# tf.contrib.factorization.gmm

tf.contrib.factorization.gmm(
inp,
initial_clusters,
num_clusters,
random_seed,
covariance_type=FULL_COVARIANCE,
params='wmc'
)


Creates the graph for Gaussian mixture model (GMM) clustering.

#### Args:

• inp: An input tensor or list of input tensors
• initial_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.