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
2023-09-28 07:01:16.582823: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-09-28 07:01:16.582867: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-09-28 07:01:16.582907: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] 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
Using TensorFlow backend

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')
2023-09-28 07:01:23.760824: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2211] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...

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 43s]
val_accuracy: 0.878250002861023

Best val_accuracy So Far: 0.890916645526886
Total elapsed time: 00h 09m 27s

The hyperparameter search is complete. The optimal number of units in the first densely-connected
layer is 416 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: 0.5015 - accuracy: 0.8232 - val_loss: 0.4154 - val_accuracy: 0.8482
Epoch 2/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.3713 - accuracy: 0.8648 - val_loss: 0.3429 - val_accuracy: 0.8768
Epoch 3/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.3324 - accuracy: 0.8776 - val_loss: 0.3480 - val_accuracy: 0.8764
Epoch 4/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.3053 - accuracy: 0.8878 - val_loss: 0.3395 - val_accuracy: 0.8793
Epoch 5/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2845 - accuracy: 0.8960 - val_loss: 0.3207 - val_accuracy: 0.8854
Epoch 6/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2711 - accuracy: 0.8991 - val_loss: 0.3303 - val_accuracy: 0.8804
Epoch 7/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2552 - accuracy: 0.9057 - val_loss: 0.3281 - val_accuracy: 0.8837
Epoch 8/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2440 - accuracy: 0.9091 - val_loss: 0.3024 - val_accuracy: 0.8920
Epoch 9/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2353 - accuracy: 0.9120 - val_loss: 0.3106 - val_accuracy: 0.8926
Epoch 10/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2265 - accuracy: 0.9150 - val_loss: 0.3229 - val_accuracy: 0.8887
Epoch 11/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2169 - accuracy: 0.9181 - val_loss: 0.3253 - val_accuracy: 0.8820
Epoch 12/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2073 - accuracy: 0.9210 - val_loss: 0.3306 - val_accuracy: 0.8910
Epoch 13/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2013 - accuracy: 0.9251 - val_loss: 0.3131 - val_accuracy: 0.8945
Epoch 14/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1927 - accuracy: 0.9263 - val_loss: 0.3253 - val_accuracy: 0.8910
Epoch 15/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1854 - accuracy: 0.9307 - val_loss: 0.3469 - val_accuracy: 0.8848
Epoch 16/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1787 - accuracy: 0.9333 - val_loss: 0.3410 - val_accuracy: 0.8910
Epoch 17/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1753 - accuracy: 0.9340 - val_loss: 0.3412 - val_accuracy: 0.8932
Epoch 18/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1676 - accuracy: 0.9358 - val_loss: 0.3349 - val_accuracy: 0.8967
Epoch 19/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1604 - accuracy: 0.9406 - val_loss: 0.3527 - val_accuracy: 0.8890
Epoch 20/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1590 - accuracy: 0.9406 - val_loss: 0.3512 - val_accuracy: 0.8924
Epoch 21/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1512 - accuracy: 0.9430 - val_loss: 0.3380 - val_accuracy: 0.8944
Epoch 22/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1464 - accuracy: 0.9441 - val_loss: 0.3741 - val_accuracy: 0.8950
Epoch 23/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1441 - accuracy: 0.9461 - val_loss: 0.3800 - val_accuracy: 0.8921
Epoch 24/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1366 - accuracy: 0.9485 - val_loss: 0.4073 - val_accuracy: 0.8826
Epoch 25/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1377 - accuracy: 0.9471 - val_loss: 0.4091 - val_accuracy: 0.8846
Epoch 26/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1329 - accuracy: 0.9501 - val_loss: 0.3760 - val_accuracy: 0.8923
Epoch 27/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1270 - accuracy: 0.9519 - val_loss: 0.3927 - val_accuracy: 0.8915
Epoch 28/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1247 - accuracy: 0.9528 - val_loss: 0.3965 - val_accuracy: 0.8917
Epoch 29/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1210 - accuracy: 0.9548 - val_loss: 0.4010 - val_accuracy: 0.8926
Epoch 30/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1172 - accuracy: 0.9565 - val_loss: 0.3914 - val_accuracy: 0.8957
Epoch 31/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1165 - accuracy: 0.9562 - val_loss: 0.4254 - val_accuracy: 0.8948
Epoch 32/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1113 - accuracy: 0.9590 - val_loss: 0.4485 - val_accuracy: 0.8901
Epoch 33/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1098 - accuracy: 0.9587 - val_loss: 0.4156 - val_accuracy: 0.8934
Epoch 34/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1058 - accuracy: 0.9609 - val_loss: 0.4374 - val_accuracy: 0.8932
Epoch 35/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1028 - accuracy: 0.9624 - val_loss: 0.4520 - val_accuracy: 0.8925
Epoch 36/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0995 - accuracy: 0.9631 - val_loss: 0.4732 - val_accuracy: 0.8913
Epoch 37/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0987 - accuracy: 0.9625 - val_loss: 0.4268 - val_accuracy: 0.8972
Epoch 38/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0952 - accuracy: 0.9644 - val_loss: 0.4506 - val_accuracy: 0.8953
Epoch 39/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0930 - accuracy: 0.9656 - val_loss: 0.4726 - val_accuracy: 0.8932
Epoch 40/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0899 - accuracy: 0.9650 - val_loss: 0.4744 - val_accuracy: 0.8917
Epoch 41/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0887 - accuracy: 0.9671 - val_loss: 0.5256 - val_accuracy: 0.8913
Epoch 42/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0883 - accuracy: 0.9670 - val_loss: 0.4868 - val_accuracy: 0.8953
Epoch 43/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0848 - accuracy: 0.9689 - val_loss: 0.4819 - val_accuracy: 0.8953
Epoch 44/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0870 - accuracy: 0.9672 - val_loss: 0.5638 - val_accuracy: 0.8848
Epoch 45/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0817 - accuracy: 0.9698 - val_loss: 0.5141 - val_accuracy: 0.8905
Epoch 46/50
1500/1500 [==============================] - 4s 3ms/step - loss: 0.0794 - accuracy: 0.9704 - val_loss: 0.5411 - val_accuracy: 0.8902
Epoch 47/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0828 - accuracy: 0.9694 - val_loss: 0.5208 - val_accuracy: 0.8920
Epoch 48/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0763 - accuracy: 0.9707 - val_loss: 0.5354 - val_accuracy: 0.8883
Epoch 49/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0749 - accuracy: 0.9717 - val_loss: 0.5483 - val_accuracy: 0.8907
Epoch 50/50
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0744 - accuracy: 0.9725 - val_loss: 0.5422 - val_accuracy: 0.8936
Best epoch: 37

