.. automodule:: astroNN.models.apogee_models

APOGEE Spectra with Bayesian NN and Gaia offset calibration - **ApogeeDR14GaiaDR2BCNN**
----------------------------------------------------------------------------------------------------------

.. autoclass:: astroNN.models.apogee_models.ApogeeDR14GaiaDR2BCNN
    :members:

.. inheritance-diagram:: astroNN.models.apogee_models.ApogeeDR14GaiaDR2BCNN
   :parts: 1

`ApogeeDR14GaiaDR2BCNN` can only be used with Apogee spectra with 7,514 pixels

.. code-block:: python

    from astroNN.models import ApogeeDR14GaiaDR2BCNN
    from astroNN.datasets import H5Loader

    # Load the train data from dataset first, x_train is spectra and y_train will be ASPCAP labels
    loader = H5Loader('datasets.h5')
    loader.load_combined = True
    loader.load_err = False
    loader.target = ['Ks-band fakemag']
    x_train, y_train, x_err, y_err = loader.load()

    # And then create an instance of Apogee Censored Bayesian Convolutional Neural Network class
    apogee_gaia_bcnn = ApogeeDR14GaiaDR2BCNN()

    # Set max_epochs to 10 for a quick result. You should train more epochs normally, especially with dropout
    apogee_gaia_bcnn.max_epochs = 10
    apogee_gaia_bcnn.train(x_train, y_train, x_err, y_err)

Here is a list of parameter you can set but you can also not set them to use default

.. code-block:: python

    ApogeeDR14GaiaDR2BCNN.batch_size = 64
    ApogeeDR14GaiaDR2BCNN.initializer = 'he_normal'
    ApogeeDR14GaiaDR2BCNN.activation = 'relu'
    ApogeeDR14GaiaDR2BCNN.num_filters = [2, 4]
    ApogeeDR14GaiaDR2BCNN.filter_len = 8
    ApogeeDR14GaiaDR2BCNN.pool_length = 4
    # number of neurone for [old_bcnn_1, old_bcnn_2, offset_hidden_1, offset_hidden_2]
    ApogeeDR14GaiaDR2BCNN.num_hidden = [162, 64, 32, 16]
    ApogeeDR14GaiaDR2BCNN.max_epochs = 50
    ApogeeDR14GaiaDR2BCNN.lr = 0.005
    ApogeeDR14GaiaDR2BCNN.reduce_lr_epsilon = 0.00005
    ApogeeDR14GaiaDR2BCNN.reduce_lr_min = 0.0000000001
    ApogeeDR14GaiaDR2BCNN.reduce_lr_patience = 10
    ApogeeDR14GaiaDR2BCNN.target = 'all'
    ApogeeDR14GaiaDR2BCNN.l2 = 5e-9
    ApogeeDR14GaiaDR2BCNN.dropout_rate = 0.2
    ApogeeDR14GaiaDR2BCNN.input_norm_mode = 3
    ApogeeDR14GaiaDR2BCNN.labels_norm_mode = 2

.. note:: You can disable astroNN data normalization via ``ApogeeDR14GaiaDR2BCNN.input_norm_mode=0`` as well as ``ApogeeDR14GaiaDR2BCNN.labels_norm_mode=0`` and do normalization yourself. But make sure you don't normalize labels with MAGIC_NUMBER (missing labels).

After the training, you can use `apogee_gaia_bcnn` in this case and call test method to test the neural network on test data. Or you can load the folder by

.. code-block:: python

    from astroNN.models import load_folder
    apogee_gaia_bcnn = load_folder('astroNN_0101_run001')

    # Load the test data from dataset, x_test is spectra and y_test will be ASPCAP labels
    test_data = ......

    # pred contains denormalized result aka. fakemag prediction in this case
    # pred_std is a list of uncertainty
    # pred_std['total'] is the total uncertainty (standard derivation) which is the sum of all the uncertainty
    # pred_std['predictive'] is the predictive uncertainty predicted by bayesian neural net
    # pred_std['model'] is the model uncertainty from dropout variational inference
    pred, pred_std = apogee_gaia_bcnn.test(test_data)

.. code-block:: python

    # Calculate jacobian
    jacobian_array = apogee_gaia_bcnn.jacobian(x_test, mean_output=True)


Architecture
==============

The architecture of this neural network is as follow.

.. image:: /neuralnets/bcnn_apogee14gaia2/ApogeeGaiaNN.png