def __init__()

in senteval/tools/classifier.py [0:0]

• 27 lines of code
• 9 McCabe index (conditional complexity)

    def __init__(self, params, inputdim, nclasses, l2reg=0., batch_size=64,
                 seed=1111, cudaEfficient=False):
        super(self.__class__, self).__init__(inputdim, nclasses, l2reg,
                                             batch_size, seed, cudaEfficient)
        """
        PARAMETERS:
        -nhid:       number of hidden units (0: Logistic Regression)
        -optim:      optimizer ("sgd,lr=0.1", "adam", "rmsprop" ..)
        -tenacity:   how many times dev acc does not increase before stopping
        -epoch_size: each epoch corresponds to epoch_size pass on the train set
        -max_epoch:  max number of epoches
        -dropout:    dropout for MLP
        """

        self.nhid = 0 if "nhid" not in params else params["nhid"]
        self.optim = "adam" if "optim" not in params else params["optim"]
        self.tenacity = 5 if "tenacity" not in params else params["tenacity"]
        self.epoch_size = 4 if "epoch_size" not in params else params["epoch_size"]
        self.max_epoch = 200 if "max_epoch" not in params else params["max_epoch"]
        self.dropout = 0. if "dropout" not in params else params["dropout"]
        self.batch_size = 64 if "batch_size" not in params else params["batch_size"]

        if params["nhid"] == 0:
            self.model = nn.Sequential(
                nn.Linear(self.inputdim, self.nclasses),
            ).cuda()
        else:
            self.model = nn.Sequential(
                nn.Linear(self.inputdim, params["nhid"]),
                nn.Dropout(p=self.dropout),
                nn.Sigmoid(),
                nn.Linear(params["nhid"], self.nclasses),
            ).cuda()

        self.loss_fn = nn.CrossEntropyLoss().cuda()
        self.loss_fn.size_average = False

        optim_fn, optim_params = utils.get_optimizer(self.optim)
        self.optimizer = optim_fn(self.model.parameters(), **optim_params)
        self.optimizer.param_groups[0]['weight_decay'] = self.l2reg