uint64 tensorflow::EnvWrapper::NowMicros() override Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.

void tensorflow::EnvWrapper::SleepForMicroseconds(int64 micros) override Sleeps/delays the thread for the prescribed number of micro-seconds.

tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1) Run output_dict tensors n times, with the same feed_dict each run. Args: output_dict: A dict mapping string names to tensors to run. Must all be from the same graph. feed_dict: dict of input values to feed each run. restore_checkpoint_path: A string containing the path to a checkpoint to restore. n: Number of times to repeat. Returns: A list of n dict objects, each containing values read from output_di

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value_type

tf.contrib.learn.monitors.ExportMonitor.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.self_adjoint_eigvals(tensor, name=None) Computes the eigenvalues of one or more self-adjoint matrices. Args: tensor: Tensor of shape [..., N, N]. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. The vector e[..., :] contains the N eigenvalues of tensor[..., :, :].

tf.contrib.distributions.Normal.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Normal.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.contrib.distributions.NormalWithSoftplusSigma.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.contrib.distributions.Laplace.is_continuous