Source code for astroNN.nn

def magic_correction_term(y_true):
    """
    Calculate a correction term to prevent the loss being lowered by magic_num

    :param y_true: Ground Truth
    :type y_true: tf.Tensor
    :return: Correction Term
    :rtype: tf.Tensor
    :History:
        | 2018-Jan-30 - Written - Henry Leung (University of Toronto)
        | 2018-Feb-17 - Updated - Henry Leung (University of Toronto)
    """
    import tensorflow as tf
    from astroNN.config import MAGIC_NUMBER

    num_nonmagic = tf.reduce_sum(tf.cast(tf.not_equal(y_true, MAGIC_NUMBER), tf.float32), axis=-1)
    num_magic = tf.reduce_sum(tf.cast(tf.equal(y_true, MAGIC_NUMBER), tf.float32), axis=-1)

    # If no magic number, then num_zero=0 and whole expression is just 1 and get back our good old loss
    # If num_nonzero is 0, that means we don't have any information, then set the correction term to ones
    return (num_nonmagic + num_magic) / num_nonmagic


def reduce_var(x, axis=None, keepdims=False):
    """
    Calculate variance using Tensorflow (as opposed to tf.nn.moment which return both variance and mean)

    :param x: Data
    :type x: tf.Tensor
    :param axis: Axis
    :type axis: int
    :param keepdims: Keeping variance dimension as data or not
    :type keepdims: boolean
    :return: Variance
    :rtype: tf.Tensor
    :History: 2018-Mar-04 - Written - Henry Leung (University of Toronto)
    """
    import tensorflow as tf

    m = tf.reduce_mean(x, axis, True)
    devs_squared = tf.square(x - m)
    return tf.reduce_mean(devs_squared, axis, keepdims)


def intpow_avx2(x, n):
    """
    Calculate integer power of float (including negative) even with Tensorflow compiled with AVX2 since --fast-math
    compiler flag aggressively optimize float operation which is common with AVX2 flag

    :param x: identifier
    :type x: tf.Tensor
    :param n: an integer power (a float will be casted to integer!!)
    :type n: int
    :return: powered float(s)
    :rtype: tf.Tensor
    :History: 2018-Aug-13 - Written - Henry Leung (University of Toronto)
    """
    import tensorflow as tf

    # expand inputs to prepare to be tiled
    expanded_inputs = tf.expand_dims(x, 1)
    # we want [1, self.n]
    return tf.reduce_prod(tf.tile(expanded_inputs, [1, n]), axis=-1)


def nn_obj_lookup(identifier, module_obj=None, module_name='default_obj'):
    """
    Lookup astroNN.nn function by name

    :param identifier: identifier
    :type identifier: str
    :param module_obj: globals()
    :type module_obj: Union([Nonetype, dir])
    :param module_name: module english name
    :type module_name: str
    :return: Looked up function
    :rtype: function
    :History: 2018-Apr-28 - Written - Henry Leung (University of Toronto)
    """
    function_name = identifier
    fn = module_obj.get(function_name)
    if fn is None:
        raise ValueError('Unknown function: ' + module_name + '.' + function_name)
    return fn