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Author: as791

setup_cifar10_model.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchsummary import summary
+from torchvision import transforms,datasets
+
+
+class CIFAR10(nn.Module):
+  def __init__(self):
+    super(CIFAR10, self).__init__()
+    self.conv1 = nn.Conv2d(3, 64, 3, 1)
+    self.conv2 = nn.Conv2d(64, 64, 3, 1)
+    self.conv3= nn.Conv2d(64, 128, 3, 1)
+    self.conv4= nn.Conv2d(128, 128, 3, 1)
+    self.dropout1 = nn.Dropout2d(0.5)
+    self.fc1 = nn.Linear(3200, 256)
+    self.fc2 = nn.Linear(256, 256)
+    self.fc3 = nn.Linear(256, 10)
+
+  def forward(self, x):
+    x = self.conv1(x)   
+    x = F.relu(x)
+    x = self.conv2(x)   
+    x = F.relu(x)
+    x = F.max_pool2d(x, 2)  
+    x = self.conv3(x)       
+    x = F.relu(x)
+    x = self.conv4(x)
+    x = F.relu(x)
+    x = F.max_pool2d(x, 2)  
+    x = torch.flatten(x,1)  
+    x = self.fc1(x)         
+    x = F.relu(x)
+    x = self.dropout1(x)
+    x = self.fc2(x)
+    x = F.relu(x)
+    x = self.fc3(x)        
+    return x
+
+def fit(model,device,train_loader,val_loader,optimizer,criterion,epochs):
+  data_loader = {'train':train_loader,'val':val_loader}
+  print("Fitting the model...")
+  train_loss,val_loss=[],[]
+  train_acc,val_acc=[],[]
+  for epoch in range(epochs):
+    loss_per_epoch,val_loss_per_epoch=0,0
+    acc_per_epoch,val_acc_per_epoch,total,val_total=0,0,0,0
+    for phase in ('train','val'):
+      for i,data in enumerate(data_loader[phase]):
+        inputs,labels  = data[0].to(device),data[1].to(device)
+        outputs = model(inputs)
+        #preding classes of one batch
+        preds = torch.max(outputs,1)[1]
+        #calculating loss on the output of one batch
+        loss = criterion(outputs,labels)
+        if phase == 'train':
+          acc_per_epoch+=(labels==preds).sum().item()
+          total+= labels.size(0)
+          optimizer.zero_grad()
+          #grad calc w.r.t Loss func
+          loss.backward()
+          #update weights
+          optimizer.step()
+          loss_per_epoch+=loss.item()
+        else:
+          val_acc_per_epoch+=(labels==preds).sum().item()
+          val_total+=labels.size(0)
+          val_loss_per_epoch+=loss.item()
+    print("Epoch: {} Loss: {:0.6f} Acc: {:0.6f} Val_Loss: {:0.6f} Val_Acc: {:0.6f}".format(epoch+1,loss_per_epoch/len(train_loader),acc_per_epoch/total,val_loss_per_epoch/len(val_loader),val_acc_per_epoch/val_total))
+    train_loss.append(loss_per_epoch/len(train_loader))
+    val_loss.append(val_loss_per_epoch/len(val_loader))
+    train_acc.append(acc_per_epoch/total)
+    val_acc.append(val_acc_per_epoch/val_total)
+  return train_loss,val_loss,train_acc,val_acc
+
+if __name__=='__main__':
+  np.random.seed(42)
+  torch.manual_seed(42)
+
+  transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5), (1.0,1.0,1.0))])
+  dataset = datasets.CIFAR10(root = './data', train=True, transform = transform, download=True)
+  train_set, val_set = torch.utils.data.random_split(dataset, [45000, 5000])
+  train_loader = torch.utils.data.DataLoader(train_set,batch_size=128,shuffle=True)
+  val_loader = torch.utils.data.DataLoader(val_set,batch_size=128,shuffle=True)
+  
+  use_cuda=True
+  device = torch.device("cuda" if (use_cuda and torch.cuda.is_available()) else "cpu")
+
+  model = CIFAR10().to(device)
+  summary(model,(3,32,32))
+
+  optimizer = optim.SGD(model.parameters(),lr=0.01, momentum=0.9, nesterov=True, weight_decay=1e-6)
+  criterion = nn.CrossEntropyLoss()
+
+  train_loss,val_loss,train_acc,val_acc=fit(model,device,train_loader,val_loader,optimizer,criterion,50)
+
+  fig = plt.figure(figsize=(5,5))
+  plt.plot(np.arange(1,51), train_loss, "*-",label="Training Loss")
+  plt.plot(np.arange(1,51), val_loss,"o-",label="Val Loss")
+  plt.xlabel("Num of epochs")
+  plt.legend()
+  plt.savefig('cifar10_model_loss_event.png')
+
+  fig = plt.figure(figsize=(5,5))
+  plt.plot(np.arange(1,51), train_acc, "*-",label="Training Acc")
+  plt.plot(np.arange(1,51), val_acc,"o-",label="Val Acc")
+  plt.xlabel("Num of epochs")
+  plt.legend()
+  plt.savefig('cifar10_model_accuracy_event.png')
+
+  torch.save(model.state_dict(),'./models/cifar10_model.pt')

