def bruna10(pretrained_path=False, hidden_size=10, **kwargs): """Constructs a very simple convNet Args: pretrained_path (bool): If True, returns the pretrained model on the path """ model = singleHiddenFullyConnected(layers=[3072, hidden_size, 100]) if pretrained_path: return loadNet(pretrained_path, model) return model
def conv1020tanh(pretrained_path=False, **kwargs): """Constructs a very simple convNet Args: pretrained_path (bool): If True, returns the pretrained model on the path """ model = conv22tanh(layers=[3, 10, 32, 400, 100], im_dim=32) if pretrained_path: return loadNet(pretrained_path, model) return model
def alex6464(pretrained_path=False, **kwargs): """alex like convnet Args: pretrained_path (bool): If True, returns the pretrained model on the path """ model = AlexNet(layers=[3, 64, 128, 512, 128, 100]) if pretrained_path: return loadNet(pretrained_path, model) return model
def alex6464raw(pretrained_path=False, **kwargs): """alex like convnet Args: pretrained_path (bool): If True, returns the pretrained model on the path """ model = AlexNet(raw=True) if pretrained_path: return loadNet(pretrained_path, model) return model
def conv6464relu(pretrained_path=False, **kwargs): """alex like convnet Args: pretrained_path (bool): If True, returns the pretrained model on the path """ model = convAlexrelu(layers=[3, 64, 64, 192, 10], k_dims=[5, 5], im_dim=32) if pretrained_path: return loadNet(pretrained_path, model) return model
def conv1020relu(pretrained_path=False, **kwargs): """todo Args: pretrained_path (bool): If True, returns the pretrained model on the path """ model = conv22relu(layers=[3, 10, 20, 100, 10], im_dim=32) if pretrained_path: return loadNet(pretrained_path, model) return model
def convTest(pretrained_path=False, **kwargs): """Constructs a very simple convNet Args: pretrained_path (bool): If True, returns the pretrained model on the path """ model = conv22relu(layers=[1, 2, 4, 32, 10]) if pretrained_path: return loadNet(pretrained_path, model) return model
elif args.dataset=='cifar10': import models.cifar10 as my_models model = getattr(my_models, args.model) elif args.dataset=='mnist': import models.mnist as my_models model = getattr(my_models, args.model) else: print("Unknown dataset: ",args.dataset); sys.exit() #Load network if args.load == 'nil': model = model(hidden_size=args.hidden_size) iniPeriod=0 else: model=loadNet(args.load,model) from re import search iniPeriod=1+int(search('_',args.model,'_(.+?).pyT',args.load).group(1)) ################################# # Initialization of the weights # ################################# # Function that yields all the optimizable parameters of the network (the network's size) def getNumParam(mymodel): tot=0 for item in list(mymodel.parameters()): n_layer=item.numel() tot+=n_layer return tot