def search_space():
num_units_first_dense = UniformNumber(lower=950,
upper=1450,
default=1024,
type=int,
log_scale=True)
num_units_second_dense = UniformNumber(lower=900,
upper=1600,
default=1024,
type=int,
log_scale=True)
dropout_rate = UniformFloat(lower=.33, upper=.55, default=.45)
lr = UniformFloat(lower=.003, upper=.007, default=.004)
momentum = UniformFloat(lower=.4, upper=.93, default=.5)
def search_space():
batch_size = UniformInt(lower=8, upper=64, default=32, log_scale=True)
num_units_first_layer = UniformInt(lower=16,
upper=1024,
default=32,
log_scale=True)
num_units_second_layer = UniformInt(lower=16,
upper=1024,
default=32,
log_scale=True)
dropout_first_layer = UniformFloat(lower=0, upper=.99, default=.2)
dropout_second_layer = UniformFloat(lower=0, upper=.99, default=.2)
learning_rate = UniformFloat(lower=10e-6,
upper=10e-1,
default=10e-2,
log_scale=True)
def search_space():
lr = UniformFloat(0.0001, 0.01, default=0.001, log_scale=True)
batch_size = UniformNumber(8, 256, default=100, type=int)
n_hidden = UniformNumber(32, 256, default=32, type=int)
keep_prob = UniformFloat(0.2, 1.0, default=0.5)
def search_space():
x = (UniformFloat(-5, 10), UniformFloat(0, 15))
def small_search_space():
batch_size = UniformInt(lower=32, upper=64, default=32, log_scale=True)
learning_rate = UniformFloat(lower=10e-3,
upper=10e-2,
default=10e-2,
log_scale=True)