from deephyper.problem import HpProblem
Problem = HpProblem()
Problem.add_dim('units', (1, 100))
Problem.add_dim('activation', ['NA', 'relu', 'sigmoid', 'tanh'])
Problem.add_dim('lr', (0.0001, 1.))
Problem.add_starting_point(
units=10,
activation='relu',
lr=0.01)
if __name__ == '__main__':
print(Problem)
"""
problem.py
"""
import ConfigSpace as cs
from deephyper.problem import HpProblem
Problem = HpProblem()
# call signature: Problem.add_dim(name, value)
Problem.add_dim('units1', (1, 64)) # int in range 1-64
Problem.add_dim('units2', (1, 64)) # int in range 1-64
Problem.add_dim('dropout1', (0.0, 1.0)) # float in range 0-1
Problem.add_dim('dropout2', (0.0, 1.0)) # float in range 0-1
Problem.add_dim('batch_size', (5, 500)) # int in range 5-500
Problem.add_dim('log10_learning_rate', (-5.0, 0.0)) # float lr range from 10^-5 to 1
# one of ['relu', ..., ]
Problem.add_dim('activation', ['relu', 'elu', 'selu', 'tanh'])
optimizer = Problem.add_dim('optimizer', [
'Adam', 'RMSprop', 'SGD', 'Nadam', 'Adagrad'
])
# Only vary momentum if optimizer is SGD
momentum = Problem.add_dim("momentum", (0.5, 0.9))
Problem.add_condition(cs.EqualsCondition(momentum, optimizer, "SGD"))
# Add a starting point to try first
Problem.add_starting_point(
units1=16,
from deephyper.problem import HpProblem
Problem = HpProblem()
Problem.add_dim("lr", [1e-4, 5e-4, .001, .005, .01, .1])
Problem.add_dim("trade_off", [.01, .05, .1, .5, 1])
Problem.add_dim("cycle_length", [2, 4, 5, 8, 10])
Problem.add_dim("weight_decay", [1e-5, 1e-4, 1e-3, 5e-2, .01, .1])
Problem.add_starting_point(lr=1e-4, trade_off=.01, cycle_length=2, weight_decay = 1e-5)
if __name__ == "__main__":
print(Problem)
from deephyper.problem import HpProblem
Problem = HpProblem()
Problem.add_dim("lr", [1e-4, 5e-4, .001, .005, .01, .1])
Problem.add_dim("trade_off", [.01, .05, .1, .5, 1])
Problem.add_dim("intra_loss_coef", [.01, .05, .1, .5, 1])
Problem.add_dim("inter_loss_coef", [.01, .05, .1, .5, 1])
Problem.add_dim("em_loss_coef", [.01, .05, .1, .5, 1])
Problem.add_dim("cycle_length", [2, 4, 8, 10])
Problem.add_dim("weight_decay", [1e-4, 1e-3, 5e-2, .01, .1])
Problem.add_dim("ad_net_mult_lr", [.01, .05, .1, .5, .75, 1.1, 1.5])
Problem.add_dim("beta_1", [.7, .8, .9])
Problem.add_dim("beta_2", [.8, .9, .99])
Problem.add_starting_point(lr=1e-4, trade_off=.01, intra_loss_coef=.01, inter_loss_coef=.01, em_loss_coef=.01, cycle_length=2, \
weight_decay = 1e-4, ad_net_mult_lr = .01, beta_1 = .7, beta_2 = .8)
if __name__ == "__main__":
print(Problem)
from deephyper.problem import HpProblem
Problem = HpProblem()
Problem.add_dim("units", (1, 100))
Problem.add_dim("activation", [None, "relu", "sigmoid", "tanh"])
Problem.add_dim("lr", (0.0001, 1.0))
Problem.add_starting_point(units=10, activation=None, lr=0.01)
if __name__ == "__main__":
print(Problem)
from deephyper.problem import HpProblem
Problem = HpProblem()
Problem.add_dim("epochs", (5, 500))
Problem.add_dim("nunits_l1", (1, 1000))
Problem.add_dim("nunits_l2", (1, 1000))
Problem.add_dim("activation_l1", ["relu", "elu", "selu", "tanh"])
Problem.add_dim("activation_l2", ["relu", "elu", "selu", "tanh"])
Problem.add_dim("batch_size", (8, 1024))
Problem.add_dim("dropout_l1", (0.0, 1.0))
Problem.add_dim("dropout_l2", (0.0, 1.0))
Problem.add_starting_point(
epochs=5,
nunits_l1=1,
nunits_l2=2,
activation_l1="relu",
activation_l2="relu",
batch_size=8,
dropout_l1=0.0,
dropout_l2=0.0,
)
if __name__ == "__main__":
print(Problem)