tf.contrib.learn.TensorFlowRNNClassifier.fit(x, y, steps=None, monitors=None, logdir=None) Neural network model from provided model_fn and training data. Note: called first time constructs the graph and initializers variables. Consecutives times it will continue training the same model. This logic follows partial_fit() interface in scikit-learn. To restart learning, create new estimator. Args: x: matrix or tensor of shape [n_samples, n_features...]. Can be iterator that returns arrays of featu

tf.contrib.learn.TensorFlowRNNClassifier.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.learn.LinearRegressor.dnn_weights_ Returns weights of deep neural network part.

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.name

tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.loss(final_loss, name='Loss')

tf.contrib.learn.monitors.CheckpointSaver.begin(max_steps=None)

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_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.distributions.Binomial.dtype The DType of Tensors handled by this Distribution.

tf.FixedLengthRecordReader.reset(name=None) Restore a reader to its initial clean state. Args: name: A name for the operation (optional). Returns: The created Operation.