Deep Learning and Neural Network - LaBRI
Deep Learning and Neural Network
Marie Beurton-Aimar, LE Van Linh December 15, 2020
Beurton-Aimar, Le
Deep Learning and Neural Network
today 1 / 18
Data Loading
Import Loading a matrix - by using pandas library : dataTrain=pandas.read excel(``data-train.xlsx'', header=0) Normalizing the data - by using scikit-learn : sts=StandardScaler DTrain=sts.fit transform(dataTrain[dataTrain.columns[:-1]] Convert and creating target variable (the last column is named 'classe' in the data file) - the variable is coded as int 0/1 : yTrain = dataTrain.classe.astype('category').cat.codes.values
Beurton-Aimar, Le
Deep Learning and Neural Network
today 2 / 18
Data Loading
Transform Tensor creating : special data type to manage data in pytorch. Convert numpy matrix of data in tensor : tensor DTrain = torch.FloatTensor(DTrain) Create a tensor for target variable : tensor yTrain= torch.FloatTensor(yTrain)
Beurton-Aimar, Le
Deep Learning and Neural Network
today 3 / 18
Data Loading
Images loading Several possibilities : give the path names or read a file containing image file names. One sample with path files :
data_dir ='Cat_Dog_data/train' transform = pose([transforms.Resize(255),
transforms.CenterCrop(224), transforms.ToTensor()]) dataset = datasets.ImageFolder(data_dir, transform=transform dataloader = torch.utils.data.DataLoader(dataset, batch_size
shuffle=True) images, labels = next(iter(dataloader)) helper.imshow(images[0], normalize=False)
Beurton-Aimar, Le
Deep Learning and Neural Network
today 4 / 18
Model in Pytorch
Designing Class By inheritance of torch.nn.Module class. Overload methods : init et forward init to describe layers and transfert functions forward to describe the sequence of calculus from input to output.
Beurton-Aimar, Le
Deep Learning and Neural Network
today 5 / 18
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