Keras documentation: KerasTuner
KerasTuner Star KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algori
[TF]Optimizerã§æ›´æ–°ã™ã‚‹å¤‰æ•°ã‚’指定ã™ã‚‹æ–¹æ³• - Qiita
２番ã®æ–¹ãŒã‚³ãƒ¼ãƒ‰ã®å¤‰æ›´ãŒæ¥½ã§ã™ã€‚ 実行çµæžœ ãªã‚“ã«ã‚‚ã—ãªã„時 下記ã®ã‚ˆã†ãªã‚³ãƒ¼ãƒ‰ã‚’実行ã™ã‚‹ã¨å¤‰æ•°w, b, x, y_ãŒæ›´æ–°ã•ã‚Œã¦ã—ã¾ã„ã¾ã™ã€‚ （下記ã®ã‚³ãƒ¼ãƒ‰è‡ªä½“実用性ã¯æ®†ã©ç„¡ã„ã§ã™ã—ã€é€šå¸¸ã¯x, y_ã‚’placeholderã«ã™ã‚‹ã®ã§å•é¡Œã«ã¯ãªã‚Šã¾ã›ã‚“ãŒã€ä¾‹ã¨ã„ã†ã“ã¨ã§ã”容赦ãã ã•ã„。） import tensorflow as tf import numpy as np w = tf.Variable(tf.constant([3.]), name='w') b = tf.Variable(tf.constant([1.]), name='b') x = tf.Variable(tf.constant([2.]), name='x') y_ = tf.Variable(tf.constant([5.]), name='y_') p = w*x y = p+b s = -y t = s
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GitHub - kubeflow/kubeflow: Machine Learning Toolkit for Kubernetes
https://learningtensorflow.com/lesson7/
Simple example of gradient descent in tensorflow
Simple way to visualize a TensorFlow graph in Jupyter?
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