tf.contrib.graph_editor.swap(sgv0, sgv1) Swap the inputs and outputs of sgv1 to sgv0 (see _reroute).

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args:

tf.contrib.layers.apply_regularization(regularizer, weights_list=None) Returns the summed penalty by applying regularizer to the weights_list. Adding a regularization penalty over the layer weights and embedding weights can help prevent overfitting the training data. Regularization over layer biases is less common/useful, but assuming proper data preprocessing/mean subtraction, it usually shouldn't hurt much either. Args: regularizer: A function that takes a single Tensor argument and returns

tf.zeta(x, q, name=None) Compute the Hurwitz zeta function \(\zeta(x, q)\). The Hurwitz zeta function is defined as: \zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x} Args: x: A Tensor. Must be one of the following types: float32, float64. q: 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.distributions.LaplaceWithSoftplusScale.loc Distribution parameter for the location.

tf.contrib.learn.DNNRegressor.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See Trainable. Raises: ValueError: If x or y are not None while input_fn is not None. ValueError: If both steps and max_steps are not None.

tf.contrib.distributions.Mixture.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

class tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue Ask the StochasticTensor for n samples and reshape the result. Sampling from a StochasticTensor increases the rank of the value by 1 (because each sample represents a new outer dimension). This ValueType requests n samples from StochasticTensors run within its context that the outer two dimensions are reshaped to intermix the samples with the outermost (usually batch) dimension. Example: # mu and sigma are both shaped (2, 3) mu

tf.contrib.graph_editor.remove_control_inputs(op, cops) Remove the control inputs cops from co. Warning: this function is directly manipulating the internals of the tf.Graph. Args: op: a tf.Operation from which to remove the control inputs. cops: an object convertible to a list of tf.Operation. Raises: TypeError: if op is not a tf.Operation ValueError: if any cop in cops is not a control input of op.

tf.contrib.rnn.LayerNormBasicLSTMCell.state_size