pip install pytorchsummary
from torch import nn
from pytorchsummary import parameter_summary
class CNNET (nn .Module ):
def __init__ (self ):
super (CNNET ,self ).__init__ ()
self .layer = nn .Sequential (
nn .Conv2d (3 ,16 ,5 ), # 28-5+1
nn .ReLU (), #24
nn .MaxPool2d (2 ,2 ), # 12
nn .Conv2d (16 ,32 ,3 ), # 12+1-3
nn .ReLU (), # 10
nn .MaxPool2d (2 ,2 ), # 5
nn .Conv2d (32 ,64 ,5 ), # 11-3+1
nn .ReLU (),
nn .Conv2d (64 ,10 ,1 )
)
def forward (self ,x ):
x = self .layer (x )
return x
m = CNNET ()
parameter_summary (m ,False )
for i ,j in enumerate (m .parameters ()):
if i == 2 :
break
j .requires_grad = False
# parameter_summary(model=m,border=False)
# if border set to True then it will print
# the lines in between every layer
LAYER TYPE KERNEL SHAPE #parameters (weights+bias) requires_grad
____________________________________________________________________________________________________
Conv2d-1 [16, 3, 5, 5] 1,216 (1200 + 16) False False
ReLU-2 - - -
MaxPool2d-3 - - -
Conv2d-4 [32, 16, 3, 3] 4,640 (4608 + 32) True True
ReLU-5 - - -
MaxPool2d-6 - - -
Conv2d-7 [64, 32, 5, 5] 51,264 (51200 + 64) True True
ReLU-8 - - -
Conv2d-9 [10, 64, 1, 1] 650 (640 + 10) True True
====================================================================================================
Total parameters 57,770
Total Non-Trainable parameters 1,216
Total Trainable parameters 56,554
57770
from torchvision import models
from pytorchsummary import parameter_summary
m = models .alexnet (False )
parameter_summary (m )
# this function returns the total number of
# parameters (int) in a model
LAYER TYPE KERNEL SHAPE #parameters (weights+bias) requires_grad
____________________________________________________________________________________________________
____________________________________________________________________________________________________
Conv2d-1 [64, 3, 11, 11] 23,296 (23232 + 64) True True
____________________________________________________________________________________________________
ReLU-2 - - -
____________________________________________________________________________________________________
MaxPool2d-3 - - -
____________________________________________________________________________________________________
Conv2d-4 [192, 64, 5, 5] 307,392 (307200 + 192) True True
____________________________________________________________________________________________________
ReLU-5 - - -
____________________________________________________________________________________________________
MaxPool2d-6 - - -
____________________________________________________________________________________________________
Conv2d-7 [384, 192, 3, 3] 663,936 (663552 + 384) True True
____________________________________________________________________________________________________
ReLU-8 - - -
____________________________________________________________________________________________________
Conv2d-9 [256, 384, 3, 3] 884,992 (884736 + 256) True True
____________________________________________________________________________________________________
ReLU-10 - - -
____________________________________________________________________________________________________
Conv2d-11 [256, 256, 3, 3] 590,080 (589824 + 256) True True
____________________________________________________________________________________________________
ReLU-12 - - -
____________________________________________________________________________________________________
MaxPool2d-13 - - -
____________________________________________________________________________________________________
AdaptiveAvgPool2d-14 - - -
____________________________________________________________________________________________________
Dropout-15 - - -
____________________________________________________________________________________________________
Linear-16 [4096, 9216] 37,752,832 (37748736 + 4096) True True
____________________________________________________________________________________________________
ReLU-17 - - -
____________________________________________________________________________________________________
Dropout-18 - - -
____________________________________________________________________________________________________
Linear-19 [4096, 4096] 16,781,312 (16777216 + 4096) True True
____________________________________________________________________________________________________
ReLU-20 - - -
____________________________________________________________________________________________________
Linear-21 [1000, 4096] 4,097,000 (4096000 + 1000) True True
====================================================================================================
Total parameters 61,100,840
Total Non-Trainable parameters 0
Total Trainable parameters 61,100,840
