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.distributions.MultivariateNormalDiagPlusVDVT.log_sigma_det(name='log_sigma_det') Log of determinant of covariance matrix.

tf.contrib.distributions.TransformedDistribution.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.MultivariateNormalFull.dtype The DType of Tensors handled by this Distribution.

tf.contrib.learn.LinearClassifier.get_variable_value(name)

tf.errors.OutOfRangeError.__init__(node_def, op, message) Creates an OutOfRangeError.

tf.nn.rnn_cell.MultiRNNCell.output_size

tf.contrib.distributions.BernoulliWithSigmoidP.logits

tf.squared_difference(x, y, name=None) Returns (x - y)(x - y) element-wise. NOTE: SquaredDifference supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.learn.TensorFlowRNNClassifier.restore(cls, path, config=None) Restores model from give path. Args: path: Path to the checkpoints and other model information. config: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured. Returns: Estimator, object of the subclass of TensorFlowEstimator. Raises: ValueError: if path does not contain a model definition.