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MLOspiderboost.py

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+from __future__ import print_function
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import numpy as np
+from matplotlib import pyplot as plt
+
+class LeNet5(nn.Module):
+
+    def __init__(self):
+        super(LeNet5, self).__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5, 1)
+        self.conv2 = nn.Conv2d(6, 16, 5, 1)
+        self.fc1 = nn.Linear(256, 120)
+        self.fc2 = nn.Linear(120,84)
+        self.fc3 = nn.Linear(84,10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 256)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return F.log_softmax(x, dim=1)
+
+class Net_FC(nn.Module):
+    def __init__(self):
+        super(Net_FC, self).__init__()
+        self.fc1 = nn.Linear(784, 500)
+        self.fc2 = nn.Linear(500, 10)
+
+    def forward(self, x):
+        x = x.view(-1, 784)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+def train_momentum_spiderboost(model, device, train_loader_xi, optimizer, epoch, b_sz):
+    model.train()
+
+    q = 64
+    B = 64
+    eta = 0.05
+    v2 = {}
+    v = {}
+    v3 = {}
+    iter = 0
+    lmbda = 0.00001
+    for batch_idx, (data, target) in enumerate(train_loader_xi):
+
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+
+        if iter % q == 0:
+            for name, p in model.named_parameters():
+                v[name] = p.grad.data
+                v2[name] = p.data       
+        else:
+            #v1 is storing gradients, v2 i storing old_x and v3 is storing current_x
+            v1 = {}
+            for p in model.parameters():
+                p.grad.data.add_(p.grad.data)
+
+            for name, p in model.named_parameters():
+                v1[name] = p.grad.data
+                v3[name] = p.data
+                p.data.copy_(v2[name])
+
+            data = data.to(device)  
+            optimizer.zero_grad()
+            output = model(data)
+            loss = F.nll_loss(output, target)
+            loss.backward()
+
+            for p in model.parameters():
+                p.grad.data.add_(p.grad.data)
+
+            for name, p in model.named_parameters():
+                    v1[name].add_(-p.grad.data)
+                    v1[name].add_(v[name])
+            v = v1
+
+            for name, p in model.named_parameters():
+                v[name].mul_(1/B)
+
+            for name, p in model.named_parameters():
+                p.data.copy_(v3[name])
+
+            data = data.to(device)  
+            optimizer.zero_grad()
+            output = model(data)
+            loss = F.nll_loss(output, target)
+            loss.backward()
+
+        for name, p in model.named_parameters():
+            p.data.add_(-eta*v[name])
+            p.data = torch.sign(p.data) * torch.maximum(torch.abs(p.data)-lmbda,\
+                    torch.zeros(p.data.size()))
+        v2 = v3  
+
+        iter +=1 
+
+        if batch_idx % 200 == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader_xi.dataset),
+                100. * batch_idx / len(train_loader_xi), loss.item()))
+
+def test(model, device, test_loader,y,epoch):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
+            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+
+    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset),
+        100. * correct / len(test_loader.dataset)))
+    y[epoch] = 100. * correct / len(test_loader.dataset)
+
+def modelSparsity(model):
+    nonzeros = 0
+    parameterCount = 0
+
+    for p in model.parameters():
+        for data in torch.flatten(p.data):
+            if(data!=0):nonzeros+=1
+            parameterCount += 1
+
+    return 1 - (nonzeros/parameterCount)
+
+def stationarityViolation(model):
+    lmbda = 0.00001
+    gradient = torch.tensor(())
+    for p in model.parameters():
+        data = torch.flatten(p.data)
+        grad = torch.flatten(p.grad.data)
+        gradient = torch.cat((gradient,(torch.sign(data-grad)*torch.maximum\
+                              (torch.zeros(data.size()), torch.abs(data-grad)-lmbda))))
+    return float(torch.linalg.norm(grad))    
+
+def main():
+    # Training settings
+    use_cuda = False # if your machine has CUDA-compatible GPU, you can change this to True
+
+    torch.manual_seed(20200930)
+
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+
+    train_loader_xi = torch.utils.data.DataLoader(
+        datasets.FashionMNIST('data', train=True, download=True,
+                       transform=transforms.Compose([
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.1307,), (0.3081,))
+                       ])),
+        batch_size=64, shuffle=True, **kwargs)
+
+
+    test_loader = torch.utils.data.DataLoader(
+        datasets.FashionMNIST('data', train=False, transform=transforms.Compose([
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.1307,), (0.3081,))
+                       ])),
+        batch_size=1000, shuffle=True, **kwargs)
+
+    #model = Net_FC().to(device)
+
+    model = LeNet5().to(device)
+
+    optimizer = optim.SGD(model.parameters(), lr=0.01)
+
+    iter = 0
+
+    b_sz = 64
+
+    totalEpochs = 15
+    x = np.zeros(totalEpochs)
+    y = np.zeros(totalEpochs)
+    z = np.zeros(totalEpochs)
+    w = np.zeros(totalEpochs)
+    for epoch in range(totalEpochs):
+        #train(model, device, train_loader, optimizer, epoch, iter)
+        train_momentum_spiderboost(model, device, 
+                       train_loader_xi, optimizer, epoch, b_sz)
+        test(model, device, test_loader,y,epoch)
+        iter += 60000//64
+
+        x[epoch] = epoch
+        z[epoch] = modelSparsity(model)
+        w[epoch] = stationarityViolation(model)
+
+    fig, axs = plt.subplots(2,1)
+    fig.suptitle("Accuracy, sparsity and violation")
+    axs[0].plot(x, y, '+') 
+    plt.ylim(75,90)
+    axs[1].plot(x, z, '*')  
+    #axs[2].plot(x, w, '+') 
+
+
+if __name__ == '__main__':
+    main()