라이브러리/Tensorflow keras
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TF Keras 학습 속도 줄이기 (30%)라이브러리/Tensorflow keras 2022. 4. 26. 10:15
만약 최신 GPU로 TF Keras를 이용하고 있다면, 몇 줄만 추가해서 30% 정도 학습속도를 줄일 수 있다. from tensorflow.keras import mixed_precision policy = mixed_precision.Policy('mixed_float16') mixed_precision.set_global_policy(policy) *주의: TF에선 마지막 레이어가 softmax (sigmoid인 경우도)라면 stability를 위해 float32로 바꾸는 걸 추천함! output_layer = tf.keras.layers.Activation('sigmoid', dtype='float32')(output_layer) 참고 https://www.kaggle.com/competition..
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tf.data, TFRecord라이브러리/Tensorflow keras 2022. 1. 5. 00:32
tf.data https://www.tensorflow.org/guide/data The tf.data API enables you to build complex input pipelines from simple, reusable pieces. For example, the pipeline for an image model might aggregate data from files in a distributed file system, apply random perturbations to each image, and merge randomly selected images into a batch for training. The pipeline for a text model might involve extrac..
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Model vs Estimator라이브러리/Tensorflow keras 2022. 1. 3. 17:01
Background The Estimators API was added to Tensorflow in Release 1.1, and provides a high-level abstraction over lower-level Tensorflow core operations. It works with an Estimator instance, which is TensorFlow's high-level representation of a complete model. Keras is similar to the Estimators API in that it abstracts deep learning model components such as layers, activation functions and optimiz..