[GDG DevFest Seoul 2018] Browsing Modern TensorFlow

Transcript

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12. 2016 2017 ⏤ TensorFlow Serving ⏤ Keras API ⏤ Eager

    Execution ⏤ TensorFlow Lite ⏤ XLA ⏤ OpenCL w/ OpenCompute ⏤ Distributed TensorFlow ⏤ Multi GPU support ⏤ Mobile TensorFlow ⏤ TensorFlow Datasets ⏤ SKLearn (contrib) ⏤ TensorFlow Slim ⏤ SyntaxNet ⏤ DRAGNN ⏤ TFLearn (contrib) ⏤ TensorFlow TimeSeries 2018 ⏤ TensorFlow Hub ⏤ Swift for TensorFlow ⏤ TensorFlow Models ⏤ ROCm TensorFlow (Test) 5FOTPS'MPX
    펻칺 ⏤ TensorRT integration

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16. TensorFlow Serving § 옮 컪찒큲 엖핒풚 § 뻚 § Servables

    § Loaders § Managers § 훊푢 믾쁳 § Model building § Model versioning § Model saving / loading § Online inference support with RPC

17. Keras-compatible API for TensorFlow § Keras ( https://keras.io ) §

    High-level API — Focus on user experience § “Deep learning accessible to everyone” § 펻칺 § 2017뼒 2풢 짪 § TensorFlow 1.2펞컪 contributor 힎옪  § TensorFlow 1.4쭎 헣킫 힎옪 킇멷 § 힣 § “Simplified workflow for TensorFlow users, more powerful features to Keras users” § 샎쭎쭒픦 Keras 슪많 믆샎옪  (keras. ̔ tf.keras.) § TensorFlow 슪퐎 Keras 슪읊 컬펂컪 칺푷 많쁳 § tf.keras. 쁢 2.0쭎 팒폖 UG많 쇮 폖헣

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23. files = tf.data.Dataset.list_files(file_pattern) dataset = tf.data.TFRecordDataset(files) dataset = dataset.shuffle(10000) dataset

    = dataset.repeat(NUM_EPOCHS) dataset = dataset.map(lambda x: tf.parse_single_example(x, features)) dataset = dataset.batch(BATCH_SIZE) iterator = dataset.make_one_shot_iterator() features = iterator.get_next() E T L

24. files = tf.data.Dataset.list_files(file_pattern) dataset = tf.data.TFRecordDataset(files) dataset = dataset.shuffle(10000) dataset

    = dataset.repeat(NUM_EPOCHS) dataset = dataset.map(lambda x: tf.parse_single_example(x, features)) dataset = dataset.batch(BATCH_SIZE) iterator = dataset.make_one_shot_iterator() features = iterator.get_next()

25. files = tf.data.Dataset.list_files(file_pattern) dataset = tf.data.TFRecordDataset(files, num_parallel_reads=32) dataset = dataset.shuffle(10000)

    dataset = dataset.repeat(NUM_EPOCHS) dataset = dataset.map(lambda x: tf.parse_single_example(x, features)) dataset = dataset.batch(BATCH_SIZE) iterator = dataset.make_one_shot_iterator() features = iterator.get_next()

26. files = tf.data.Dataset.list_files(file_pattern) dataset = tf.data.TFRecordDataset(files, num_parallel_reads=32) dataset = dataset.apply(

    tf.contrib.data.shuffle_and_repeat(10000, NUM_EPOCHS)) dataset = dataset.apply( tf.contrib.data.map_and_batch(lambda x: ..., BATCH_SIZE)) iterator = dataset.make_one_shot_iterator() features = iterator.get_next()

27. files = tf.data.Dataset.list_files(file_pattern) dataset = tf.data.TFRecordDataset(files, num_parallel_reads=32) dataset = dataset.apply(

    tf.contrib.data.shuffle_and_repeat(10000, NUM_EPOCHS)) dataset = dataset.apply( tf.contrib.data.map_and_batch(lambda x: ..., BATCH_SIZE)) dataset = dataset.apply(tf.contrib.data.prefetch_to_device("/gpu:0")) iterator = dataset.make_one_shot_iterator() features = iterator.get_next()

28. files = tf.data.Dataset.list_files(file_pattern) dataset = tf.data.TFRecordDataset(files, num_parallel_reads=32) dataset = dataset.apply(

    tf.contrib.data.shuffle_and_repeat(10000, NUM_EPOCHS)) dataset = dataset.apply( tf.contrib.data.map_and_batch(lambda x: ..., BATCH_SIZE)) dataset = dataset.apply(tf.contrib.data.prefetch_to_device("/gpu:0")) iterator = dataset.make_one_shot_iterator() features = iterator.get_next() Check out Brennan’s talk to learn more! https://www.youtube.com/watch?v=SxOsJPaxHME

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32. 젆킮얺삫픦 푢콚 섾핂 팚몮읺흦 헒줆힎킫 펾칾

33. 젆킮얺삫 뫃퓮 Module TensorFlow Hub Model

34. 졶슖픎 짆읺 엶쇪 많훟 짝 믆앦읊 샂몮 핖픚

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36. tensorflow.org/hub

37. # Download and use NASNet feature vector module. module =

    hub.Module( "https://tfhub.dev/google/imagenet/nasnet_large/feature_vector/1") features = module(my_images) logits = tf.layers.dense(features, NUM_CLASSES) probabilities = tf.nn.softmax(logits) 62,000+ GPU 시간! 젆킮얺삫 뫃퓮
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38. # Download and use NASNet feature vector module. module =

    hub.Module( "https://tfhub.dev/google/imagenet/nasnet_large/feature_vector/1", trainable=True, tags={“train”}) features = module(my_images) logits = tf.layers.dense(features, NUM_CLASSES) probabilities = tf.nn.softmax(logits)

39. # Use pre-trained universal sentence encoder to build text vector

    column. review = hub.text_embedding_column( "review", "https://tfhub.dev/google/universal-sentence-encoder/1") features = { "review": np.array(["an arugula masterpiece", "inedible shoe leather", ...]) } labels = np.array([[1], [0], ...]) input_fn = tf.estimator.input.numpy_input_fn(features, labels, shuffle=True) estimator = tf.estimator.DNNClassifier(hidden_units, [review]) estimator.train(input_fn, max_steps=100) 젆킮얺삫 뫃퓮
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40. # Use pre-trained universal sentence encoder to build text vector

    column. review = hub.text_embedding_column( "review", "https://tfhub.dev/google/universal-sentence-encoder/1", trainable=True) features = { "review": np.array(["an arugula masterpiece", "inedible shoe leather", ...]) } labels = np.array([[1], [0], ...]) input_fn = tf.estimator.input.numpy_input_fn(features, labels, shuffle=True) estimator = tf.estimator.DNNClassifier(hidden_units, [review]) estimator.train(input_fn, max_steps=100) 젆킮얺삫 뫃퓮
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41. # Use pre-trained universal sentence encoder to build text vector

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