Creates the graph for Gaussian mixture model (GMM) clustering.
tf.contrib.factorization.gmm(
inp, initial_clusters, num_clusters, random_seed,
covariance_type=FULL_COVARIANCE, params='wmc'
)
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.
|