tf.contrib.learn.LinearClassifier.bias_

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) e

tf.contrib.learn.extract_dask_labels(labels) Extract data from dask.Series for labels.

tf.contrib.learn.infer(restore_checkpoint_path, output_dict, feed_dict=None) Restore graph from restore_checkpoint_path and run output_dict tensors. If restore_checkpoint_path is supplied, restore from checkpoint. Otherwise, init all variables. Args: restore_checkpoint_path: A string containing the path to a checkpoint to restore. output_dict: A dict mapping string names to Tensor objects to run. Tensors must all be from the same graph. feed_dict: dict object mapping Tensor objects to input

tf.contrib.learn.extract_pandas_data(data) Extract data from pandas.DataFrame for predictors. Given a DataFrame, will extract the values and cast them to float. The DataFrame is expected to contain values of type int, float or bool. Args: data: pandas.DataFrame containing the data to be extracted. Returns: A numpy ndarray of the DataFrame's values as floats. Raises: ValueError: if data contains types other than int, float or bool.

tf.contrib.learn.extract_pandas_labels(labels) Extract data from pandas.DataFrame for labels. Args: labels: pandas.DataFrame or pandas.Series containing one column of labels to be extracted. Returns: A numpy ndarray of labels from the DataFrame. Raises: ValueError: if more than one column is found or type is not int, float or bool.

tf.contrib.learn.extract_pandas_matrix(data) Extracts numpy matrix from pandas DataFrame. Args: data: pandas.DataFrame containing the data to be extracted. Returns: A numpy ndarray of the DataFrame's values.

tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None, feature_engineering_fn=None) Constructs an Estimator instance. Args: model_fn: Model function, takes features and targets tensors or dicts of tensors and returns predictions and loss tensors. Supports next three signatures for the function: (features, targets) -> (predictions, loss, train_op) (features, targets, mode) -> (predictions, loss, train_op) (features, targets, mode, params) -> (pred

tf.contrib.learn.Estimator.__repr__()

tf.contrib.learn.extract_dask_data(data) Extract data from dask.Series or dask.DataFrame for predictors.