Introduction to the Keras Tuner

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Overview

The Keras Tuner is a library that helps you pick the optimal set of hyperparameters for your TensorFlow program. The process of selecting the right set of hyperparameters for your machine learning (ML) application is called hyperparameter tuning or hypertuning.

Hyperparameters are the variables that govern the training process and the topology of an ML model. These variables remain constant over the training process and directly impact the performance of your ML program. Hyperparameters are of two types:

  1. Model hyperparameters which influence model selection such as the number and width of hidden layers
  2. Algorithm hyperparameters which influence the speed and quality of the learning algorithm such as the learning rate for Stochastic Gradient Descent (SGD) and the number of nearest neighbors for a k Nearest Neighbors (KNN) classifier

In this tutorial, you will use the Keras Tuner to perform hypertuning for an image classification application.

Setup

import tensorflow as tf
from tensorflow import keras
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2023-12-07 03:19:58.479624: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-12-07 03:19:58.481203: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

Install and import the Keras Tuner.

pip install -q -U keras-tuner
import keras_tuner as kt

Download and prepare the dataset

In this tutorial, you will use the Keras Tuner to find the best hyperparameters for a machine learning model that classifies images of clothing from the Fashion MNIST dataset.

Load the data.

(img_train, label_train), (img_test, label_test) = keras.datasets.fashion_mnist.load_data()
# Normalize pixel values between 0 and 1
img_train = img_train.astype('float32') / 255.0
img_test = img_test.astype('float32') / 255.0

Define the model

When you build a model for hypertuning, you also define the hyperparameter search space in addition to the model architecture. The model you set up for hypertuning is called a hypermodel.

You can define a hypermodel through two approaches:

  • By using a model builder function
  • By subclassing the HyperModel class of the Keras Tuner API

You can also use two pre-defined HyperModel classes - HyperXception and HyperResNet for computer vision applications.

In this tutorial, you use a model builder function to define the image classification model. The model builder function returns a compiled model and uses hyperparameters you define inline to hypertune the model.

def model_builder(hp):
  model = keras.Sequential()
  model.add(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(keras.layers.Dense(units=hp_units, activation='relu'))
  model.add(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=keras.optimizers.Adam(learning_rate=hp_learning_rate),
                loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])

  return model

Instantiate the tuner and perform hypertuning

Instantiate the tuner to perform the hypertuning. The Keras Tuner has four tuners available - RandomSearch, Hyperband, BayesianOptimization, and Sklearn. In this tutorial, you use the Hyperband tuner.

To instantiate the Hyperband tuner, you must specify the hypermodel, the objective to optimize and the maximum number of epochs to train (max_epochs).

tuner = kt.Hyperband(model_builder,
                     objective='val_accuracy',
                     max_epochs=10,
                     factor=3,
                     directory='my_dir',
                     project_name='intro_to_kt')

The Hyperband tuning algorithm uses adaptive resource allocation and early-stopping to quickly converge on a high-performing model. This is done using a sports championship style bracket. The algorithm trains a large number of models for a few epochs and carries forward only the top-performing half of models to the next round. Hyperband determines the number of models to train in a bracket by computing 1 + logfactor(max_epochs) and rounding it up to the nearest integer.

Create a callback to stop training early after reaching a certain value for the validation loss.

stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)

Run the hyperparameter search. The arguments for the search method are the same as those used for tf.keras.model.fit in addition to the callback above.

tuner.search(img_train, label_train, epochs=50, validation_split=0.2, callbacks=[stop_early])

# Get the optimal hyperparameters
best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]

print(f"""
The hyperparameter search is complete. The optimal number of units in the first densely-connected
layer is {best_hps.get('units')} and the optimal learning rate for the optimizer
is {best_hps.get('learning_rate')}.
""")
Trial 30 Complete [00h 00m 41s]
val_accuracy: 0.8550833463668823

Best val_accuracy So Far: 0.8900833129882812
Total elapsed time: 00h 08m 43s

The hyperparameter search is complete. The optimal number of units in the first densely-connected
layer is 224 and the optimal learning rate for the optimizer
is 0.001.

Train the model

Find the optimal number of epochs to train the model with the hyperparameters obtained from the search.

