From 6b0f459345f805cfb432e6e3c055abc70e38bb04 Mon Sep 17 00:00:00 2001
From: "wasels.chr" <wasels.chr@gmail.com>
Date: Wed, 2 Mar 2022 17:45:58 +0100
Subject: [PATCH] Modification of V13
---
Notes.txt | 2 +-
UNet/UNet_V13.py | 6 +++---
2 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/Notes.txt b/Notes.txt
index d494b1b..8205023 100644
--- a/Notes.txt
+++ b/Notes.txt
@@ -8,7 +8,7 @@ V9: 3 layer, doppelte depth Conv pro layer, norm. Daten,kernel 5, phase only
V10: 3 layer, eine Conv pro layer, norm Daten, phase only
V11: 3 layer, doppel Conv, normDaten, phase + angle
V12: 3 layer, doppel Conv, normDataen,phase 64
-V13: 4 layer, doppel Conv, normDataen,phase 64
+V13: 4 layer, doppel Conv, normDataen,angles 64
V14: 4 layer, single conv, normDataen,phase + angle 64
V15: 3 layer, doppelte depth Conv pro layer, norm. Daten,kernel 7, phase only, dropout 0.3, 32
V16: 3 layer, doppelte depth Conv pro layer, norm. Daten,kernel 7, angelsonly, dropout 0.5, 32
diff --git a/UNet/UNet_V13.py b/UNet/UNet_V13.py
index 2242a60..536127b 100644
--- a/UNet/UNet_V13.py
+++ b/UNet/UNet_V13.py
@@ -134,7 +134,7 @@ def accuracy(outputs, labels,normalization, threshold = 0.05):
return percentage
class UNet(UNetBase):
- def __init__(self,kernel_size = 5, enc_chs=((6,6,16), (16,16,32), (32,32,64), (64,128,128)), dec_chs_up=(128, 128, 64, 32), dec_chs_conv=((192,128,128),(160,64,64),(80,32,32),(38,16,1)),normalization=np.array([0,1])):
+ def __init__(self,kernel_size = 7, enc_chs=((6,16,32), (32,32,64), (64,64,128), (128,128,256)), dec_chs_up=(256,256, 128, 64), dec_chs_conv=((384,256,256),(320,128,128),(160,64,64),(70,32,32)),normalization=np.array([0,1])):
super().__init__()
self.encoder = Encoder(kernel_size = kernel_size, chs = enc_chs)
self.decoder = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv)
@@ -225,8 +225,8 @@ if __name__ == '__main__':
path_to_rep = '/home/yk138599/Hiwi/damask3'
use_seeds = False
seed = 373686838
- num_epochs = 300
- b_size = 10
+ num_epochs = 200
+ b_size = 16
opt_func = torch.optim.Adam
lr = 0.00003
kernel = 7
--
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