tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.distribution

tf.test.is_built_with_cuda() Returns whether TensorFlow was built with CUDA (GPU) support.

tf.QueueBase.dtypes The list of dtypes for each component of a queue element.

tf.contrib.learn.monitors.CheckpointSaver.end(session=None)

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.input_dict

tf.Session.as_default() Returns a context manager that makes this object the default session. Use with the with keyword to specify that calls to Operation.run() or Tensor.eval() should be executed in this session. c = tf.constant(..) sess = tf.Session() with sess.as_default(): assert tf.get_default_session() is sess print(c.eval()) To get the current default session, use tf.get_default_session(). N.B. The as_default context manager does not close the session when you exit the context, an

tf.contrib.distributions.InverseGamma.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.

virtual Status tensorflow::WritableFile::Sync()=0

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.loss(sample_loss) Returns the term to add to the surrogate loss. This method is called by surrogate_loss. The input sample_loss should have already had stop_gradient applied to it. This is because the surrogate_loss usually provides a Monte Carlo sample term of the form differentiable_surrogate * sample_loss where sample_loss is considered constant with respect to the input for purposes of the gradient. Args: sample_loss: Tensor, sam

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.loss(final_loss, name='Loss')