tf.contrib.learn.LinearClassifier

class tf.contrib.learn.LinearClassifier

Linear classifier model.

Train a linear model to classify instances into one of multiple possible classes. When number of possible classes is 2, this is binary classification.

Example:

education = sparse_column_with_hash_bucket(column_name="education",
                                           hash_bucket_size=1000)
occupation = sparse_column_with_hash_bucket(column_name="occupation",
                                            hash_bucket_size=1000)

education_x_occupation = crossed_column(columns=[education, occupation],
                                        hash_bucket_size=10000)

# Estimator using the default optimizer.
estimator = LinearClassifier(
    feature_columns=[occupation, education_x_occupation])

# Or estimator using the FTRL optimizer with regularization.
estimator = LinearClassifier(
    feature_columns=[occupation, education_x_occupation],
    optimizer=tf.train.FtrlOptimizer(
      learning_rate=0.1,
      l1_regularization_strength=0.001
    ))

# Or estimator using the SDCAOptimizer.
estimator = LinearClassifier(
   feature_columns=[occupation, education_x_occupation],
   optimizer=tf.contrib.linear_optimizer.SDCAOptimizer(
     example_id_column='example_id',
     num_loss_partitions=...,
     symmetric_l2_regularization=2.0
   ))

# 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)

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

if weight_column_name is not None, a feature with key=weight_column_name whose value is a Tensor.
for each column in feature_columns:
- if column is a SparseColumn, a feature with key=column.name whose value is a SparseTensor.
- if column is a WeightedSparseColumn, two features: the first with key the id column name, the second with key the weight column name. Both features' value must be a SparseTensor.
- if column is a RealValuedColumn, a feature with key=column.name whose value is a Tensor. - - -

Links:

https://www.tensorflow.org/versions/master/api_docs/python/contrib.learn.html#LinearClassifier

doc_TensorFlow

2016-10-14 13:05:48

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