oborchers

import numpy as np
REAL = np.float32 

def sif_embeddings(sentences, model, alpha=1e-3):
    """Compute the SIF embeddings for a list of sentences
    Parameters
    ----------
    sentences : list
        The sentences to compute the embeddings for
    model : `~gensim.models.base_any2vec.BaseAny2VecModel`

oborchers

from tensorflow_probability import distributions as tfd

def slice_parameter_vectors(parameter_vector):
    """ Returns an unpacked list of paramter vectors.
    """
    return [parameter_vector[:,i*components:(i+1)*components] for i in range(no_parameters)]

def gnll_loss(y, parameter_vector):
    """ Computes the mean negative log-likelihood loss of y given the mixture parameters.
    """

oborchers

import tensorflow as tf

def nnelu(input):
    """ Computes the Non-Negative Exponential Linear Unit
    """
    return tf.add(tf.constant(1, dtype=tf.float32), tf.nn.elu(input))

tf.keras.utils.get_custom_objects().update({'nnelu': Activation(nnelu)})

oborchers

from tensorflow.keras.layers import Input, Dense, Concatenate

neurons = 500     # Neurons of the DNN hidden layers
components = 2    # Number of components in the mixture
no_parameters = 3 # Paramters of the mixture (alpha, mu, sigma)

inputs = Input(shape=(x_train.shape[1],))
h1 = Dense(neurons, activation="relu")(inputs)
h2 = Dense(neurons, activation="relu")(h1)

oborchers

import numpy as np
import tensorflow as tf
from tensorflow_probability import distributions as tfd

tf.enable_eager_execution()

# Set values for the mixture
alphas = [0.6, 0.3, 0.1]
means = [30, 60, 120]
sigmas = [5, 3, 1]