Building powerful image classification models using very little data
Note: this post was originally written in June 2016. It is now very outdated. Please see this guide to fine-tuning for an up-to-date alternative, or check out chapter 8 of my book "Deep Learning with Python (2nd edition)". In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hu
Keras documentation: Transfer learning & fine-tuning
Transfer learning & fine-tuning Author: fchollet Date created: 2020/04/15 Last modified: 2023/06/25 Description: Complete guide to transfer learning & fine-tuning in Keras. View in Colab • GitHub source Setup import numpy as np import keras from keras import layers import tensorflow_datasets as tfds import matplotlib.pyplot as plt Introduction Transfer learning consists of taking features learned
Online/Incremental Learning with Keras and Creme - PyImageSearch
PyImageSearch You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch In this tutorial, you will learn how to perform online/incremental learning with Keras and Creme on datasets too large to fit into memory. A few weeks ago I showed you how to use Keras for feature extraction and online learning — we used that tutorial to perform transfer learning and recognize classes the origi
FAQ - Keras Documentation
Keras FAQ: Kerasに関するよくある質問 Kerasを引用するには？ KerasをGPUで動かすには？ KerasをマルチGPUで動かすには？ "sample","batch"，"epoch" の意味は？ Keras modelを保存するには？ training lossがtesting lossよりもはるかに大きいのはなぜ？ 中間レイヤーの出力を得るには？ メモリに載らない大きさのデータを扱うには？ validation lossが減らなくなったときに学習を中断するには？ validation splitはどのように実行されますか？ 訓練時にデータはシャッフルされますか？ 各epochのtraining/validationのlossやaccuracyを記録するには？ レイヤーを "freeze" するには？ stateful RNNを利用するには？ Sequentialモデ
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