def main():
"""Create and execute an experiment."""
model = AnalogSequential(
Flatten(),
AnalogLinear(INPUT_SIZE,
HIDDEN_SIZES[0],
True,
rpu_config=SingleRPUConfig(device=ConstantStepDevice())),
Sigmoid(),
AnalogLinear(HIDDEN_SIZES[0],
HIDDEN_SIZES[1],
True,
rpu_config=SingleRPUConfig(device=ConstantStepDevice())),
Sigmoid(),
AnalogLinear(HIDDEN_SIZES[1],
OUTPUT_SIZE,
True,
rpu_config=SingleRPUConfig(device=ConstantStepDevice())),
LogSoftmax(dim=1))
# Create the training Experiment.
experiment = BasicTraining(dataset=FashionMNIST,
model=model,
epochs=EPOCHS,
batch_size=BATCH_SIZE)
# Create the runner and execute the experiment.
runner = LocalRunner(device=DEVICE)
results = runner.run(experiment, dataset_root=PATH_DATASET)
print(results)
def get_experiment(self,
real: bool = False,
rpu_config: Any = EcRamPreset):
"""Return a BasicTraining experiment."""
argv = {
'dataset': FashionMNIST,
'model': self.get_model(rpu_config),
'epochs': 30,
'batch_size': 8,
'learning_rate': 0.01
}
if not real:
argv['epochs'] = 1
return BasicTraining(**argv)
def get_experiment(self,
real: bool = False,
rpu_config: Any = IdealizedPreset):
"""Return a BasicTraining experiment."""
argv = {
'dataset': SVHN,
'model': self.get_model(rpu_config),
'epochs': 20,
'batch_size': 10,
'learning_rate': 0.01
}
if not real:
argv['epochs'] = 1
return BasicTraining(**argv)
