Random Search in TensorFlow

random-tf.py

import kerastuner as kt
import tensorflow as tf

def model_builder(hp):
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
  
  # Tune the number of units in the first Dense layer
  # Choose an optimal value between 32-512
  hp_units = hp.Int('units', min_value = 32, max_value = 512, step = 32)
  model.add(tf.keras.layers.Dense(units = hp_units, activation = 'relu'))
  model.add(tf.keras.layers.Dense(10))

  # Tune the learning rate for the optimizer 
  # Choose an optimal value from 0.01, 0.001, or 0.0001
  hp_learning_rate = hp.Choice('learning_rate', values = [1e-2, 1e-3, 1e-4]) 
  
  model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = hp_learning_rate),
                loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True), 
                metrics = ['accuracy'])
  
  return model
  
tuner = kt.RandomSearch(model_builder,
                        objective = 'val_accuracy', 
                        max_trials = 10,
                        directory = 'random_search_starter',
                        project_name = 'intro_to_kt') 
                     
tuner.search(img_train, label_train, epochs = 10, validation_data = (img_test, label_test))

# Which was the best model?
best_model = tuner.get_best_models(1)[0]

# What were the best hyperparameters?
best_hyperparameters = tuner.get_best_hyperparameters(1)[0]