.. automodule:: astroNN.models.apogee_models

APOGEE Spectra with Convolutional Neural Net - **ApogeeCNN**
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.. autoclass:: astroNN.models.apogee_models.ApogeeCNN
    :members:

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

Although in theory you can feed any 1D data to astroNN neural networks. This tutorial will only focus on spectra analysis.

.. code-block:: python

    from astroNN.models import ApogeeCNN
    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_err = False
    x_train, y_train = loader.load()

    # And then create an instance of Convolutional Neural Network class
    cnn_net = ApogeeCNN()

    # You don't have to specify the task because its 'regression' by default. But if you are doing classification. you can set task='classification'
    cnn_net.task = 'regression'

    # Set max_epochs to 10 for a quick result. You should train more epochs normally
    cnn_net.max_epochs = 10
    cnn_net.train(x_train, y_train)


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

.. code-block:: python

    ApogeeCNN.batch_size = 64
    ApogeeCNN.initializer = 'he_normal'
    ApogeeCNN.activation = 'relu'
    ApogeeCNN.num_filters = [2, 4]
    ApogeeCNN.filter_len = 8
    ApogeeCNN.pool_length = 4
    ApogeeCNN.num_hidden = [196, 96]
    ApogeeCNN.max_epochs = 250
    ApogeeCNN.lr = 0.005
    ApogeeCNN.reduce_lr_epsilon = 0.00005
    ApogeeCNN.reduce_lr_min = 0.0000000001
    ApogeeCNN.reduce_lr_patience = 10
    ApogeeCNN.target = 'all'
    ApogeeCNN.l2 = 1e-7
    ApogeeCNN.input_norm_mode = 1
    ApogeeCNN.labels_norm_mode = 2

.. note:: You can disable astroNN data normalization via ``ApogeeCNN.input_norm_mode=0`` as well as ``ApogeeCNN.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 `cnn_net` 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
    cnn_net = load_folder('astroNN_0101_run001')

    # Load the test data from dataset, x_test is spectra and y_test will be ASPCAP labels
    loader2 = H5Loader('datasets.h5')
    loader2.load_combined = False
    x_test, y_test = loader2.load()

    pred = cnn_net.test(x_test)  # pred contains denormalized result aka. ASPCAP labels prediction in this case


Since `astroNN.models.ApogeeCNN` does not have uncertainty analysis feature.

You can calculate jacobian which represents the output derivative to the input and see where those output is sensitive to in inputs.

.. code-block:: python

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

.. note:: You can access to Keras model method like model.predict via (in the above tutorial) cnn_net.keras_model (Example: cnn_net.keras_model.predict())

Example Plots using aspcap_residue_plot
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.. image:: /neuralnets/cnn_apogee/logg_test.png
.. image:: /neuralnets/cnn_apogee/teff_test.png

ASPCAP labels prediction using CNN vs The Cannon 2
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.. warning:: Please refer to Bayesian Neural Network for the most updated result: https://astronn.readthedocs.io/en/latest/neuralnets/apogee_bcnn.html


.. image:: https://image.ibb.co/fDY5JG/table1.png

Example Plots using jacobian
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.. image:: /neuralnets/bcnn_apogee/Cl_jacobian.png
.. image:: /neuralnets/bcnn_apogee/Na_jacobian.png