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  Model summary in pytorch 29



  pytorch



  
  answered by
  
    SpiderWasp42
  
  on 02:55AM - 06 Mar 17


  
  
    
    
  
  


A result (in my case):
:Sequential (
  (0): Linear (29 -> 1024), weights=((1024L, 29L), (1024L,)), parameters=30720
  (1): Dropout (p = 0.05), weights=(), parameters=0
  (2): Tanh (), weights=(), parameters=0
  (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True), weights=((1024L,), (1024L,)), parameters=2048
  (4): Linear (1024 -> 128), weights=((128L, 1024L), (128L,)), parameters=131200
  (5): Dropout (p = 0.05), weights=(), parameters=0
  (6): Tanh (), weights=(), parameters=0
  (7): Linear (128 -> 64), weights=((64L, 128L), (64L,)), parameters=8256
  (8): Dropout (p = 0.05), weights=(), parameters=0
  (9): LeakyReLU (0.01), weights=(), parameters=0
  (10): Linear (64 -> 32), weights=((32L, 64L), (32L,)), parameters=2080
  (11): Dropout (p = 0.05), weights=(), parameters=0
  (12): Tanh (), weights=(), parameters=0
  (13): Linear (32 -> 16), weights=((16L, 32L), (16L,)), parameters=528
  (14): Dropout (p = 0.05), weights=(), parameters=0
  (15): LeakyReLU (0.01), weights=(), parameters=0
  (16): Linear (16 -> 1), weights=((1L, 16L), (1L,)), parameters=17
  (17): Sigmoid (), weights=(), parameters=0
)