GiulioCMSanto

def process_image(image):
    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    '''
    
    # TODO: Process a PIL image for use in a PyTorch model
    
    #Loading image with PIL (https://pillow.readthedocs.io/en/latest/reference/Image.html)
    im = Image.open(image)
    

GiulioCMSanto

#Extract the class_to_idx transformation 
model.class_to_idx = train_data.class_to_idx

#Put the model in CPU mode to allow predictions without having CUDA
model.to('cpu')

#Create the checkpoint
checkpoint = {'input_size':25088,
              'output_size':102,
              'hidden_layers':[each.out_features for each in classifier.hidden_layers],

GiulioCMSanto

#The class bellow was created based in the one provided by Udacity
class Network(nn.Module):
    def __init__(self, input_size, output_size, hidden_layers, drop_p=0.5):
        ''' Builds a feedforward network with arbitrary hidden layers.
        
            Arguments
            ---------
            input_size: integer, size of the input layer
            output_size: integer, size of the output layer
            hidden_layers: list of integers, the sizes of the hidden layers

GiulioCMSanto

model = models.vgg16(pretrained=True)

GiulioCMSanto

train_transforms = transforms.Compose([transforms.RandomRotation(30),
                                       transforms.RandomResizedCrop(224),
                                       transforms.RandomHorizontalFlip(),
                                       transforms.ToTensor(),
                                       transforms.Normalize([0.485, 0.456, 0.406], 
                                                            [0.229, 0.224, 0.225])])