spaCy/spacy/_theano.pyx

from thinc.api cimport Example
from thinc.typedefs cimport weight_t

from ._ml cimport arg_max_if_true
from ._ml cimport arg_max_if_zero

import numpy
from os import path


cdef class TheanoModel(Model):
    def __init__(self, n_classes, input_spec, train_func, predict_func, model_loc=None):
        if model_loc is not None and path.isdir(model_loc):
            model_loc = path.join(model_loc, 'model')

        self.eta = 0.001
        self.mu = 0.9
        self.t = 1
        initializer = lambda: 0.2 * numpy.random.uniform(-1.0, 1.0)
        self.input_layer = InputLayer(input_spec, initializer)
        self.train_func = train_func
        self.predict_func = predict_func

        self.n_classes = n_classes
        self.n_feats = len(self.input_layer)
        self.model_loc = model_loc
        
    def predict(self, Example eg):
        self.input_layer.fill(eg.embeddings, eg.atoms)
        theano_scores = self.predict_func(eg.embeddings)
        cdef int i
        for i in range(self.n_classes):
            eg.scores[i] = theano_scores[i]
        eg.guess = arg_max_if_true(<weight_t*>eg.scores.data, <int*>eg.is_valid.data,
                                   self.n_classes)

    def train(self, Example eg):
        self.predict(eg)
        update, t, eta, mu = self.train_func(eg.embeddings, eg.scores, eg.costs)
        self.input_layer.update(eg.atoms, update, self.t, self.eta, self.mu)
        eg.best = arg_max_if_zero(<weight_t*>eg.scores.data, <int*>eg.costs.data,
                                  self.n_classes)
        eg.cost = eg.costs[eg.guess]
        self.t += 1