spaCy/spacy/_theano.pyx

53 lines
1.9 KiB
Cython
Raw Normal View History

2015-06-28 23:36:03 +03:00
from thinc.api cimport Example, ExampleC
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,
2015-06-30 15:26:32 +03:00
eta=0.001, mu=0.9, debug=None):
if model_loc is not None and path.isdir(model_loc):
model_loc = path.join(model_loc, 'model')
self.eta = eta
self.mu = mu
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.debug = debug
self.n_classes = n_classes
self.n_feats = len(self.input_layer)
self.model_loc = model_loc
def predict(self, Example eg):
2015-06-29 17:44:42 +03:00
self.input_layer.fill(eg.embeddings, eg.atoms, use_avg=True)
theano_scores = self.predict_func(eg.embeddings)[0]
cdef int i
for i in range(self.n_classes):
eg.c.scores[i] = theano_scores[i]
eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
def train(self, Example eg):
2015-06-27 05:18:47 +03:00
self.input_layer.fill(eg.embeddings, eg.atoms, use_avg=False)
theano_scores, update, y, loss = self.train_func(eg.embeddings, eg.costs,
self.eta, self.mu)
self.input_layer.update(update, eg.atoms, self.t, self.eta, self.mu)
for i in range(self.n_classes):
2015-06-28 23:36:03 +03:00
eg.c.scores[i] = theano_scores[i]
eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
eg.c.cost = eg.c.costs[eg.c.guess]
eg.c.loss = loss
self.t += 1
def end_training(self):
pass