Day33 参加职训(机器学习与资料分析工程师培训班)，网站设计与网页工程技术

上午: AIoT资料分析应用系统框架设计与实作

今日运用Django架设Framework，只完成一部份，下次课程会继续完成，画面如下:

下午: Pytroch与深度学习初探
使用CNN建构网路，预测MNIST

######################### step1: load data (generate) ############import torchimport torch.nn as nnimport torch.nn.functional as F import matplotlib.pyplot as pltimport numpy as npfrom torchvision import datasets, transforms#Assign cuda GPU located at location '0' to a variable# device= torch.device('cuda:0')print(torch.cuda.device_count())print(torch.cuda.get_device_name(0))device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")print(device)t1=transforms.Resize((28,28))t2=transforms.ToTensor()t3=transforms.Normalize((0.5,),(0.5,))transform=transforms.Compose([t1,t2,t3])train_data= datasets.MNIST(root='./data',train=True, download=True, transform=transform)validate_data=datasets.MNIST(root='./data',train=False, download=True, transform=transform)print(len(train_data),type(train_data))print(len(validate_data),type(validate_data))train_loader=torch.utils.data.DataLoader(train_data, batch_size=100,shuffle=True)validation_loader=torch.utils.data.DataLoader(validate_data,batch_size=100,shuffle=False)print(len(train_loader),type(train_loader))print(len(validation_loader),type(validation_loader))

def im_convert(tensor):  image = tensor.clone().detach().numpy()  image = image.transpose(1, 2, 0)  image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5))  image = image.clip(0, 1)  return image  dataiter = iter(train_loader)images, labels = dataiter.next()fig = plt.figure(figsize=(25, 4))for idx in np.arange(20):  ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])  plt.imshow(im_convert(images[idx]))  ax.set_title([labels[idx].item()])

########################### step3: build model ############class LeNet(nn.Module):  def __init__(self):    super().__init__()    self.conv1 = nn.Conv2d(1, 20, 5, 1)    self.conv2 = nn.Conv2d(20, 50, 5, 1)    self.fc1 = nn.Linear(4*4*50, 500)    self.dropout1 = nn.Dropout(0.5)    self.fc2 = nn.Linear(500, 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, 4*4*50)    x = F.relu(self.fc1(x))    x = self.dropout1(x)    x = self.fc2(x)    return xmodel=LeNet().to(device)print(model)# class myDNN(nn.Module):#  def __init__(self,numIn,numH1,numH2,numOut):#   super(myDNN,self).__init__()#   self.layer1=torch.nn.Linear(numIn,numH1)#   self.layer2=torch.nn.Linear(numH1,numH2)#   self.layer3=torch.nn.Linear(numH2,numOut)#  def forward(self,x):#   x=F.relu(self.layer1(x))#   x=F.relu(self.layer2(x))#   yProb=self.layer3(x)#   return yProb# model=myDNN(784,256,64,10)#backward pathcriterion= nn.CrossEntropyLoss()optimizer=torch.optim.Adam(model.parameters(),lr=0.0001)#advance 2:lr=0.0001

############################ step4: traing model############epochs = 15running_loss_history = []running_corrects_history = []val_running_loss_history = []val_running_corrects_history = []for e in range(epochs):    running_loss = 0.0  running_corrects = 0.0  val_running_loss = 0.0  val_running_corrects = 0.0    for inputs, labels in train_loader:    inputs = inputs.to(device)    labels= labels.to(device)    outputs = model(inputs)    loss = criterion(outputs, labels)        optimizer.zero_grad()    loss.backward()    optimizer.step()        _, preds = torch.max(outputs, 1)    running_loss += loss.item()    running_corrects += torch.sum(preds == labels.data)  else:    with torch.no_grad():      for val_inputs, val_labels in validation_loader:        val_inputs = val_inputs.to(device)        val_labels = val_labels.to(device)        val_outputs = model(val_inputs)        val_loss = criterion(val_outputs, val_labels)                _, val_preds = torch.max(val_outputs, 1)        val_running_loss += val_loss.item()        val_running_corrects += torch.sum(val_preds == val_labels.data)          epoch_loss = running_loss/len(train_loader)    epoch_acc = running_corrects.float()/ len(train_loader)    running_loss_history.append(epoch_loss)    running_corrects_history.append(epoch_acc)        val_epoch_loss = val_running_loss/len(validation_loader)    val_epoch_acc = val_running_corrects.float()/ len(validation_loader)    val_running_loss_history.append(val_epoch_loss)    val_running_corrects_history.append(val_epoch_acc)    print('epoch :', (e+1))    print('training loss: {:.4f}, acc {:.4f} '.format(epoch_loss, epoch_acc.item()))    print('validation loss: {:.4f}, validation acc {:.4f} '.format(val_epoch_loss, val_epoch_acc.item()))