tf.contrib.distributions.Uniform.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.QueueBase.name The name of the underlying queue.

tf.contrib.distributions.Binomial.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.LaplaceWithSoftplusScale.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.BernoulliWithSigmoidP.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.learn.monitors.StopAtStep.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

tf.contrib.distributions.LaplaceWithSoftplusScale.dtype The DType of Tensors handled by this Distribution.

tf.contrib.framework.get_or_create_global_step(graph=None) Returns and create (if necessary) the global step variable. Args: graph: The graph in which to create the global step. If missing, use default graph. Returns: the tensor representing the global step variable.

tf.contrib.learn.monitors.BaseMonitor.step_begin(step) Callback before training step begins. You may use this callback to request evaluation of additional tensors in the graph. Args: step: int, the current value of the global step. Returns: List of Tensor objects or string tensor names to be run. Raises: ValueError: if we've already begun a step, or step < 0, or step > max_steps.