tf.contrib.factorization.GmmAlgorithm
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Tensorflow Gaussian mixture model clustering class.
tf.contrib.factorization.GmmAlgorithm(
data, num_classes, initial_means=None, params='wmc',
covariance_type=FULL_COVARIANCE, random_seed=0
)
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
alphas
View source
alphas()
assignments
View source
assignments()
Returns a list of Tensors with the matrix of assignments per shard.
clusters
View source
clusters()
Returns the clusters with dimensions num_classes X 1 X num_dimensions.
covariances
View source
covariances()
Returns the covariances matrices.
init_ops
View source
init_ops()
Returns the initialization operation.
is_initialized
View source
is_initialized()
Returns a boolean operation for initialized variables.
log_likelihood_op
View source
log_likelihood_op()
Returns the log-likelihood operation.
scores
View source
scores()
Returns the per-sample likelihood fo the data.
| Returns |
|---|
|
Log probabilities of each data point.
|
training_ops
View source
training_ops()
Returns the training operation.
Class Variables
CLUSTERS_COVS_VARIABLE = 'clusters_covs' CLUSTERS_VARIABLE = 'clusters' CLUSTERS_WEIGHT = 'alphas'
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.factorization.GmmAlgorithm\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L92-L459) |\n\nTensorflow Gaussian mixture model clustering class. \n\n tf.contrib.factorization.GmmAlgorithm(\n data, num_classes, initial_means=None, params='wmc',\n covariance_type=FULL_COVARIANCE, random_seed=0\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `data` | a list of Tensors with data, each row is a new example. |\n| `num_classes` | number of clusters. |\n| `initial_means` | a Tensor with a matrix of means. If None, means are computed by sampling randomly. |\n| `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. |\n| `covariance_type` | one of \"full\", \"diag\". |\n| `random_seed` | Seed for PRNG used to initialize seeds. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|---|---|\n| Exception if covariance type is unknown. ||\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `alphas`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L235-L236) \n\n alphas()\n\n### `assignments`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L246-L251) \n\n assignments()\n\nReturns a list of Tensors with the matrix of assignments per shard.\n\n### `clusters`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L238-L240) \n\n clusters()\n\nReturns the clusters with dimensions num_classes X 1 X num_dimensions.\n\n### `covariances`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L242-L244) \n\n covariances()\n\nReturns the covariances matrices.\n\n### `init_ops`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L223-L225) \n\n init_ops()\n\nReturns the initialization operation.\n\n### `is_initialized`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L231-L233) \n\n is_initialized()\n\nReturns a boolean operation for initialized variables.\n\n### `log_likelihood_op`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L261-L263) \n\n log_likelihood_op()\n\nReturns the log-likelihood operation.\n\n### `scores`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L253-L259) \n\n scores()\n\nReturns the per-sample likelihood fo the data.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| Log probabilities of each data point. ||\n\n\u003cbr /\u003e\n\n### `training_ops`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/factorization/python/ops/gmm_ops.py#L227-L229) \n\n training_ops()\n\nReturns the training operation.\n\nClass Variables\n---------------\n\n- `CLUSTERS_COVS_VARIABLE = 'clusters_covs'`\n- `CLUSTERS_VARIABLE = 'clusters'`\n- `CLUSTERS_WEIGHT = 'alphas'`"]]
View source on GitHub