 View on TensorFlow.org
|
 Run in Google Colab
|
 View source on GitHub
|
 Download notebook
|
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:
- Model hyperparameters which influence model selection such as the number and width of hidden layers
- 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
2024-08-16 01:25:04.811063: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-08-16 01:25:04.832191: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-08-16 01:25:04.838460: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] 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
HyperModelclass 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')
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1723771509.637777 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.641612 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.644868 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.648549 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.660168 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.663655 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.666633 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.670142 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.673591 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.677149 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.680118 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771509.683613 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.907409 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.909510 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.911592 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.913615 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.915637 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.917579 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.919546 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.921484 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.923384 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.925354 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.927319 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.929258 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.967243 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.969279 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.971299 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.973289 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.975342 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.977295 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.979282 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.981238 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.983175 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.985662 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.988059 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723771510.990406 14090 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
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 25s] val_accuracy: 0.8913333415985107 Best val_accuracy So Far: 0.8913333415985107 Total elapsed time: 00h 05m 37s 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 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.7774 - loss: 0.6344 - val_accuracy: 0.8590 - val_loss: 0.4024 Epoch 2/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.8643 - loss: 0.3766 - val_accuracy: 0.8632 - val_loss: 0.3783 Epoch 3/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.8790 - loss: 0.3291 - val_accuracy: 0.8803 - val_loss: 0.3296 Epoch 4/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.8884 - loss: 0.3036 - val_accuracy: 0.8708 - val_loss: 0.3529 Epoch 5/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.8955 - loss: 0.2840 - val_accuracy: 0.8817 - val_loss: 0.3297 Epoch 6/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.8998 - loss: 0.2655 - val_accuracy: 0.8581 - val_loss: 0.4232 Epoch 7/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9040 - loss: 0.2564 - val_accuracy: 0.8808 - val_loss: 0.3396 Epoch 8/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9101 - loss: 0.2421 - val_accuracy: 0.8718 - val_loss: 0.3550 Epoch 9/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9089 - loss: 0.2353 - val_accuracy: 0.8920 - val_loss: 0.3055 Epoch 10/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9160 - loss: 0.2239 - val_accuracy: 0.8918 - val_loss: 0.3077 Epoch 11/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9211 - loss: 0.2111 - val_accuracy: 0.8913 - val_loss: 0.3258 Epoch 12/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9223 - loss: 0.2076 - val_accuracy: 0.8936 - val_loss: 0.3115 Epoch 13/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9260 - loss: 0.1968 - val_accuracy: 0.8892 - val_loss: 0.3134 Epoch 14/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9273 - loss: 0.1914 - val_accuracy: 0.8890 - val_loss: 0.3284 Epoch 15/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9321 - loss: 0.1836 - val_accuracy: 0.8911 - val_loss: 0.3366 Epoch 16/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9363 - loss: 0.1710 - val_accuracy: 0.8952 - val_loss: 0.3252 Epoch 17/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9374 - loss: 0.1710 - val_accuracy: 0.8898 - val_loss: 0.3381 Epoch 18/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9383 - loss: 0.1629 - val_accuracy: 0.8913 - val_loss: 0.3500 Epoch 19/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9414 - loss: 0.1552 - val_accuracy: 0.8954 - val_loss: 0.3418 Epoch 20/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9435 - loss: 0.1495 - val_accuracy: 0.8926 - val_loss: 0.3455 Epoch 21/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9431 - loss: 0.1509 - val_accuracy: 0.8903 - val_loss: 0.3748 Epoch 22/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9454 - loss: 0.1431 - val_accuracy: 0.8960 - val_loss: 0.3444 Epoch 23/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9490 - loss: 0.1361 - val_accuracy: 0.8948 - val_loss: 0.3433 Epoch 24/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9492 - loss: 0.1342 - val_accuracy: 0.8918 - val_loss: 0.3569 Epoch 25/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9488 - loss: 0.1342 - val_accuracy: 0.8910 - val_loss: 0.3757 Epoch 26/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9528 - loss: 0.1263 - val_accuracy: 0.8914 - val_loss: 0.3831 Epoch 27/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9518 - loss: 0.1261 - val_accuracy: 0.8935 - val_loss: 0.3801 Epoch 28/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9545 - loss: 0.1193 - val_accuracy: 0.8914 - val_loss: 0.4115 Epoch 29/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9551 - loss: 0.1182 - val_accuracy: 0.8816 - val_loss: 0.4434 Epoch 30/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9577 - loss: 