vae.py 文件源码

def evaluateBound(self, dataset, batch_size, S=10):
        """
                                    Evaluate bound S times on dataset 
        """
        N = dataset.shape[0]
        bound = 0
        for bnum,st_idx in enumerate(range(0,N,batch_size)):
            end_idx = min(st_idx+batch_size, N)
            X       = dataset[st_idx:end_idx].astype(config.floatX)
            for s in range(S):
                eps     = np.random.randn(X.shape[0],self.params['dim_stochastic']).astype(config.floatX)
                if self.params['inference_model']=='single':
                    batch_bound = self.evaluate(X=X, eps=eps)
                else:
                    assert False,'Should not be here'
                bound  += batch_bound
        bound /= float(N*S)
        return bound