Day31 参加职训(机器学习与资料分析工程师培训班)，tf.keras & Pytorch

早上: Python机器学习套件与资料分析

今天也是练习CNN

import osimport cv2parent_directory = './AfricanWildlife'X = []y = []for folders in os.listdir('./AfricanWildlife'):    filepath = os.path.join(parent_directory, folders)    if not os.path.isdir(filepath):        continue    Category_index = os.listdir('./AfricanWildlife').index(folders)    print(Category_index)    for files in os.listdir(filepath):        if files.endswith('jpg'):            filename = os.path.join(filepath, files)            image = cv2.imread(filename)            image_resized = cv2.resize(image, (128,128))            X.append(image_resized)            y.append(Category_index)

import numpy as npX = np.array(X).astype(np.float32)/255y = np.array(y)# 切分训练&测试集from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=1)

# 建立网路from tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, FlattenAW_Model = Sequential()AW_Model.add(Conv2D(64,(5,5), activation='relu', input_shape=(128,128,3)))AW_Model.add(MaxPooling2D(2))AW_Model.add(Conv2D(32,(5,5), activation='relu'))AW_Model.add(MaxPooling2D(2))AW_Model.add(Conv2D(16,(5,5), activation='relu'))AW_Model.add(MaxPooling2D(2))AW_Model.add(Flatten())AW_Model.add(Dense(32, activation='relu'))AW_Model.add(Dense(4, activation='softmax'))

AW_Model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])AW_Model.fit(X_train, y_train, epochs=5, batch_size=28, validation_data=(X_test, y_test))

结果不尽理想，应该是参数或是网路架构要再调整

下午: Pytorch 与深度学习初探

######################### 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, transformsfrom torchsummary import summaryt1=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 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 yProbmodel=myDNN(784,256,64,10)print(model)#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.view(inputs.shape[0], -1)    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.view(val_inputs.shape[0], -1)        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()))