0.1131 - val_accuracy: 0.8971 - val_loss: 0.3876 Epoch 31/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9558 - loss: 0.1169 - val_accuracy: 0.8903 - val_loss: 0.4025 Epoch 32/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9574 - loss: 0.1101 - val_accuracy: 0.8984 - val_loss: 0.4147 Epoch 33/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9583 - loss: 0.1114 - val_accuracy: 0.8970 - val_loss: 0.4005 Epoch 34/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9623 - loss: 0.1007 - val_accuracy: 0.8935 - val_loss: 0.4260 Epoch 35/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9614 - loss: 0.1021 - val_accuracy: 0.8926 - val_loss: 0.4296 Epoch 36/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9642 - loss: 0.0961 - val_accuracy: 0.8928 - val_loss: 0.4305 Epoch 37/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9633 - loss: 0.0964 - val_accuracy: 0.8891 - val_loss: 0.4603 Epoch 38/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9634 - loss: 0.0962 - val_accuracy: 0.8977 - val_loss: 0.4350 Epoch 39/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9645 - loss: 0.0953 - val_accuracy: 0.8938 - val_loss: 0.4520 Epoch 40/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9661 - loss: 0.0875 - val_accuracy: 0.8923 - val_loss: 0.4823 Epoch 41/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9652 - loss: 0.0903 - val_accuracy: 0.8904 - val_loss: 0.4852 Epoch 42/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9686 - loss: 0.0844 - val_accuracy: 0.8822 - val_loss: 0.5031 Epoch 43/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9672 - loss: 0.0860 - val_accuracy: 0.8942 - val_loss: 0.4723 Epoch 44/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9691 - loss: 0.0818 - val_accuracy: 0.8944 - val_loss: 0.4678 Epoch 45/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9726 - loss: 0.0719 - val_accuracy: 0.8940 - val_loss: 0.4623 Epoch 46/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9697 - loss: 0.0792 - val_accuracy: 0.8934 - val_loss: 0.4757 Epoch 47/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9720 - loss: 0.0728 - val_accuracy: 0.8953 - val_loss: 0.5138 Epoch 48/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9717 - loss: 0.0752 - val_accuracy: 0.8931 - val_loss: 0.5226 Epoch 49/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9728 - loss: 0.0746 - val_accuracy: 0.8975 - val_loss: 0.5169 Epoch 50/50 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9723 - loss: 0.0755 - val_accuracy: 0.8923 - val_loss: 0.5257 Best epoch: 32
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 1/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.7789 - loss: 0.6225 - val_accuracy: 0.8573 - val_loss: 0.4007 Epoch 2/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.8678 - loss: 0.3664 - val_accuracy: 0.8658 - val_loss: 0.3632 Epoch 3/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8781 - loss: 0.3343 - val_accuracy: 0.8702 - val_loss: 0.3546 Epoch 4/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8879 - loss: 0.3070 - val_accuracy: 0.8768 - val_loss: 0.3472 Epoch 5/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8908 - loss: 0.2902 - val_accuracy: 0.8777 - val_loss: 0.3441 Epoch 6/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9002 - loss: 0.2703 - val_accuracy: 0.8832 - val_loss: 0.3262 Epoch 7/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9053 - loss: 0.2552 - val_accuracy: 0.8917 - val_loss: 0.3017 Epoch 8/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9085 - loss: 0.2475 - val_accuracy: 0.8852 - val_loss: 0.3255 Epoch 9/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9112 - loss: 0.2351 - val_accuracy: 0.8930 - val_loss: 0.3077 Epoch 10/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9157 - loss: 0.2237 - val_accuracy: 0.8913 - val_loss: 0.3110 Epoch 11/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9194 - loss: 0.2138 - val_accuracy: 0.8927 - val_loss: 0.3143 Epoch 12/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9213 - loss: 0.2086 - val_accuracy: 0.8829 - val_loss: 0.3420 Epoch 13/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9240 - loss: 0.2002 - val_accuracy: 0.8898 - val_loss: 0.3196 Epoch 14/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9279 - loss: 0.1937 - val_accuracy: 0.8892 - val_loss: 0.3296 Epoch 15/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9314 - loss: 0.1839 - val_accuracy: 0.8842 - val_loss: 0.3548 Epoch 16/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9333 - loss: 0.1788 - val_accuracy: 0.8895 - val_loss: 0.3340 Epoch 17/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9348 - loss: 0.1738 - val_accuracy: 0.8977 - val_loss: 0.3317 Epoch 18/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9369 - loss: 0.1676 - val_accuracy: 0.8918 - val_loss: 0.3366 Epoch 19/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9379 - loss: 0.1621 - val_accuracy: 0.8974 - val_loss: 0.3227 Epoch 20/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9410 - loss: 0.1548 - val_accuracy: 0.8919 - val_loss: 0.3713 Epoch 21/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9417 - loss: 0.1532 - val_accuracy: 0.8823 - val_loss: 0.4058 Epoch 22/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9467 - loss: 0.1451 - val_accuracy: 0.8979 - val_loss: 0.3486 Epoch 23/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9464 - loss: 0.1425 - val_accuracy: 0.8975 - val_loss: 0.3381 Epoch 24/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9485 - loss: 0.1373 - val_accuracy: 0.8963 - val_loss: 0.3478 Epoch 25/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9500 - loss: 0.1341 - val_accuracy: 0.8926 - val_loss: 0.3846 Epoch 26/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9525 - loss: 0.1279 - val_accuracy: 0.8879 - val_loss: 0.3929 Epoch 27/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9518 - loss: 0.1268 - val_accuracy: 0.8972 - val_loss: 0.3604 Epoch 28/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9546 - loss: 0.1209 - val_accuracy: 0.8967 - val_loss: 0.3876 Epoch 29/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9537 - loss: 0.1233 - val_accuracy: 0.8942 - val_loss: 0.3985 Epoch 30/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9564 - loss: 0.1163 - val_accuracy: 0.8955 - val_loss: 0.4011 Epoch 31/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9559 - loss: 0.1171 - val_accuracy: 0.8975 - val_loss: 0.3997 Epoch 32/32 1500/1500 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9596 - loss: 0.1086 - val_accuracy: 0.8938 - val_loss: 0.4147 <keras.src.callbacks.history.History at 0x7fa8b80d4970>
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 - accuracy: 0.8873 - loss: 0.4595 [test loss, test accuracy]: [0.4649185538291931, 0.8881000280380249]
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.