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.

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.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.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.