tf.contrib.factorization.gmm(
inp,
initial_clusters,
num_clusters,
random_seed,
covariance_type=FULL_COVARIANCE,
params='wmc'
)
Defined in tensorflow/contrib/factorization/python/ops/gmm_ops.py
.
Creates the graph for Gaussian mixture model (GMM) clustering.
Args:
inp
: An input tensor or list of input tensorsinitial_clusters
: Specifies the clusters used during initialization. Can be a tensor or numpy array, or a function that generates the clusters. Can also be "random" to specify that clusters should be chosen randomly from input data. Note: type is diverse to be consistent with skflow.num_clusters
: number of clusters.random_seed
: Python integer. Seed for PRNG used to initialize centers.covariance_type
: one of "diag", "full".params
: Controls which parameters are updated in the training process. Can contain any combination of "w" for weights, "m" for means, and "c" for covars.
Returns:
Note
: tuple of lists returned to be consistent with skflow A tuple consisting of:assignments
: A vector (or list of vectors). Each element in the vector corresponds to an input row in 'inp' and specifies the cluster id corresponding to the input.training_op
: an op that runs an iteration of training.init_op
: an op that runs the initialization.