An hyperparameter tuner for Keras.
Here's how to perform hyperparameter tuning on the MNIST digits dataset, using the current API.
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
from kerastuner.tuners import GridSearch
from kerastuner.distributions import Range, Choice
(x, y), (val_x, val_y) = keras.datasets.mnist.load_data()
x = x.astype('float32') / 255.
val_x = val_x.astype('float32') / 255.
"""Basic case:
- We define a `build_model` function
- It returns a compiled model
- It uses hyperparameters defined on the fly
"""
def build_model():
model = keras.Sequential()
model.add(layers.Flatten(input_shape=(28, 28)))
for i in range(Range('num_layers', 2, 20)):
model.add(layers.Dense(units=Range('units_' + str(i), 32, 512, 32),
activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(
optimizer=keras.optimizers.Adam(
Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
tuner = GridSearch(
build_model,
objective='val_accuracy',
num_executions=2)
tuner.search(x=x,
y=y,
validation_data=(val_x, val_y))Here's how to perform hyperparameter tuning on the MNIST digits dataset.
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
from kerastuner.tuner import SequentialRandomSearch
(x, y), (val_x, val_y) = keras.datasets.mnist.load_data()
x = x.astype('float32') / 255.
val_x = val_x.astype('float32') / 255.
"""Basic case:
- We define a `build_model` function
- It returns a compiled model
- It uses hyperparameters defined on the fly
"""
def build_model(hp):
model = keras.Sequential()
model.add(layers.Flatten(input_shape=(28, 28)))
for i in range(hp.Range('num_layers', 2, 20)):
model.add(layers.Dense(units=hp.Range('units_' + str(i), 32, 512, 32),
activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(
optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
tuner = SequentialRandomSearch(
build_model,
objective='val_accuracy')
tuner.search(trials=2,
x=x,
y=y,
epochs=5,
validation_data=(val_x, val_y))