def _predict_loop(self, f, ins, batch_size=32, verbose=0):
        nb_sample = len(ins[0])
        outs = []
        if verbose == 1:
            progbar = Progbar(target=nb_sample)
        batches = make_batches(nb_sample, batch_size)
        index_array = np.arange(nb_sample)
        for batch_index, (batch_start, batch_end) in enumerate(batches):
            batch_ids = index_array[batch_start:batch_end]
            if type(ins[-1]) is float:
                # do not slice the training phase flag
                ins_batch = slice_X(ins[:-1], batch_ids) + [ins[-1]]
            else:
                ins_batch = slice_X(ins, batch_ids)

            batch_outs = f(ins_batch)
            if type(batch_outs) != list:
                batch_outs = [batch_outs]
            if batch_index == 0:
                for batch_out in batch_outs:
                    shape = (nb_sample,) + batch_out.shape[1:]
                    outs.append(np.zeros(shape))

            for i, batch_out in enumerate(batch_outs):
                outs[i][batch_start:batch_end] = batch_out
            if verbose == 1:
                progbar.update(batch_end)
        if len(outs) == 1:
            return outs[0]
        return outs
Issue the given command via the cftp client. Return a C{Deferred} that
dataset/ETHPy150Open fchollet/keras/keras/engine/training.py/Model._predict_loop--crossover with--dataset/ETHPy150Open twisted/twisted/twisted/conch/test/test_cftp.py/SFTPTestProcess.runCommand