# Build the model with the optimal hyperparameters and train it on the data for 50 epochs
model = tuner.hypermodel.build(best_hps)
history = model.fit(img_train, label_train, epochs=50, validation_split=0.2)

val_acc_per_epoch = history.history['val_accuracy']
best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1
print('Best epoch: %d' % (best_epoch,))
Epoch 1/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.5055 - accuracy: 0.8205 - val_loss: 0.4009 - val_accuracy: 0.8582
Epoch 2/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.3772 - accuracy: 0.8628 - val_loss: 0.3637 - val_accuracy: 0.8685
Epoch 3/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.3366 - accuracy: 0.8766 - val_loss: 0.3698 - val_accuracy: 0.8662
Epoch 4/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.3110 - accuracy: 0.8858 - val_loss: 0.3599 - val_accuracy: 0.8703
Epoch 5/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.2924 - accuracy: 0.8906 - val_loss: 0.3289 - val_accuracy: 0.8818
Epoch 6/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.2768 - accuracy: 0.8958 - val_loss: 0.3491 - val_accuracy: 0.8743
Epoch 7/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.2622 - accuracy: 0.9022 - val_loss: 0.3127 - val_accuracy: 0.8866
Epoch 8/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.2512 - accuracy: 0.9067 - val_loss: 0.3378 - val_accuracy: 0.8822
Epoch 9/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.2412 - accuracy: 0.9104 - val_loss: 0.3282 - val_accuracy: 0.8848
Epoch 10/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.2294 - accuracy: 0.9143 - val_loss: 0.3398 - val_accuracy: 0.8838
Epoch 11/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2217 - accuracy: 0.9166 - val_loss: 0.3158 - val_accuracy: 0.8897
Epoch 12/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2124 - accuracy: 0.9197 - val_loss: 0.3443 - val_accuracy: 0.8858
Epoch 13/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2051 - accuracy: 0.9226 - val_loss: 0.3649 - val_accuracy: 0.8854
Epoch 14/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1986 - accuracy: 0.9254 - val_loss: 0.3195 - val_accuracy: 0.8901
Epoch 15/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1908 - accuracy: 0.9287 - val_loss: 0.3173 - val_accuracy: 0.8971
Epoch 16/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1823 - accuracy: 0.9306 - val_loss: 0.3480 - val_accuracy: 0.8911
Epoch 17/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1803 - accuracy: 0.9314 - val_loss: 0.3258 - val_accuracy: 0.8929
Epoch 18/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1721 - accuracy: 0.9370 - val_loss: 0.3331 - val_accuracy: 0.8950
Epoch 19/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1676 - accuracy: 0.9383 - val_loss: 0.3331 - val_accuracy: 0.8962
Epoch 20/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1634 - accuracy: 0.9382 - val_loss: 0.3432 - val_accuracy: 0.8932
Epoch 21/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1566 - accuracy: 0.9405 - val_loss: 0.3597 - val_accuracy: 0.8873
Epoch 22/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1538 - accuracy: 0.9412 - val_loss: 0.3446 - val_accuracy: 0.8933
Epoch 23/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1493 - accuracy: 0.9435 - val_loss: 0.3677 - val_accuracy: 0.8888
Epoch 24/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1459 - accuracy: 0.9454 - val_loss: 0.3472 - val_accuracy: 0.8961
Epoch 25/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1400 - accuracy: 0.9469 - val_loss: 0.3984 - val_accuracy: 0.8827
Epoch 26/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1374 - accuracy: 0.9484 - val_loss: 0.3767 - val_accuracy: 0.8931
Epoch 27/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1323 - accuracy: 0.9491 - val_loss: 0.3849 - val_accuracy: 0.8909
Epoch 28/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1312 - accuracy: 0.9511 - val_loss: 0.3897 - val_accuracy: 0.8903
Epoch 29/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1242 - accuracy: 0.9533 - val_loss: 0.4042 - val_accuracy: 0.8907
Epoch 30/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1238 - accuracy: 0.9533 - val_loss: 0.3784 - val_accuracy: 0.8934
Epoch 31/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1176 - accuracy: 0.9554 - val_loss: 0.4152 - val_accuracy: 0.8940
Epoch 32/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1152 - accuracy: 0.9570 - val_loss: 0.4081 - val_accuracy: 0.8886
Epoch 33/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1123 - accuracy: 0.9578 - val_loss: 0.4372 - val_accuracy: 0.8856
Epoch 34/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1120 - accuracy: 0.9582 - val_loss: 0.4068 - val_accuracy: 0.8937
Epoch 35/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1073 - accuracy: 0.9607 - val_loss: 0.4246 - val_accuracy: 0.8943
Epoch 36/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1040 - accuracy: 0.9606 - val_loss: 0.4211 - val_accuracy: 0.8934
Epoch 37/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1034 - accuracy: 0.9613 - val_loss: 0.4291 - val_accuracy: 0.8933
Epoch 38/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0991 - accuracy: 0.9627 - val_loss: 0.4504 - val_accuracy: 0.8942
Epoch 39/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0977 - accuracy: 0.9635 - val_loss: 0.4331 - val_accuracy: 0.8950
Epoch 40/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0948 - accuracy: 0.9653 - val_loss: 0.4429 - val_accuracy: 0.8944
Epoch 41/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0939 - accuracy: 0.9643 - val_loss: 0.4727 - val_accuracy: 0.8888
Epoch 42/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0937 - accuracy: 0.9650 - val_loss: 0.4521 - val_accuracy: 0.8969
Epoch 43/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0888 - accuracy: 0.9673 - val_loss: 0.4801 - val_accuracy: 0.8908
Epoch 44/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0880 - accuracy: 0.9678 - val_loss: 0.4582 - val_accuracy: 0.8973
Epoch 45/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0878 - accuracy: 0.9668 - val_loss: 0.5006 - val_accuracy: 0.8920
Epoch 46/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0862 - accuracy: 0.9678 - val_loss: 0.4547 - val_accuracy: 0.8942
Epoch 47/50
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0836 - accuracy: 0.9680 - val_loss: 0.5050 - val_accuracy: 0.8908
Epoch 48/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0808 - accuracy: 0.9692 - val_loss: 0.4956 - val_accuracy: 0.8954
Epoch 49/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0803 - accuracy: 0.9696 - val_loss: 0.5260 - val_accuracy: 0.8928
Epoch 50/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0761 - accuracy: 0.9716 - val_loss: 0.5449 - val_accuracy: 0.8914
Best epoch: 44

