On top of this, the code introduces the Object-Oriented interface for SHADHO
spaces, as well as repeating and dependent spaces.
"""
from shadho import Shadho, spaces
if __name__ == '__main__':
# As a part of the architecture search, we are interested in optimizing
# the number of layers, size/shape of each layer, activation function,
# and whether or not to attach a batch normalization layer.
# Like with the SVM example, search spaces can be defined once and reused
# in multiple places.
activations = ['glu', 'leaky_relu', 'prelu', 'relu', 'selu', 'sigmoid', 'tanh']
batch_norm = spaces.log10_uniform(-4, 4)
# For each convolutional layer, we sample over the number of convolutional
# kernels, the kernel shape, activation function, and batch normalization.
conv_layer = spaces.scope(
out_filters=spaces.log2_randint(4, 10),
kernel_shape=spaces.randint(1, 10, step=2),
activation=activations,
batch_norm=batch_norm
)
# Additionally, we want to not worry about computing padding during model
# construction. SHADHO offers *dependent* hyperparameter domains that
# compute their value based on the value of another domain. The `padding`
# domain here implements "same" padding.
conv = spaces.scope(
kernel_size=spaces.randint(1, 12, 2),
activation=activations,
kernel_initializer=initializers,
bias_initializer=initializers,
kernel_regularizer=regularizers,
bias_regularizer=regularizers,
activity_regularizer=regularizers,
kernel_constrains=constraints,
bias_constraint=constraints)
# Search over the built-in optimizers, parameterizing SGD
optimizers = spaces.scope(
exclusive=True
sgd=spaces.scope(
lr=spaces.log10_uniform(-4, -1),
momentum=spaces.uniform(0, 1),
decay=spaces.log10_uniform(-4, -1)),
rmsprop='rmsprop',
adagrad='adagrad',
adadelta='adadelta',
adam='adam',
adamax='adamax',
nadam='nadam')
# Set up the full search space over the U-Net down- and upsampling blocks
space = spaces.scope(
optimizer=optimizers,
min_filters=spaces.log2_randint(5, 8),
down1=spaces.scope(conv1=conv, conv2=conv),
down2=spaces.scope(conv1=conv, conv2=conv),
args.result_file = './'
if args.master_name == '':
parser.error('must provide a distinct master name')
# TODO check if provided paths are valid
#if args.output_results_path invalid, then parser error.
return args
if __name__ == '__main__':
args = parse_args()
# Domains can be stored as variables and used more than once in the event
# that the domain is used multilpe times.
C = spaces.log2_uniform(-5, 15)
gamma = spaces.log10_uniform(-3, 3)
coef0 = spaces.uniform(-1000, 1000)
# The search space in this case is hierarchical with mutually exclusive
# subspaces for each SVM kernel. The 'exclusive' tag instructs SHADHO to
# select one of the subspaces from among 'linear', 'rbf', 'sigmoid', and
# 'poly' at a time and only generate hyperprameters for that subspace.
space = {
'exclusive': True,
'linear': {
'kernel': 'linear', # add the kernel name for convenience
'C': C
},
'rbf': {
'kernel': 'rbf', # add the kernel name for convenience
'C': C,
On top of setting up the search, this tutorial demonstrates the use of the
"exclusive" flag to split non-overlapping search spaces into separate trees.
"""
from shadho import Shadho, spaces
if __name__ == '__main__':
# Set up the search space. In this case, we care searching over SVM kernel
# hyperparameterizations. Because some spaces are used with multiple
# kernels, we can create the spaces outside of the dictionary and use them
# multiple times. SHADHO makes sure no aliasing occurs.
C = spaces.uniform(-1000, 2000)
gamma = spaces.log10_uniform(-5, 8)
coef0 = spaces.uniform(-1000, 2000)
degree = [2, 3, 4, 5, 6, 7]
# The joint hyperparameter domains for each kernel should be searched
# independent of one another, so we use the "exclusive" flag to tell
# SHADHO to sample each space independently.
search_space = {
'linear': {
'C': C,
},
'rbf': {
'C': C,
'gamma': gamma,
},