nn_layers.py 文件源码

def __init__(self,rng, W=None,m=1.0, n_samples=50,shape=None,batch_size=1000):
        if W is None:
            W = numpy.asarray(rng.uniform(
                low=-numpy.sqrt(6. / (shape[0] + shape[1])),
                high=numpy.sqrt(6. / (shape[0] + shape[1])),
                size=(shape[0], shape[1])), dtype=theano.config.floatX)

        self.W = theano.shared(value=W, name='Hashtag_emb', borrow=True)
        self.batch_size = batch_size
        self.n_ht = W.shape[0]
        self.m = m
        self.n_samples = n_samples
        self.csrng = CURAND_RandomStreams(123)
        mask = self.csrng.uniform(size=(self.n_samples,1),low=0.0,high=1.0,dtype=theano.config.floatX)
        self.rfun = theano.function([],mask.argsort(axis=0))

        self.alpha = T.constant(1.0/numpy.arange(start=1,stop=self.n_ht + 1,step=1))

        self.weights = [self.W]
        self.biases = []