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
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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