davrot/kk_contour_net_shallow
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Die negative slope der Leaky ReLU wird nun schrittweise verkleinert, anstatt sie direkt auf 0.0 zu setzen.
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katharinakorb 2023-08-04 08:50:01 +02:00 committed by GitHub
parent c4a1737fa7
commit 81aea7fecd
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1 changed files with 12 additions and 4 deletions
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16
Classic_contour_net_shallow/cnn_training.py
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@ -31,6 +31,7 @@ def main(
with open(config_filenname, "r") as file_handle: with open(config_filenname, "r") as file_handle:
file_contents = file_handle.read() file_contents = file_handle.read()
f_contents = jsmin(file_contents) f_contents = jsmin(file_contents)
print(f_contents)
config = json.loads(f_contents) config = json.loads(f_contents)
# config = json.loads(jsmin(file_handle.read())) # config = json.loads(jsmin(file_handle.read()))
@ -134,9 +135,12 @@ def run_network(
device: torch.device = torch.device(device_str) device: torch.device = torch.device(device_str)
torch.set_default_dtype(torch.float32) torch.set_default_dtype(torch.float32)
# switch to relu if using leaky relu # switch to relu if using leaky relu (not switched yet)
switched_to_relu: bool = False switched_to_relu: bool = False
# get initial leaky slope:
leaky_slope = leak_relu_negative_slope
# ------------------------------------------------------------------- # -------------------------------------------------------------------
logger.info("-==- START -==-") logger.info("-==- START -==-")
@ -372,14 +376,18 @@ def run_network(
if round(previous_test_acc, precision_100_percent) == 100.0: if round(previous_test_acc, precision_100_percent) == 100.0:
if activation_function == "leaky relu": if activation_function == "leaky relu":
if switch_leakyR_to_relu and not switched_to_relu: if switch_leakyR_to_relu and not switched_to_relu:
leaky_slope /= 10
logger.info( logger.info(
"100% test performance reached. Switching to LeakyReLU slope 0.0." f"100% test performance reached. Decreasing LeakyReLU slope to {leaky_slope}."
) )
for name, module in model.named_children(): for name, module in model.named_children():
if isinstance(module, torch.nn.LeakyReLU): if isinstance(module, torch.nn.LeakyReLU):
module.negative_slope = 0.0 module.negative_slope = leaky_slope
logger.info(model) logger.info(model)
switched_to_relu = True
if leaky_slope <= 1e-5:
switched_to_relu = True
activation_function = "relu"
else: else:
logger.info("100% test performance reached. Stop training.") logger.info("100% test performance reached. Stop training.")
break break