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]Optimizerで更新する変数を指定する方法 - Qiita
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 +y_ f = t*t gx, gb, gw, gp, gy, gy_,gs, gt, gf = tf.gradients(f, [x, b, w, p, y, y_,s, t, f]) init = tf.initialize_all_variables() opt = tf.train.
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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?
