resources.wrappers.FileJsonPyTorch.gate-lf-pytorch-json.gatelfpytorchjson.modules.AllNumsClass.py Maven / Gradle / Ivy

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A GATE plugin that provides many different machine learning algorithms for a wide range of NLP-related machine learning tasks like text classification, tagging, or chunking.

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import torch.nn
from gatelfpytorchjson import CustomModule
from torch.autograd import Variable as V
import sys
import logging

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
streamhandler = logging.StreamHandler(stream=sys.stderr)
formatter = logging.Formatter(
                '%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
streamhandler.setFormatter(formatter)
logger.addHandler(streamhandler)


class AllNumsClass(CustomModule):

    # NOTE: make sure the dataset is not stored and only used to decide on parameters etc so
    # that the dataset data is not getting pickled when the model is saved!
    def __init__(self, dataset, config={}):
        super().__init__()
        self.fflayers = torch.nn.Sequential(
            torch.nn.Linear(34, 34, bias=True),
            torch.nn.Dropout(p=0.5),
            torch.nn.ELU(),
            torch.nn.Linear(34, 2, bias=True),
            torch.nn.LogSoftmax(dim=1)
        )
        self._on_cuda = None

    def forward(self, batch):
        # batch is a list of features, each feature with all the values for all the batch examples
        # TODO: Check how to best deal with the shape mismatch and why we need to transpose here
        vals4pt = V(torch.FloatTensor(batch).t(), requires_grad=True)
        if self.on_cuda():
            vals4pt = vals4pt.cuda()
        out = self.fflayers(vals4pt)
        return out