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tf.contrib.factorization.gmm

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 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.