74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
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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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from tools import utils, Train, Loader, WebVisual
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class MINISTCNN(UniModule.ModuleBase):
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def __init__(self):
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super(MINISTCNN, self).__init__()
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self.layer1 = nn.Sequential( # 28-4=24 /2=12
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nn.Conv2d(1, 16, kernel_size=5),
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nn.BatchNorm2d(16),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2))
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self.layer2 = nn.Sequential( # 12-2=10 /2=5
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nn.Conv2d(16, 32, kernel_size=3),
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nn.BatchNorm2d(32),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2))
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self.layer3 = nn.Sequential( # 5-2=3
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nn.Conv2d(32, 64, kernel_size=3),
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nn.BatchNorm2d(64),
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nn.ReLU()
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)
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self.fc = nn.Sequential(
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nn.Linear(64*3*3, 512),
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# nn.ReLU(),
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nn.Linear(512, 10))
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def forward(self, x):
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return F.log_softmax(x, dim=1)
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WebVisual.InitVisdom()
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window = WebVisual.LineWin()
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linePre = WebVisual.Line(window, "linePre")
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traindata, testdata = Loader.MNIST(
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32, num_workers=4, trainsize=60000, shuffle=True)
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modelMinist = utils.SetDevice(MINISTCNN())
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# model = utils.LoadModel(model, CurrentPath+"/checkpointEntropySearch.pkl")
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optimizer = optim.SGD(modelMinist.parameters(), lr=0.0005)
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Train.TrainEpochs(modelMinist, traindata, optimizer,
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testdata, 100, 1, linePre)
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utils.SaveModel(modelMinist, CurrentPath+"/checkpointMnistTrain.pkl")
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