tf.contrib.learn.DNNClassifier.predict_proba(*args, **kwargs) Returns prediction probabilities for given features. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below. Args: x: features. input_fn: Input function. If set, x and y must

tf.contrib.distributions.LaplaceWithSoftplusScale.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='LaplaceWithSoftplusScale')

tf.contrib.learn.Estimator.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.MultivariateNormalFull.mu

tf.contrib.learn.LinearRegressor.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.rnn.GridLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: state Tensor, 2D, batch x state_size. scope: VariableScope for the created subgraph; defaults to "LSTMCell". Returns: A tuple containing: - A 2D, batch x output_dim, Tensor representing the output of the LSTM after reading "inputs" when previous state was "state". Here output_dim is num_units. - A 2D, batch x state_size, Tensor representing the ne

tf.contrib.rnn.TimeFreqLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: state Tensor, 2D, batch x state_size. scope: VariableScope for the created subgraph; defaults to "TimeFreqLSTMCell". Returns: A tuple containing: - A 2D, batch x output_dim, Tensor representing the output of the LSTM after reading "inputs" when previous state was "state". Here output_dim is num_units. - A 2D, batch x state_size, Tensor represe

tf.contrib.distributions.Binomial.mode(name='mode') Mode. Additional documentation from Binomial: Note that when (n + 1) * p is an integer, there are actually two modes. Namely, (n + 1) * p and (n + 1) * p - 1 are both modes. Here we return only the larger of the two modes.

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.name

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.