setup_mnist_model.py

@@ -0,0 +1,113 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchsummary import summary
+from torchvision import transforms,datasets
+
+class MNIST(nn.Module):
+  def __init__(self):
+    super(MNIST, self).__init__()
+    self.conv1 = nn.Conv2d(1, 32, 3, 1)
+    self.conv2 = nn.Conv2d(32, 32, 3, 1)
+    self.conv3= nn.Conv2d(32, 64, 3, 1)
+    self.conv4= nn.Conv2d(64, 64, 3, 1)
+    self.dropout1 = nn.Dropout2d(0.5)
+    self.fc1 = nn.Linear(1024, 200)
+    self.fc2 = nn.Linear(200, 200)
+    self.fc3 = nn.Linear(200, 10)
+
+  def forward(self, x):
+    x = self.conv1(x)   
+    x = F.relu(x)
+    x = self.conv2(x)   
+    x = F.relu(x)
+    x = F.max_pool2d(x, 2)  
+    x = self.conv3(x)       
+    x = F.relu(x)
+    x = self.conv4(x)
+    x = F.relu(x)
+    x = F.max_pool2d(x, 2)  
+    x = torch.flatten(x,1)  
+    x = self.fc1(x)         
+    x = F.relu(x)
+    x = self.dropout1(x)
+    x = self.fc2(x)
+    x = F.relu(x)
+    x = self.fc3(x)        
+    return x
+
+def fit(model,device,train_loader,val_loader,optimizer,criterion,epochs):
+  data_loader = {'train':train_loader,'val':val_loader}
+  print("Fitting the model...")
+  train_loss,val_loss=[],[]
+  train_acc,val_acc=[],[]
+  for epoch in range(epochs):
+    loss_per_epoch,val_loss_per_epoch=0,0
+    acc_per_epoch,val_acc_per_epoch,total,val_total=0,0,0,0
+    for phase in ('train','val'):
+      for i,data in enumerate(data_loader[phase]):
+        inputs,labels  = data[0].to(device),data[1].to(device)
+        outputs = model(inputs)
+        #preding classes of one batch
+        preds = torch.max(outputs,1)[1]
+        #calculating loss on the output of one batch
+        loss = criterion(outputs,labels)
+        if phase == 'train':
+          acc_per_epoch+=(labels==preds).sum().item()
+          total+= labels.size(0)
+          optimizer.zero_grad()
+          #grad calc w.r.t Loss func
+          loss.backward()
+          #update weights
+          optimizer.step()
+          loss_per_epoch+=loss.item()
+        else:
+          val_acc_per_epoch+=(labels==preds).sum().item()
+          val_total+=labels.size(0)
+          val_loss_per_epoch+=loss.item()
+    print("Epoch: {} Loss: {:0.6f} Acc: {:0.6f} Val_Loss: {:0.6f} Val_Acc: {:0.6f}".format(epoch+1,loss_per_epoch/len(train_loader),acc_per_epoch/total,val_loss_per_epoch/len(val_loader),val_acc_per_epoch/val_total))
+    train_loss.append(loss_per_epoch/len(train_loader))
+    val_loss.append(val_loss_per_epoch/len(val_loader))
+    train_acc.append(acc_per_epoch/total)
+    val_acc.append(val_acc_per_epoch/val_total)
+  return train_loss,val_loss,train_acc,val_acc
+
+if __name__=='__main__':
+  np.random.seed(42)
+  torch.manual_seed(42)
+
+  transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
+  dataset = datasets.MNIST(root = './data', train=True, transform = transform, download=True)
+  train_set, val_set = torch.utils.data.random_split(dataset, [55000, 5000])
+  train_loader = torch.utils.data.DataLoader(train_set,batch_size=128,shuffle=True)
+  val_loader = torch.utils.data.DataLoader(val_set,batch_size=128,shuffle=True)
+  
+  use_cuda=True
+  device = torch.device("cuda" if (use_cuda and torch.cuda.is_available()) else "cpu")
+
+  model = MNIST().to(device)
+  summary(model,(1,28,28))
+
+  optimizer = optim.SGD(model.parameters(),lr=0.01, momentum=0.9, nesterov=True, weight_decay=1e-6)
+  criterion = nn.CrossEntropyLoss()
+
+  train_loss,val_loss,train_acc,val_acc=fit(model,device,train_loader,val_loader,optimizer,criterion,50)
+
+  fig = plt.figure(figsize=(5,5))
+  plt.plot(np.arange(1,51), train_loss, "*-",label="Training Loss")
+  plt.plot(np.arange(1,51), val_loss,"o-",label="Val Loss")
+  plt.xlabel("Num of epochs")
+  plt.legend()
+  plt.savefig('mnist_model_loss_event.png')
+
+  fig = plt.figure(figsize=(5,5))
+  plt.plot(np.arange(1,51), train_acc, "*-",label="Training Acc")
+  plt.plot(np.arange(1,51), val_acc,"o-",label="Val Acc")
+  plt.xlabel("Num of epochs")
+  plt.legend()
+  plt.savefig('mnist_model_accuracy_event.png')
+
+  torch.save(model.state_dict(),'./models/mnist_model.pt')