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 1/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.4886 - accuracy: 0.8290 - val_loss: 0.4179 - val_accuracy: 0.8546
Epoch 2/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.3672 - accuracy: 0.8651 - val_loss: 0.3462 - val_accuracy: 0.8750
Epoch 3/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.3309 - accuracy: 0.8789 - val_loss: 0.3543 - val_accuracy: 0.8720
Epoch 4/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.3057 - accuracy: 0.8861 - val_loss: 0.3413 - val_accuracy: 0.8767
Epoch 5/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2852 - accuracy: 0.8934 - val_loss: 0.3232 - val_accuracy: 0.8855
Epoch 6/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2694 - accuracy: 0.8992 - val_loss: 0.3517 - val_accuracy: 0.8782
Epoch 7/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2580 - accuracy: 0.9041 - val_loss: 0.3315 - val_accuracy: 0.8846
Epoch 8/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2439 - accuracy: 0.9084 - val_loss: 0.3281 - val_accuracy: 0.8870
Epoch 9/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2340 - accuracy: 0.9125 - val_loss: 0.3325 - val_accuracy: 0.8851
Epoch 10/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2220 - accuracy: 0.9163 - val_loss: 0.3119 - val_accuracy: 0.8917
Epoch 11/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2148 - accuracy: 0.9189 - val_loss: 0.3411 - val_accuracy: 0.8831
Epoch 12/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.2081 - accuracy: 0.9210 - val_loss: 0.3186 - val_accuracy: 0.8894
Epoch 13/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1970 - accuracy: 0.9252 - val_loss: 0.3442 - val_accuracy: 0.8882
Epoch 14/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1911 - accuracy: 0.9280 - val_loss: 0.3057 - val_accuracy: 0.8994
Epoch 15/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1865 - accuracy: 0.9287 - val_loss: 0.3203 - val_accuracy: 0.8938
Epoch 16/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1770 - accuracy: 0.9333 - val_loss: 0.3222 - val_accuracy: 0.8953
Epoch 17/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1723 - accuracy: 0.9352 - val_loss: 0.3336 - val_accuracy: 0.8944
Epoch 18/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1672 - accuracy: 0.9368 - val_loss: 0.3268 - val_accuracy: 0.8963
Epoch 19/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1596 - accuracy: 0.9413 - val_loss: 0.3511 - val_accuracy: 0.8930
Epoch 20/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1599 - accuracy: 0.9404 - val_loss: 0.3686 - val_accuracy: 0.8907
Epoch 21/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1506 - accuracy: 0.9420 - val_loss: 0.3596 - val_accuracy: 0.8914
Epoch 22/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1460 - accuracy: 0.9452 - val_loss: 0.3511 - val_accuracy: 0.8975
Epoch 23/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1445 - accuracy: 0.9460 - val_loss: 0.4118 - val_accuracy: 0.8825
Epoch 24/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1384 - accuracy: 0.9477 - val_loss: 0.3831 - val_accuracy: 0.8900
Epoch 25/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1340 - accuracy: 0.9499 - val_loss: 0.3846 - val_accuracy: 0.8949
Epoch 26/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1309 - accuracy: 0.9511 - val_loss: 0.3845 - val_accuracy: 0.8950
Epoch 27/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1268 - accuracy: 0.9521 - val_loss: 0.3950 - val_accuracy: 0.8967
Epoch 28/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1242 - accuracy: 0.9530 - val_loss: 0.3944 - val_accuracy: 0.8941
Epoch 29/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1196 - accuracy: 0.9549 - val_loss: 0.4600 - val_accuracy: 0.8852
Epoch 30/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1161 - accuracy: 0.9557 - val_loss: 0.4099 - val_accuracy: 0.8901
Epoch 31/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1137 - accuracy: 0.9572 - val_loss: 0.4176 - val_accuracy: 0.8960
Epoch 32/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1115 - accuracy: 0.9578 - val_loss: 0.4383 - val_accuracy: 0.8903
Epoch 33/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1063 - accuracy: 0.9603 - val_loss: 0.4602 - val_accuracy: 0.8877
Epoch 34/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1097 - accuracy: 0.9593 - val_loss: 0.4323 - val_accuracy: 0.8947
Epoch 35/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1048 - accuracy: 0.9601 - val_loss: 0.4370 - val_accuracy: 0.8925
Epoch 36/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.0995 - accuracy: 0.9624 - val_loss: 0.4267 - val_accuracy: 0.8924
Epoch 37/37
1500/1500 [==============================] - 5s 3ms/step - loss: 0.1005 - accuracy: 0.9619 - val_loss: 0.4724 - val_accuracy: 0.8898
<keras.src.callbacks.History at 0x7f3b94fc5dc0>

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: 0.5181 - accuracy: 0.8854
[test loss, test accuracy]: [0.5180676579475403, 0.8853999972343445]

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.