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.. "auto_examples/ex_nn.py"
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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_ex_nn.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ex_nn.py:


Residual Neural Network Construction
=====================================

This example demonstrates how to use the Neural Network wrapper class, ``pytuq.surrogates.nn`` with scalar valued functions.
The constructor of the NN class accepts in an optional dictionary, ``net_options``, to specify additional hyperparameters. 
The ``build()`` and ``evaluate()`` functions similarly accept dictionaries and explicit keyword arguments during their respective function calls.

.. GENERATED FROM PYTHON SOURCE LINES 9-109

.. code-block:: Python


    import sys
    import torch
    import numpy as np

    from pytuq.surrogates.nn import NN
    from quinn.solvers.nn_vi import NN_VI
    from quinn.nns.rnet import RNet, Poly
    from quinn.utils.plotting import myrc
    from quinn.utils.maps import scale01ToDom
    from quinn.func.funcs import Sine, Sine10, blundell

    def main():
        """Main function."""
        torch.set_default_dtype(torch.double)
        myrc()

        #################################################################################
        #################################################################################

        # defaults to cuda:0 if available
        device_id='cuda:0'
        device = torch.device(device_id if torch.cuda.is_available() else 'cpu')
        print("Using device",device)

        nall = 15 # total number of points
        trn_factor = 0.9 # which fraction of nall goes to training
        ntst = 13 # separate test set
        ndim = 1 # input dimensionality
        datanoise = 0.02 # Noise in the generated data
        true_model, nout = Sine, 1  # Scalar valued output example

        #################################################################################
        #################################################################################

        # Domain: defining range of input variable
        domain = np.tile(np.array([-np.pi, np.pi]), (ndim, 1))

        np.random.seed(111)

        # Generating x-y training, validation, and testing data
        xall = scale01ToDom(np.random.rand(nall, ndim), domain)
        if true_model is not None:
            yall = true_model(xall, datanoise=datanoise)

        if ntst > 0:
            np.random.seed(100)
            xtst = scale01ToDom(np.random.rand(ntst, ndim), domain)
            if true_model is not None:
                ytst = true_model(xtst, datanoise=datanoise)

        # (1) Initialize neural network with optional parameters for object instantiation
        net_options = {'wp_function': Poly(0),
                       'indim': ndim, 
                       'outdim': nout,
                       'layer_pre': True,
                       'layer_post': True,
                       'biasorno': True,
                       'nonlin': True,
                       'mlp': False, 
                       'final_layer': None,
                       'device': device,
                       }

        # Pass in unpacked net_options dict to constructor through kwargs
        nnet = NN('RNet', 3, 3, **net_options)
    
        # (1.5) Split data into training and validation, assign through member functions
        ntrn = int(trn_factor * nall)
        indperm = range(nall) # np.random.permutation(range(nall))
        indtrn = indperm[:ntrn]
        indval = indperm[ntrn:]
        xtrn, xval = xall[indtrn, :], xall[indval, :]
        ytrn, yval = yall[indtrn, :], yall[indval, :]

        nnet.set_validation_data(xval, yval) # optional
        nnet.set_training_data(xtrn, ytrn)

        # (2) 1st build: Call build function (defaults to UQ method of variational inference) with optional parameters for fitting
        nnet.build(datanoise=datanoise, lrate=0.01, batch_size=None, nsam=1, nepochs=5000, verbose=False)

        results = nnet.evaluate(xtst, nsam = 100, msc = 2) # Return samples of predictions with variance + covariance
        print("Y_eval:", results['Y_eval'], end="\n\n") # Printing only samples of predictions

        # (3) 2nd build: Example of throwing an exception when passing in a network option through build()
        # fit_options = {'datanoise': 0.05,
        #                'outdim': 3,
        #                'lrate': 0.01,
        #                'batch_size': None,
        #                'nsam': 1,
        #                'nepochs': 300,
        #                'verbose': False
        #                }

        # nnet.build(**fit_options)
        # results = nnet.evaluate(xtst, nsam = 100, msc = 2)
        # print(results)


    if __name__ == '__main__':
        main()

.. _sphx_glr_download_auto_examples_ex_nn.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: ex_nn.ipynb <ex_nn.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: ex_nn.py <ex_nn.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: ex_nn.zip <ex_nn.zip>`


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