add MnistDemo

MnistDemo/Minist.py

@@ -0,0 +1,73 @@
+from __future__ import print_function
+
+import os
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision
+from torchvision import datasets, transforms
+import torchvision.models as models
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
+sys.path.append(CurrentPath+'../tools')
+sys.path.append(CurrentPath+'../')
+
+from tools import UniModule
+from tools import utils, Train, Loader, WebVisual
+
+
+class MINISTCNN(UniModule.ModuleBase):
+    def __init__(self):
+        super(MINISTCNN, self).__init__()
+        self.layer1 = nn.Sequential(  # 28-4=24  /2=12
+            nn.Conv2d(1, 16, kernel_size=5),
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layer2 = nn.Sequential(  # 12-2=10  /2=5
+            nn.Conv2d(16, 32, kernel_size=3),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layer3 = nn.Sequential(  # 5-2=3
+            nn.Conv2d(32, 64, kernel_size=3),
+            nn.BatchNorm2d(64),
+            nn.ReLU()
+        )
+        self.fc = nn.Sequential(
+            nn.Linear(64*3*3, 512),
+            # nn.ReLU(),
+            nn.Linear(512, 10))
+
+    def forward(self, x):
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return F.log_softmax(x, dim=1)
+
+
+
+WebVisual.InitVisdom()
+window = WebVisual.LineWin()
+linePre = WebVisual.Line(window, "linePre")
+
+traindata, testdata = Loader.MNIST(
+    32, num_workers=4, trainsize=60000, shuffle=True)
+
+modelMinist = utils.SetDevice(MINISTCNN())
+
+# model = utils.LoadModel(model, CurrentPath+"/checkpointEntropySearch.pkl")
+
+optimizer = optim.SGD(modelMinist.parameters(), lr=0.0005)
+Train.TrainEpochs(modelMinist, traindata, optimizer,
+                  testdata, 100, 1, linePre)
+utils.SaveModel(modelMinist, CurrentPath+"/checkpointMnistTrain.pkl")
+
+