rec_layers.py 文件源码

def _init_params(self):
        self.W_hhs = []
        self.b_hhs = []
        for dx in xrange(self.n_layers):
            W_hh = self.init_fn[dx](self.n_hids[(dx-1)%self.n_layers],
                                        self.n_hids[dx],
                                        self.sparsity[dx],
                                        self.scale[dx],
                                        rng=self.rng)
            self.W_hhs.append(theano.shared(value=W_hh, name="W%d_%s" %
                                       (dx,self.name)))
            if dx > 0:
                self.b_hhs.append(theano.shared(
                    self.bias_fn[dx-1](self.n_hids[dx],
                                       self.bias_scale[dx-1],
                                       self.rng),
                    name='b%d_%s' %(dx, self.name)))
        self.params = [x for x in self.W_hhs] + [x for x in self.b_hhs]
        self.params_grad_scale = [self.grad_scale for x in self.params]
        if self.weight_noise:
            self.nW_hhs = [theano.shared(x.get_value()*0, name='noise_'+x.name) for x in self.W_hhs]
            self.nb_hhs = [theano.shared(x.get_value()*0, name='noise_'+x.name) for x in self.b_hhs]
            self.noise_params = [x for x in self.nW_hhs] + [x for x in self.nb_hhs]
            self.noise_params_shape_fn = [constant_shape(x.get_value().shape)
                            for x in self.noise_params]