.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_example_5_keras_worker.py: 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 | integer | [4, 64] | logarithmically varied | | the first conf layer | | | integer values | +-------------------------+----------------+-----------------+------------------------+ | Number of filters in | integer | [4, 64] | only active if number | | the second conf layer | | | of layers >= 2 | +-------------------------+----------------+-----------------+------------------------+ | Number of filters in | integer | [4, 64] | only active if number | | the third conf layer | | | of layers == 3 | +-------------------------+----------------+-----------------+------------------------+ | Dropout rate | float | [0, 0.9] | standard continuous | | | | | parameter | +-------------------------+----------------+-----------------+------------------------+ | Number of hidden units | integer | [8,256] | logarithmically varied | | in fully connected layer| | | 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. .. code-block:: python 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) .. _sphx_glr_download_auto_examples_example_5_keras_worker.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: example_5_keras_worker.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: example_5_keras_worker.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_