Note
Click here to download the full example code
Worker for Example 5 - Keras¶
In this example implements a small CNN in Keras to train it on MNIST. The configuration space shows the most common types of hyperparameters and even contains conditional dependencies.
We’ll optimise the following hyperparameters:
| Parameter Name | Parameter type | Range/Choices | Comment |
|---|---|---|---|
| Learning rate | float | [1e-6, 1e-2] | varied logarithmically |
| Optimizer | categorical | {Adam, SGD } | discrete choice |
| SGD momentum | float | [0, 0.99] | only active if optimizer == SGD |
| Number of conv layers | integer | [1,3] | can only take integer values 1, 2, or 3 |
| Number of filters in the first conf layer | integer | [4, 64] | logarithmically varied integer values |
| Number of filters in the second conf layer | integer | [4, 64] | only active if number of layers >= 2 |
| Number of filters in the third conf layer | integer | [4, 64] | only active if number of layers == 3 |
| Dropout rate | float | [0, 0.9] | standard continuous parameter |
| Number of hidden units in fully connected layer | integer | [8,256] | logarithmically varied integer values |
Please refer to the compute method below to see how those are defined using the ConfigSpace package.
The network does not achieve stellar performance when a random configuration is samples, but a few iterations should yield an accuracy of >90%. To speed up training, only 8192 images are used for training, 1024 for validation. The purpose is not to achieve state of the art on MNIST, but to show how to use Keras inside HpBandSter, and to demonstrate a more complicated search space.
try:
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
except:
raise ImportError("For this example you need to install keras.")
try:
import torchvision
import torchvision.transforms as transforms
except:
raise ImportError("For this example you need to install pytorch-vision.")
import ConfigSpace as CS
import ConfigSpace.hyperparameters as CSH
from hpbandster.core.worker import Worker
import logging
logging.basicConfig(level=logging.DEBUG)
class KerasWorker(Worker):
def __init__(self, N_train=8192, N_valid=1024, **kwargs):
super().__init__(**kwargs)
self.batch_size = 64
img_rows = 28
img_cols = 28
self.num_classes = 10
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
self.input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
self.input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# zero-one normalization
x_train /= 255
x_test /= 255
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
self.x_train, self.y_train = x_train[:N_train], y_train[:N_train]
self.x_validation, self.y_validation = x_train[-N_valid:], y_train[-N_valid:]
self.x_test, self.y_test = x_test, y_test
self.input_shape = (img_rows, img_cols, 1)
def compute(self, config, budget, working_directory, *args, **kwargs):
"""
Simple example for a compute function using a feed forward network.
It is trained on the MNIST dataset.
The input parameter "config" (dictionary) contains the sampled configurations passed by the bohb optimizer
"""
model = Sequential()
model.add(Conv2D(config['num_filters_1'], kernel_size=(3,3),
activation='relu',
input_shape=self.input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
if config['num_conv_layers'] > 1:
model.add(Conv2D(config['num_filters_2'], kernel_size=(3, 3),
activation='relu',
input_shape=self.input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
if config['num_conv_layers'] > 2:
model.add(Conv2D(config['num_filters_3'], kernel_size=(3, 3),
activation='relu',
input_shape=self.input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(config['dropout_rate']))
model.add(Flatten())
model.add(Dense(config['num_fc_units'], activation='relu'))
model.add(Dropout(config['dropout_rate']))
model.add(Dense(self.num_classes, activation='softmax'))
if config['optimizer'] == 'Adam':
optimizer = keras.optimizers.Adam(lr=config['lr'])
else:
optimizer = keras.optimizers.SGD(lr=config['lr'], momentum=config['sgd_momentum'])
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=optimizer,
metrics=['accuracy'])
model.fit(self.x_train, self.y_train,
batch_size=self.batch_size,
epochs=int(budget),
verbose=0,
validation_data=(self.x_test, self.y_test))
train_score = model.evaluate(self.x_train, self.y_train, verbose=0)
val_score = model.evaluate(self.x_validation, self.y_validation, verbose=0)
test_score = model.evaluate(self.x_test, self.y_test, verbose=0)
#import IPython; IPython.embed()
return ({
'loss': 1-val_score[1], # remember: HpBandSter always minimizes!
'info': { 'test accuracy': test_score[1],
'train accuracy': train_score[1],
'validation accuracy': val_score[1],
'number of parameters': model.count_params(),
}
})
@staticmethod
def get_configspace():
"""
It builds the configuration space with the needed hyperparameters.
It is easily possible to implement different types of hyperparameters.
Beside float-hyperparameters on a log scale, it is also able to handle categorical input parameter.
:return: ConfigurationsSpace-Object
"""
cs = CS.ConfigurationSpace()
lr = CSH.UniformFloatHyperparameter('lr', lower=1e-6, upper=1e-1, default_value='1e-2', log=True)
# For demonstration purposes, we add different optimizers as categorical hyperparameters.
# To show how to use conditional hyperparameters with ConfigSpace, we'll add the optimizers 'Adam' and 'SGD'.
# SGD has a different parameter 'momentum'.
optimizer = CSH.CategoricalHyperparameter('optimizer', ['Adam', 'SGD'])
sgd_momentum = CSH.UniformFloatHyperparameter('sgd_momentum', lower=0.0, upper=0.99, default_value=0.9, log=False)
cs.add_hyperparameters([lr, optimizer, sgd_momentum])
num_conv_layers = CSH.UniformIntegerHyperparameter('num_conv_layers', lower=1, upper=3, default_value=2)
num_filters_1 = CSH.UniformIntegerHyperparameter('num_filters_1', lower=4, upper=64, default_value=16, log=True)
num_filters_2 = CSH.UniformIntegerHyperparameter('num_filters_2', lower=4, upper=64, default_value=16, log=True)
num_filters_3 = CSH.UniformIntegerHyperparameter('num_filters_3', lower=4, upper=64, default_value=16, log=True)
cs.add_hyperparameters([num_conv_layers, num_filters_1, num_filters_2, num_filters_3])
dropout_rate = CSH.UniformFloatHyperparameter('dropout_rate', lower=0.0, upper=0.9, default_value=0.5, log=False)
num_fc_units = CSH.UniformIntegerHyperparameter('num_fc_units', lower=8, upper=256, default_value=32, log=True)
cs.add_hyperparameters([dropout_rate, num_fc_units])
# The hyperparameter sgd_momentum will be used,if the configuration
# contains 'SGD' as optimizer.
cond = CS.EqualsCondition(sgd_momentum, optimizer, 'SGD')
cs.add_condition(cond)
# You can also use inequality conditions:
cond = CS.GreaterThanCondition(num_filters_2, num_conv_layers, 1)
cs.add_condition(cond)
cond = CS.GreaterThanCondition(num_filters_3, num_conv_layers, 2)
cs.add_condition(cond)
return cs
if __name__ == "__main__":
worker = KerasWorker(run_id='0')
cs = worker.get_configspace()
config = cs.sample_configuration().get_dictionary()
print(config)
res = worker.compute(config=config, budget=1, working_directory='.')
print(res)
Total running time of the script: ( 0 minutes 0.000 seconds)