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timeSeriesConfig.py
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timeSeriesConfig.py
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# These are the parameters for the neural network.
# The subnetworks are named by x,y,z.
# Differential indicates how many times the finite difference is applied to the layers.
# 6 Layers has pooling in the forth.
cnnmodel = dict(
batch_size=128,
image_size=(500, 3),
validation_split=0.1,
l1=0.001,
l2=0.01,
epochs=10 ** 5,
learning_rate=1e-5,
filters=64,
kernel_size=3,
pooling_size=3,
activation="swish",
padding="causal",
dropout_rate=0.5,
layers_x=42,
layers_y=42,
layers_z=42,
differential_x=1,
differential_y=1,
differential_z=1,
dropouts_x=[],
dropouts_y=[],
dropouts_z=[],
name_x="raw",
name_y="betti1",
name_z="betti2",
max_pool_x=[],
max_pool_y=[],
max_pool_z=[],
avg_pool_x=[],
avg_pool_y=[],
avg_pool_z=[],
)
lstmmodel = dict(
units=32,
l1=0.001,
l2=0.01,
pooling_size=5,
dropout_rate=0.5,
return_state=False,
go_backwards=False,
stateful=False,
differential=1,
layers=22,
name_a="CuDNNLSTM",
max_pool=[],
avg_pool=[],
)
paths = dict(
general="/home/goku/Dokumente/siemens_powerplant_samples",
files="/home/goku/Dokumente/siemens_powerplant_samples/powerplant",
target="/home/goku/Dokumente/siemens_powerplant_samples/powerplant_slidingwindow",
pershom="/home/goku/Dokumente/siemens_powerplant_samples/powerplant_pershom",
silhouette="/home/goku/Dokumente/siemens_powerplant_samples/powerplant_silhouette",
betticurve="/home/goku/Dokumente/siemens_powerplant_samples/powerplant_betticurve",
heatkernels="/home/goku/Dokumente/siemens_powerplant_samples/powerplant_heatkernels",
split_analysed="/home/goku/Dokumente/siemens_powerplant_samples/signaldaten_ordner_gruppiert_analysiert",
split_ordered="/home/goku/Dokumente/siemens_powerplant_samples/signaldaten_ordner_gruppiert_aufgeräumt",
split="/home/goku/Dokumente/siemens_powerplant_samples/signaldaten_ordner_gruppiert",
data="/home/goku/Dokumente/TwirlFlake/data/",
images="/home/goku/Dokumente/TwirlFlake/images/",
test="/home/goku/Dokumente/TwirlFlake/test/",
)
# These are the categories of the signals.
# Can be completed but is not used for classifications.
# They are used for plotting analysis only.
pptcat = [
"EKT20",
"EKT30",
"MBP",
"MBR10",
"MBA26",
"MBA12",
"MBY",
"MBA11",
"MBL",
"MKC",
"BBT",
"BAT",
"HBK10",
"LBA15",
"LAE20",
"LBA10",
"HAH10",
"HAH20",
"HAJ50",
"HAJ60",
"LAE10",
"HAH15",
"HAH10",
"HAC30",
"HAC20",
"HAD30",
"HAH50",
"LAE01",
"HAD50",
"HAH80",
"LBA15",
"HAC50",
"HAC10",
"HAD80",
"HAA10",
"HAC60",
"LBA80",
"LAB60",
"LAB30",
"LAB90",
"LCA40",
"LCA60",
"LCA30",
"LCA70",
"LBB40",
"LAH",
"LBB45",
"LAF10",
"LAF01",
"LAF20",
"LBC45",
"10LBC40",
"10MAA50",
"10MAA10-20",
"10LBA20",
"10MAB10-20",
"10LBB50",
"10MAB50",
"10MAC10-20",
"10MAC40-45",
"10MAC50",
"10MAG",
"10MKY",
"10MKC",
"10BAT",
"PAB30",
"PAB20",
"LCB",
"LCA10",
"10LCA20",
"LBA90",
"LAC",
]