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The best emulation (although I didn't check full equivalency): import mxnet as mx SEQUENCE_LENGTH = 100 stack = mx.gluon.rnn.SequentialRNNCell() stack.add(mx.gluon.rnn.LSTMCell(16, prefix='first') stack.add(mx.gluon.rnn.LSTMCell(1, prefix='first') x, _ = stack.unroll(length=SEQUENCE_LENGTH, inputs=x, layout='NTC', merge_outputs=True, ) Here ...


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If you want to predict "Avg CPU Load", your y_train should be only the "Avg CPU Load" column and your x_train should be rest of the columns (you might skip the time column if all timestamps are equally spaced). The network will then learn a function mapping rest of the columns to "Avg CPU Load" and it shall be predicting the same. Hope it helps. edit: ...


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