diff --git a/Notes.txt b/Notes.txt index d494b1b8bfbc66ad7267f4de64a1f8568841e3fb..82050237b5ac0e9e1fac7a2e7ead79ee6fdb1d3a 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 2242a6041faa0f2794e6bbfa00500a103eec5487..536127b8be1a52685401165f8480dff504055146 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