(self, dataset)
return Ypred[:dataset.ndata]
Get the activation outputs of the final model layer for the dataset

        Arguments:
            dataset (NervanaDataIterator): Dataset iterator to perform fit on

        Returns:
            Host numpy array: the output of the final layer for the entire Dataset
Get the activation outputs of the final model layer for the dataset
Get the activation outputs of the final model layer for the dataset
def get_outputs(self, dataset):
        """
        Get the activation outputs of the final model layer for the dataset

        Arguments:
            dataset (NervanaDataIterator): Dataset iterator to perform fit on

        Returns:
            Host numpy array: the output of the final layer for the entire Dataset
        """
        self.initialize(dataset)
        dataset.reset()  # Move "pointer" back to beginning of dataset
        n = dataset.nbatches
        x = self.layers.layers[-1].outputs
        assert not isinstance(x, list), "Can not get_outputs with Branch terminal"
        Ypred = None
        for idx, input_data in enumerate(dataset):
            x = self.fprop(input_data[0], inference=True)
            if Ypred is None:
                (dim0, dim1) = x.shape
                Ypred = np.empty((n * dim1, dim0), dtype=x.dtype)
                nsteps = dim1 // self.be.bsz
            cur_batch = slice(idx * dim1, (idx + 1) * dim1)
            Ypred[cur_batch] = x.get().T

        # Handle the recurrent case.
        if nsteps != 1:
            b, s = (self.be.bsz, nsteps)
            Ypred = Ypred.reshape((n, s, b, -1)).transpose(0, 2, 1, 3).copy().reshape(n * b, s, -1)

        return Ypred[:dataset.ndata]
def get_outputs ( self , dataset ) : self . initialize ( dataset ) dataset . reset ( ) # Move "pointer" back to beginning of dataset n = dataset . nbatches x = self . layers . layers [ - 1 ] . outputs assert not isinstance ( x , list ) , "Can not get_outputs with Branch terminal" Ypred = None for idx , input_data in enumerate ( dataset ) : x = self . fprop ( input_data [ 0 ] , inference = True ) if Ypred is None : ( dim0 , dim1 ) = x . shape Ypred = np . empty ( ( n * dim1 , dim0 ) , dtype = x . dtype ) nsteps = dim1 // self . be . bsz cur_batch = slice ( idx * dim1 , ( idx + 1 ) * dim1 ) Ypred [ cur_batch ] = x . get ( ) . T # Handle the recurrent case. if nsteps != 1 : b , s = ( self . be . bsz , nsteps ) Ypred = Ypred . reshape ( ( n , s , b , - 1 ) ) . transpose ( 0 , 2 , 1 , 3 ) . copy ( ) . reshape ( n * b , s , - 1 ) return Ypred [ : dataset . ndata ]