Re-instantiate the hypermodel and train it with the optimal number of epochs from above.

hypermodel = tuner.hypermodel.build(best_hps)

# Retrain the model
hypermodel.fit(img_train, label_train, epochs=best_epoch, validation_split=0.2)
Epoch 1/44
1500/1500 [==============================] - 5s 3ms/step - loss: 0.5087 - accuracy: 0.8195 - val_loss: 0.4183 - val_accuracy: 0.8519
Epoch 2/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.3767 - accuracy: 0.8639 - val_loss: 0.3740 - val_accuracy: 0.8653
Epoch 3/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.3355 - accuracy: 0.8771 - val_loss: 0.3642 - val_accuracy: 0.8691
Epoch 4/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.3109 - accuracy: 0.8860 - val_loss: 0.3444 - val_accuracy: 0.8782
Epoch 5/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2908 - accuracy: 0.8918 - val_loss: 0.3312 - val_accuracy: 0.8801
Epoch 6/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.2757 - accuracy: 0.8969 - val_loss: 0.3437 - val_accuracy: 0.8782
Epoch 7/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2617 - accuracy: 0.9030 - val_loss: 0.3414 - val_accuracy: 0.8788
Epoch 8/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2504 - accuracy: 0.9062 - val_loss: 0.3221 - val_accuracy: 0.8827
Epoch 9/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2389 - accuracy: 0.9105 - val_loss: 0.3210 - val_accuracy: 0.8858
Epoch 10/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2310 - accuracy: 0.9140 - val_loss: 0.3371 - val_accuracy: 0.8807
Epoch 11/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2208 - accuracy: 0.9172 - val_loss: 0.3135 - val_accuracy: 0.8898
Epoch 12/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2143 - accuracy: 0.9191 - val_loss: 0.3253 - val_accuracy: 0.8863
Epoch 13/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.2049 - accuracy: 0.9233 - val_loss: 0.3268 - val_accuracy: 0.8873
Epoch 14/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1993 - accuracy: 0.9259 - val_loss: 0.3168 - val_accuracy: 0.8919
Epoch 15/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1927 - accuracy: 0.9273 - val_loss: 0.3196 - val_accuracy: 0.8913
Epoch 16/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1832 - accuracy: 0.9316 - val_loss: 0.3353 - val_accuracy: 0.8911
Epoch 17/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1791 - accuracy: 0.9338 - val_loss: 0.3295 - val_accuracy: 0.8903
Epoch 18/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1728 - accuracy: 0.9364 - val_loss: 0.3304 - val_accuracy: 0.8913
Epoch 19/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1672 - accuracy: 0.9370 - val_loss: 0.3382 - val_accuracy: 0.8899
Epoch 20/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1605 - accuracy: 0.9400 - val_loss: 0.3623 - val_accuracy: 0.8908
Epoch 21/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1593 - accuracy: 0.9403 - val_loss: 0.3704 - val_accuracy: 0.8881
Epoch 22/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1519 - accuracy: 0.9430 - val_loss: 0.3617 - val_accuracy: 0.8940
Epoch 23/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1512 - accuracy: 0.9441 - val_loss: 0.3308 - val_accuracy: 0.8947
Epoch 24/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1439 - accuracy: 0.9460 - val_loss: 0.3594 - val_accuracy: 0.8918
Epoch 25/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1391 - accuracy: 0.9475 - val_loss: 0.3781 - val_accuracy: 0.8931
