what's the shape of output in the example?

[byzhang]

c.f. https://github.com/tensorflow/lattice/blob/master/examples/uci_census.py#L419

[byzhang]

and also the values in the output? Thanks a lot!

[si-you]

input_calibration_layer accepts features and applies 1-D calibration per each feature and returns them. In this example, we have 15 features, so the input to this layer has a shape, [batch_size, 15], and then the output should be [batch_size, 15].

Initial output value will depend on hparam's output_min and output_max. Since it is configured to [-1, 1], the output should be a linear function of your input that maps [input_min, input_max] to [-1, 1]. But note that output keypoints are trained jointly with your upper layer DNN. So as training proceeds, the output value will change even for the same input value :).

[byzhang]

Thank you Seungil! We need to precompute the quantiles? And what's the effect of nodes_per_layer and layers in https://github.com/tensorflow/lattice/blob/master/examples/uci_census.py#L407

[si-you]

Yes. You need to pre-compute quantiles, but that can be done by passing the flag --compute_quantiles :).

nodes_per_layer and layers param is used in here: https://github.com/tensorflow/lattice/blob/master/examples/uci_census.py#L424

to construct DNN.

[byzhang]

Thanks!

Thanks, -B

On Sat, Feb 10, 2018 at 4:59 PM, Seungil You notifications@github.com wrote:

Yes. You need to pre-compute quantiles, but that can be done by passing the flag --compute_quantiles :).

nodes_per_layer and layers param is used in here: https://github.com/tensorflow/lattice/blob/master/examples/ uci_census.py#L424

to construct DNN.

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