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

tf.contrib.metrics.streaming_mean_iou(*args, **kwargs) Calculate per-step mean Intersection-Over-Union (mIOU). (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each s

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.WishartFull.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.Chi2WithAbsDf.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.