Problem = HpProblem()
Problem.add_dim('batch_size', (1, 32))
Problem.add_dim('image_size', [32])
Problem.add_dim('conv1_in_chan', [3])
Problem.add_dim('conv1_out_chan', (3, 64))
Problem.add_dim('conv1_kern', (3, 8))
Problem.add_dim('pool_size', [2])
Problem.add_dim('conv2_out_chan', (3, 64))
Problem.add_dim('conv2_kern', (3, 8))
Problem.add_dim('fc1_out', (64, 16384))
Problem.add_dim('fc2_out', (32, 16384))
Problem.add_dim('fc3_out', [10])
Problem.add_dim('omp_num_threads', [64])
Problem.add_starting_point(batch_size=10,
image_size=32,
conv1_in_chan=3,
conv1_out_chan=16,
conv1_kern=5,
pool_size=2,
conv2_out_chan=16,
conv2_kern=5,
fc1_out=128,
fc2_out=84,
fc3_out=10,
omp_num_threads=64)
if __name__ == '__main__':
print(Problem)
from deephyper.benchmark import HpProblem
Problem = HpProblem()
Problem.add_dim('batch_size', (1, 64))
Problem.add_dim('image_size', (16, 128))
Problem.add_dim('in_channels', (2, 64))
Problem.add_dim('out_channels', (2, 64))
Problem.add_dim('kernel_size', (2, 10))
Problem.add_dim('omp_num_threads', [64])
Problem.add_starting_point(batch_size=10,
image_size=28,
in_channels=2,
out_channels=2,
kernel_size=2,
omp_num_threads=64)
if __name__ == '__main__':
print(Problem)
from deephyper.benchmark import HpProblem
Problem = HpProblem()
Problem.add_dim('batch_size', (1, 512))
Problem.add_dim('height', (128, 1024))
Problem.add_dim('width', (128, 1024))
Problem.add_dim('in_channels', (2, 64))
Problem.add_dim('out_channels', (2, 64))
Problem.add_dim('kernel_size', (2, 16))
Problem.add_dim('omp_num_threads', (8, 64))
Problem.add_starting_point(batch_size=128,
height=512,
width=512,
in_channels=3,
out_channels=64,
kernel_size=3,
omp_num_threads=64)
if __name__ == '__main__':
print(Problem)
from deephyper.benchmark import HpProblem
# import os
# import sys
# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from torch_wrapper import use_knl # noqa
Problem = HpProblem()
Problem.add_dim("batch_size", (1, 8192))
Problem.add_dim("in_features", (128, 8192))
Problem.add_dim("out_features", (128, 8192))
Problem.add_dim("bias", [0, 1])
if use_knl:
Problem.add_dim("omp_num_threads", (8, 64))
Problem.add_starting_point(batch_size=128,
in_features=1024,
out_features=512,
bias=0,
omp_num_threads=64)
else:
Problem.add_starting_point(batch_size=128,
in_features=1024,
out_features=512,
bias=0)
if __name__ == "__main__":
print(Problem)
from deephyper.benchmark import HpProblem
Problem = HpProblem()
sp = {}
# Problem.add_dim("lr", (0.0009, 0.1))
for i in range(1, 18):
Problem.add_dim(f"u{i}", (32, 128))
Problem.add_dim(f"a{i}", [None, "relu", "sigmoid", "tanh"])
sp[f"u{i}"] = 64
sp[f"a{i}"] = "relu"
Problem.add_starting_point(**sp)
Problem.add_starting_point(
**{
"a1": None,
"a10": "relu",
"a11": "relu",
"a12": "tanh",
"a13": "tanh",
"a14": "tanh",
"a15": "relu",
"a16": "relu",
"a17": "tanh",
"a2": "sigmoid",
"a3": None,
"a4": "sigmoid",
"a5": "tanh",
"a6": None,
from deephyper.benchmark 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)
import os
import numpy as np
from deephyper.benchmark import HpProblem
from deephyper.benchmark.benchmark_functions_wrappers import polynome_2
# Problem definition
Problem = HpProblem()
num_dim = 10
for i in range(num_dim):
Problem.add_dim(f'e{i}', (-10.0, 10.0))
Problem.add_starting_point(**{f'e{i}': 10.0 for i in range(num_dim)})
# Definition of the function which runs the model
def run(param_dict):
f, _, _ = polynome_2()
num_dim = 10
x = np.array([param_dict[f'e{i}'] for i in range(num_dim)])
return f(x) # the objective
if __name__ == '__main__':
print(Problem)
# some_file.py
import sys
# insert at 1, 0 is the script path (or '' in REPL)
sys.path.insert(1, '/home/yzamora/perf_pred/deephyper_repo/deephyper/')
from deephyper.benchmark import HpProblem
Problem = HpProblem()
#Width of hidden layers
Problem.add_dim('nunits', (1, 1000))
Problem.add_dim('depth', (1,20))
#Problem.add_dim('nunits_l2', (1, 1000))
Problem.add_dim('activation', ['relu', 'elu', 'selu', 'tanh'])
Problem.add_dim('batch_size', (8, 100))
#Problem.add_dim('dropout_l1', (0.0, 1.0))
#Problem.add_dim('dropout_l2', (0.0, 1.0))
Problem.add_starting_point(
nunits=1,
activation='relu',
batch_size=8,
depth=1
)
if __name__ == '__main__':
print(Problem)
# problem.py
from deephyper.benchmark import HpProblem
Problem = HpProblem()
Problem.add_dim('log2_batch_size', (5, 10))
Problem.add_dim('nunits_1', (10, 100))
Problem.add_dim('nunits_2', (10, 30))
Problem.add_dim('dropout_1', (0.0, 1.0))
Problem.add_dim('dropout_2', (0.0, 1.0))
Problem.add_dim('optimizer_type', ['RMSprop', 'Adam'])
Problem.add_starting_point(log2_batch_size=7,
nunits_1=100,
nunits_2=20,
dropout_1=0.2,
dropout_2=0.2,
optimizer_type='RMSprop')
Problem = HpProblem()
Problem.add_dim("batch_size", (1, 1024))
Problem.add_dim("seq_length", (128, 1024))
Problem.add_dim("in_features", (128, 1024))
Problem.add_dim("hidden_units", (128, 1024))
Problem.add_dim("num_layers", (1, 2))
# Problem.add_dim("out_features", (128, 8192))
Problem.add_dim("bias", [0, 1])
if use_knl:
Problem.add_dim("omp_num_threads", (8, 64))
Problem.add_starting_point(
batch_size=128,
seq_length=512,
in_features=1024,
hidden_units=512,
num_layers=1,
bias=0,
omp_num_threads=64,
)
else:
Problem.add_starting_point(
batch_size=128,
seq_length=512,
in_features=1024,
hidden_units=512,
num_layers=1,
bias=0,
)
from deephyper.benchmark 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.))
Problem.add_starting_point(units=10, activation=None, lr=0.01)
if __name__ == '__main__':
print(Problem)