class tf.contrib.learn.monitors.EveryN Base class for monitors that execute callbacks every N steps. This class adds three new callbacks: - every_n_step_begin - every_n_step_end - every_n_post_step The callbacks are executed every n steps, or optionally every step for the first m steps, where m and n can both be user-specified. When extending this class, note that if you wish to use any of the BaseMonitor callbacks, you must call their respective super implementation: def step_begin(self, step

tf.contrib.learn.monitors.EveryN.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.contrib.learn.monitors.BaseMonitor.post_step(step, session) Callback after the step is finished. Called after step_end and receives session to perform extra session.run calls. If failure occurred in the process, will be called as well. Args: step: int, global step of the model. session: Session object.

tf.contrib.distributions.Uniform.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.learn.monitors.GraphDump.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

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

tf.contrib.graph_editor.SubGraphView.copy() Return a copy of itself. Note that this class is a "view", copying it only create another view and does not copy the underlying part of the tf.Graph. Returns: A new instance identical to the original one.

tf.errors.UnimplementedError.__init__(node_def, op, message) Creates an UnimplementedError.

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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.