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Class GmmAlgorithm
Tensorflow Gaussian mixture model clustering class.
__init__
__init__(
data,
num_classes,
initial_means=None,
params='wmc',
covariance_type=FULL_COVARIANCE,
random_seed=0
)
Constructor.
Args:
data: a list of Tensors with data, each row is a new example.num_classes: number of clusters.initial_means: a Tensor with a matrix of means. If None, means are computed by sampling randomly.params: Controls which parameters are updated in the training process. Can contain any combination of "w" for weights, "m" for means, and "c" for covariances.covariance_type: one of "full", "diag".random_seed: Seed for PRNG used to initialize seeds.
Raises:
Exception if covariance type is unknown.
Methods
tf.contrib.factorization.GmmAlgorithm.alphas
alphas()
tf.contrib.factorization.GmmAlgorithm.assignments
assignments()
Returns a list of Tensors with the matrix of assignments per shard.
tf.contrib.factorization.GmmAlgorithm.clusters
clusters()
Returns the clusters with dimensions num_classes X 1 X num_dimensions.
tf.contrib.factorization.GmmAlgorithm.covariances
covariances()
Returns the covariances matrices.
tf.contrib.factorization.GmmAlgorithm.init_ops
init_ops()
Returns the initialization operation.
tf.contrib.factorization.GmmAlgorithm.is_initialized
is_initialized()
Returns a boolean operation for initialized variables.
tf.contrib.factorization.GmmAlgorithm.log_likelihood_op
log_likelihood_op()
Returns the log-likelihood operation.
tf.contrib.factorization.GmmAlgorithm.scores
scores()
Returns the per-sample likelihood fo the data.
Returns:
Log probabilities of each data point.
tf.contrib.factorization.GmmAlgorithm.training_ops
training_ops()
Returns the training operation.