tf.contrib.distributions.TransformedDistribution.inverse Inverse function of transform, y => x.

tf.matrix_solve(matrix, rhs, adjoint=None, name=None) Solves systems of linear equations. Matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. Rhs is a tensor of shape [..., M, K]. The output is a tensor shape [..., M, K]. If adjoint is False then each output matrix satisfies matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]. If adjoint is True then each output matrix satisfies adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]. Args: m

tf.contrib.learn.TensorFlowRNNRegressor.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed

bool tensorflow::PartialTensorShapeUtils::AreCompatible(const gtl::ArraySlice< PartialTensorShape > &shapes0, const gtl::ArraySlice< PartialTensorShape > &shapes1)

tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

virtual Status tensorflow::Session::Extend(const GraphDef &graph)=0 Adds operations to the graph that is already registered with the Session . The names of new operations in "graph" must not exist in the graph that is already registered.

tf.contrib.framework.VariableDeviceChooser.__init__(num_tasks=0, job_name='ps', device_type='CPU', device_index=0) Initialize VariableDeviceChooser. Usage: To use with 2 parameter servers: VariableDeviceChooser(2) To use without parameter servers: VariableDeviceChooser() VariableDeviceChooser(device_type='GPU') # For GPU placement Args: num_tasks: number of tasks. job_name: String, a name for the parameter server job. device_type: Optional device type string (e.g. "CPU" or "GPU") device_in

tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Beta: Note that the argument x must be a non-negative floating point tensor whose shape can be broadcast with self.a and self.b. For fixed leading dimensions, the last dimension represents counts for the corresponding Beta distribution in self.a and self.b. x is only legal if 0 < x < 1. Args: value: float or double Tensor. na

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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: value:

tf.contrib.distributions.LaplaceWithSoftplusScale.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.