Epoch 26/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1359 - accuracy: 0.9493 - val_loss: 0.3700 - val_accuracy: 0.8934
Epoch 27/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1331 - accuracy: 0.9505 - val_loss: 0.3792 - val_accuracy: 0.8917
Epoch 28/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1299 - accuracy: 0.9519 - val_loss: 0.3837 - val_accuracy: 0.8921
Epoch 29/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1264 - accuracy: 0.9532 - val_loss: 0.4043 - val_accuracy: 0.8850
Epoch 30/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1238 - accuracy: 0.9534 - val_loss: 0.3983 - val_accuracy: 0.8917
Epoch 31/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1175 - accuracy: 0.9572 - val_loss: 0.4241 - val_accuracy: 0.8884
Epoch 32/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1151 - accuracy: 0.9563 - val_loss: 0.4080 - val_accuracy: 0.8930
Epoch 33/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1150 - accuracy: 0.9566 - val_loss: 0.4250 - val_accuracy: 0.8902
Epoch 34/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1120 - accuracy: 0.9588 - val_loss: 0.4427 - val_accuracy: 0.8895
Epoch 35/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1073 - accuracy: 0.9603 - val_loss: 0.4317 - val_accuracy: 0.8926
Epoch 36/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1057 - accuracy: 0.9610 - val_loss: 0.4138 - val_accuracy: 0.8936
Epoch 37/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.1039 - accuracy: 0.9616 - val_loss: 0.4180 - val_accuracy: 0.8917
Epoch 38/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.1007 - accuracy: 0.9625 - val_loss: 0.4698 - val_accuracy: 0.8807
Epoch 39/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0989 - accuracy: 0.9632 - val_loss: 0.4371 - val_accuracy: 0.8920
Epoch 40/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0954 - accuracy: 0.9644 - val_loss: 0.4582 - val_accuracy: 0.8933
Epoch 41/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0944 - accuracy: 0.9648 - val_loss: 0.5068 - val_accuracy: 0.8855
Epoch 42/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0909 - accuracy: 0.9663 - val_loss: 0.5006 - val_accuracy: 0.8864
Epoch 43/44
1500/1500 [==============================] - 4s 2ms/step - loss: 0.0922 - accuracy: 0.9653 - val_loss: 0.4598 - val_accuracy: 0.8942
Epoch 44/44
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0860 - accuracy: 0.9673 - val_loss: 0.4797 - val_accuracy: 0.8913
<keras.src.callbacks.History at 0x7f6b341323a0>

To finish this tutorial, evaluate the hypermodel on the test data.

eval_result = hypermodel.evaluate(img_test, label_test)
print("[test loss, test accuracy]:", eval_result)
313/313 [==============================] - 1s 2ms/step - loss: 0.5223 - accuracy: 0.8872
[test loss, test accuracy]: [0.5223038792610168, 0.8871999979019165]

The my_dir/intro_to_kt directory contains detailed logs and checkpoints for every trial (model configuration) run during the hyperparameter search. If you re-run the hyperparameter search, the Keras Tuner uses the existing state from these logs to resume the search. To disable this behavior, pass an additional overwrite=True argument while instantiating the tuner.

Summary

In this tutorial, you learned how to use the Keras Tuner to tune hyperparameters for a model. To learn more about the Keras Tuner, check out these additional resources:

Also check out the HParams Dashboard in TensorBoard to interactively tune your model hyperparameters.