from brainspy.utils import manager
from bspytasks.boolean.logger import Logger
from brainspy.utils.io import load_configs
from brainspy.utils.transforms import (
DataToTensor,
DataPointsToPlateau,
DataToVoltageRange,
)
from brainspy.processors.dnpu import DNPU
V_MIN = [-1.2, -1.2]
V_MAX = [0.6, 0.6]
configs = load_configs("configs/boolean.yaml")
data_transforms = transforms.Compose(
[DataToVoltageRange(V_MIN, V_MAX, -1, 1), DataToTensor('cpu')]
)
waveform_transforms = transforms.Compose(
[DataPointsToPlateau(configs["processor"]["data"]["waveform"])]
)
criterion = manager.get_criterion(configs["algorithm"])
algorithm = manager.get_algorithm(configs["algorithm"])
configs["current_dimension"] = 4
results = vc_dimension_test(
configs,
DNPU,
criterion,
algorithm,
plt.xlabel("Accuracy values")
plt.ylabel("Counts")
plt.savefig(os.path.join(save_dir, "accuracy_histogram_" + label + "." + extension))
if show_plots:
plt.show()
if __name__ == "__main__":
from torchvision import transforms
from brainspy.utils import manager
from brainspy.utils.io import load_configs
from brainspy.utils.transforms import DataToTensor, DataToVoltageRange
from brainspy.processors.dnpu import DNPU
V_MIN = [-1.2, -1.2]
V_MAX = [0.6, 0.6]
transforms = transforms.Compose(
[DataToVoltageRange(V_MIN, V_MAX, -1, 1), DataToTensor(torch.device('cpu'))]
)
configs = load_configs("configs/ring.yaml")
criterion = manager.get_criterion(configs["algorithm"])
algorithm = manager.get_algorithm(configs["algorithm"])
search_solution(configs, DNPU, criterion, algorithm, transforms=transforms)
from brainspy.utils import manager
from brainspy.utils.io import load_configs
from brainspy.utils.transforms import DataToTensor, DataToVoltageRange, DataPointsToPlateau, ToDevice
from brainspy.processors.dnpu import DNPU
#TorchUtils.force_cpu = False
V_MIN = [-1.2, -1.2]
V_MAX = [0.6, 0.6]
configs = load_configs("configs/ring.yaml")
data_transforms = tfms.Compose([
DataToVoltageRange(V_MIN, V_MAX, -1, 1),
DataToTensor(device=torch.device('cpu'))
])
# Add your custom transformations for the datapoints
waveform_transforms = tfms.Compose([
# DataPointsToPlateau(configs['processor']['waveform']),
ToDevice()
])
criterion = manager.get_criterion(configs["algorithm"])
algorithm = manager.get_algorithm(configs["algorithm"])
dataloaders = get_ring_data(configs, data_transforms)
ring_task(configs, dataloaders, DNPU, criterion,