96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
from __future__ import print_function
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import os
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import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision
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from torchvision import datasets, transforms
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import torchvision.models as models
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import matplotlib.pyplot as plt
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import numpy as np
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CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
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sys.path.append(CurrentPath+'../tools')
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sys.path.append(CurrentPath+'../')
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from tools import UniModule
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class Net535(UniModule.ModuleBase):
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def __init__(self):
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super(Net535, self).__init__()
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layers = []
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layers += [nn.Conv2d(1, 8, kernel_size=5,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 10, kernel_size=5,bias=False)]
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self.features = nn.Sequential(*layers)
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def forward(self, x):
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x = self.features(x)
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x = x.view(-1, 1*10)
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return F.log_softmax(x, dim=1)
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class Net5Grad35(UniModule.ModuleBase):
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def __init__(self):
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super(Net5Grad35, self).__init__()
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layers = []
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layers += [nn.Conv2d(1, 8, kernel_size=5,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 10, kernel_size=5,bias=False)]
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self.features = nn.Sequential(*layers)
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self.SetConvRequiresGrad(0,False)
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def forward(self, x):
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x = self.features(x)
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x = x.view(-1, 1*10)
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return F.log_softmax(x, dim=1)
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class Net3335(UniModule.ModuleBase):
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def __init__(self):
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super(Net3335, self).__init__()
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layers = []
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layers += [nn.Conv2d(1, 8, kernel_size=3,bias=False,padding=1),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 10, kernel_size=5,bias=False)]
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self.features = nn.Sequential(*layers)
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def forward(self, x):
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x = self.features(x)
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x = x.view(-1, 1*10)
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return F.log_softmax(x, dim=1)
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class Net3Grad335(UniModule.ModuleBase):
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def __init__(self):
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super(Net3Grad335, self).__init__()
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layers = []
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layers += [nn.Conv2d(1, 8, kernel_size=3,bias=False,padding=1),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 10, kernel_size=5,bias=False)]
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self.features = nn.Sequential(*layers)
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self.SetConvRequiresGrad(0,False)
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def forward(self, x):
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x = self.features(x)
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x = x.view(-1, 1*10)
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return F.log_softmax(x, dim=1)
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class Net31535(UniModule.ModuleBase):
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def __init__(self):
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super(Net31535, self).__init__()
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layers = []
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layers += [nn.Conv2d(1, 8, kernel_size=[1,3],bias=False,padding=[0,1]),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=5,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
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layers += [nn.Conv2d(8, 10, kernel_size=5,bias=False)]
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self.features = nn.Sequential(*layers)
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def forward(self, x):
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x = self.features(x)
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x = x.view(-1, 1*10)
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return F.log_softmax(x, dim=1)
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