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# For a more detailed explanation on the SumOfGaussian model, please
# refer to:
import tensorflow as tf
import numpy as np
def calculate_multivariate_gaussian(positions, amp, mu, sigma):
"""Calculates the multivariate Gaussian distribution on given positions in D dimensions.
Based on:
positions: Array of (<inner_shape>, D) of data points of dimension D.
amp: The amplitude (scalar).
mu: Array of (D,) with the mean vector.
sigma: Array of (D,D) with the covariance matrix.
Array of (<inner_shape>) with the gaussian function value multiplied by the amplitude
at each point.
inv_sigma = tf.linalg.inv(sigma)
flatten_pos = tf.reshape(positions, (-1, tf.shape(positions)[-1]))
flatten_result = (amp * tf.exp(-0.5 * tf.einsum('ak,kl,al->a', flatten_pos - mu, inv_sigma, flatten_pos - mu)))
return tf.reshape(flatten_result, tf.shape(positions)[:-1])
def _means_spread_regularizer(means, eps=1e-9):
"""Defines a loss term that penalize for two centers that are close to each other."""
num_means = means.shape[0]
means_pairwise_distances = tf.norm(tf.expand_dims(means, 0) - tf.expand_dims(means, 1) + eps, axis=-1)
matrix_upper_half_only = tf.linalg.band_part(means_pairwise_distances, 0, -1)
num_distances = num_means * (num_means - 1) / 2
avg_distance = tf.reduce_sum(matrix_upper_half_only) / num_distances
return 1. / avg_distance
def _multiple_identity_matrices_initializer(shape, dtype=None):
"""Initialize a stacked tensor of multiple identity matrices."""
del dtype # unused.
assert len(shape) == 3 and shape[1] == shape[2], 'shape must be (N, D, D)'
return np.stack([np.identity(shape[1]) for _ in range(shape[0])])
def _pseudo_sigma_to_sigma(pseudo_sigma):
"""Returns a legal non-singular covariance matrix from a "pseudo_sigma" tensor."""
pseudo_sigma = tf.linalg.band_part(pseudo_sigma, 0, -1) # take only upper half.
sigma = pseudo_sigma @ tf.transpose(pseudo_sigma)
# make sure it's non singular
sigma += tf.cast(tf.identity(sigma.shape[0]), sigma.dtype) * 1e-6
return sigma
class SumOfGaussians(tf.keras.layers.Layer):
"""Assumes data is a sum of multiple Gaussians and learns the means, covariances and amplitudes."""
def __init__(self, num_gaussians, amps_l1_reg=1e-3, use_means_spread_regularizer=True,
force_amps_nonneg=True, centers_min=0., centers_max=1.,
super(SumOfGaussians, self).__init__(**kwargs)
self.num_gaussians = num_gaussians
self.amps_regularizer = tf.keras.regularizers.l1(amps_l1_reg)
self.amps_constraint = (tf.keras.constraints.NonNeg()
if force_amps_nonneg
else None)
self.centers_regularizer = (_means_spread_regularizer
if use_means_spread_regularizer
else None)
self.centers_min = np.array(centers_min)
self.centers_max = np.array(centers_max)
def build(self, input_shape):
self.dim = input_shape[-1]
if (self.centers_min.shape != self.centers_max.shape) or (self.centers_min.shape not in ((), (self.dim,))):
raise ValueError('Illegal centers_min, centers_max. Must be either a number or a (dim,) shape array')
self.centers_initializer = tf.keras.initializers.RandomUniform(
minval=self.centers_min, maxval=self.centers_max)
self.means = self.add_weight(
'means', shape=(self.num_gaussians, self.dim), dtype=tf.float32,
self.pseudo_sigmas = self.add_weight(
'pseudo_sigmas', shape=(self.num_gaussians, self.dim, self.dim), dtype=tf.float32,
self.amps = self.add_weight(
'amps', shape=(self.num_gaussians,), dtype=tf.float32,
def _get_sigma(self, i):
return _pseudo_sigma_to_sigma(self.pseudo_sigmas[i])
def get_current_amps_regularizer_value(self):
return self.amps_regularizer(self.amps)
def call(self, inputs, **kwargs):
del kwargs # unused.
per_gaussian_output = [
calculate_multivariate_gaussian(inputs, self.amps[i], self.means[i], self._get_sigma(i))
for i in range(self.num_gaussians)]
result = tf.reduce_sum(tf.stack(per_gaussian_output, axis=-1), axis=-1, keepdims=True)
return result