Commit 5b9eef90 authored by Martin Řepa's avatar Martin Řepa

Learnign epochs with big nn and syntetic data

parent b1b3a15e
experiments_per_setup: 6 # 6
legacy_folder: /home/ignac/experiments/learning_epochs/2000to30000by4000_6times_smallnn_synteticdata # Change in regards with configuration
legacy_folder: /home/ignac/experiments/learning_epochs/2000to30000by4000_6times_bignn_synteticdata # Change in regards with configuration
epochs:
lower_bound: 2000
number_of_steps: 8 # 8 (6-8 should be enough)
......
......@@ -69,28 +69,28 @@ class SoftClip(nn.Module):
class NeuralNetwork:
def __init__(self, input_features=2,
nn_conf: NeuralNetworkConfig = NeuralNetworkConfig()):
# self.model = nn.Sequential(
# nn.Linear(input_features, 20),
# nn.ReLU(),
# nn.Linear(20, 15),
# nn.ReLU(),
# nn.Linear(15, 20),
# nn.ReLU(),
# nn.Linear(20, 1),
# nn.Tanh(),
# SoftClip(50)
# # nn.Sigmoid()
# ).to(DEVICE)
self.model = nn.Sequential(
nn.Linear(input_features, 5),
nn.Linear(input_features, 20),
nn.ReLU(),
nn.Linear(5, 5),
nn.Linear(20, 15),
nn.ReLU(),
nn.Linear(5, 1),
nn.Linear(15, 20),
nn.ReLU(),
nn.Linear(20, 1),
nn.Tanh(),
SoftClip(50)
# nn.Sigmoid()
).to(DEVICE)
# self.model = nn.Sequential(
# nn.Linear(input_features, 5),
# nn.ReLU(),
# nn.Linear(5, 5),
# nn.ReLU(),
# nn.Linear(5, 1),
# nn.Tanh(),
# SoftClip(50)
# # nn.Sigmoid()
# ).to(DEVICE)
self._set_weights()
self.conf = nn_conf
self.id = OrderCounter.next()
......
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