLAGS.label_filename)
#
# Example taken from https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_customization/demo/image_classification.ipynb
#
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import tensorflow as tf
assert tf.__version__.startswith('2')
from tensorflow_examples.lite.model_customization.core.data_util.image_dataloader import ImageClassifierDataLoader
from tensorflow_examples.lite.model_customization.core.task import image_classifier
from tensorflow_examples.lite.model_customization.core.task.model_spec import efficientnet_b0_spec
from tensorflow_examples.lite.model_customization.core.task.model_spec import ImageModelSpec
import matplotlib.pyplot as plt
#image_path = tf.keras.utils.get_file('dogs', 'images/dogs.tgz', untar=True)
data = ImageClassifierDataLoader.from_folder("images")
model = image_classifier.create(data)
loss, accuracy = model.evaluate()
model.export('dogs_classifier.tflite', 'dog_labels.txt')
def test_mobilenetv2_model_create_v1_incompatible(self):
with self.assertRaisesRegex(ValueError, 'Incompatible versions'):
_ = image_classifier.create(self.train_data, mef.ModelExportFormat.TFLITE,
model_spec.mobilenet_v2_spec)
