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54 lines
1.9 KiB
Cython
54 lines
1.9 KiB
Cython
from thinc.api cimport Example, ExampleC
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from thinc.typedefs cimport weight_t
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from ._ml cimport arg_max_if_true
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from ._ml cimport arg_max_if_zero
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import numpy
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from os import path
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cdef class TheanoModel(Model):
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def __init__(self, n_classes, input_spec, train_func, predict_func, model_loc=None,
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eta=0.001, mu=0.9,
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debug=None):
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if model_loc is not None and path.isdir(model_loc):
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model_loc = path.join(model_loc, 'model')
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self.eta = eta
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self.mu = mu
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self.t = 1
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initializer = lambda: 0.2 * numpy.random.uniform(-1.0, 1.0)
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self.input_layer = InputLayer(input_spec, initializer)
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self.train_func = train_func
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self.predict_func = predict_func
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self.debug = debug
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self.n_classes = n_classes
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self.n_feats = len(self.input_layer)
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self.model_loc = model_loc
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def predict(self, Example eg):
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self.input_layer.fill(eg.embeddings, eg.atoms, use_avg=True)
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theano_scores = self.predict_func(eg.embeddings)[0]
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cdef int i
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for i in range(self.n_classes):
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eg.c.scores[i] = theano_scores[i]
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eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
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def train(self, Example eg):
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self.input_layer.fill(eg.embeddings, eg.atoms, use_avg=False)
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theano_scores, update, y, loss = self.train_func(eg.embeddings, eg.costs,
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self.eta, self.mu)
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self.input_layer.update(update, eg.atoms, self.t, self.eta, self.mu)
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for i in range(self.n_classes):
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eg.c.scores[i] = theano_scores[i]
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eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
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eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
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eg.c.cost = eg.c.costs[eg.c.guess]
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eg.c.loss = loss
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self.t += 1
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def end_training(self):
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pass
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