LostTech.TensorFlow : API Documentation

Type LinearRegressor

Namespace tensorflow.contrib.learn

Parent Estimator

Interfaces ILinearRegressor

Linear regressor model.

THIS CLASS IS DEPRECATED. See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) for general migration instructions.

Train a linear regression model to predict label value given observation of feature values.

Example: Input of `fit` and `evaluate` should have following features, otherwise there will be a KeyError:

* if `weight_column_name` is not `None`: key=weight_column_name, value=a `Tensor` * for column in `feature_columns`: - if isinstance(column, `SparseColumn`): key=column.name, value=a `SparseTensor` - if isinstance(column, `WeightedSparseColumn`): {key=id column name, value=a `SparseTensor`, key=weight column name, value=a `SparseTensor`} - if isinstance(column, `RealValuedColumn`): key=column.name, value=a `Tensor`
Show Example
sparse_column_a = sparse_column_with_hash_bucket(...)
            sparse_column_b = sparse_column_with_hash_bucket(...) 

sparse_feature_a_x_sparse_feature_b = crossed_column(...)

estimator = LinearRegressor( feature_columns=[sparse_column_a, sparse_feature_a_x_sparse_feature_b])

# Input builders def input_fn_train: # returns x, y ... def input_fn_eval: # returns x, y ... estimator.fit(input_fn=input_fn_train) estimator.evaluate(input_fn=input_fn_eval) estimator.predict(x=x)



Public static methods

LinearRegressor NewDyn(object feature_columns, object model_dir, object weight_column_name, object optimizer, object gradient_clip_norm, ImplicitContainer<T> enable_centered_bias, ImplicitContainer<T> label_dimension, ImplicitContainer<T> _joint_weights, object config, object feature_engineering_fn)

Construct a `LinearRegressor` estimator object.
object feature_columns
An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`.
object model_dir
Directory to save model parameters, graph, etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
object weight_column_name
A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example.
object optimizer
An instance of `tf.Optimizer` used to train the model. If `None`, will use an Ftrl optimizer.
object gradient_clip_norm
A `float` > 0. If provided, gradients are clipped to their global norm with this clipping ratio. See tf.clip_by_global_norm for more details.
ImplicitContainer<T> enable_centered_bias
A bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias.
ImplicitContainer<T> label_dimension
Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `[batch_size, label_dimension]`).
ImplicitContainer<T> _joint_weights
If True use a single (possibly partitioned) variable to store the weights. It's faster, but requires all feature columns are sparse and have the 'sum' combiner. Incompatible with SDCAOptimizer.
object config
`RunConfig` object to configure the runtime settings.
object feature_engineering_fn
Feature engineering function. Takes features and labels which are the output of `input_fn` and returns features and labels which will be fed into the model.
A `LinearRegressor` estimator.

Public properties

object config get;

object config_dyn get;

string model_dir get;

object model_dir_dyn get;

object model_fn get;

object model_fn_dyn get;

object PythonObject get;