class tf.contrib.learn.monitors.GraphDump Dumps almost all tensors in the graph at every step. Note

tf.add_n(inputs, name=None) Adds all input tensors element-wise. Args:

tf.contrib.distributions.Exponential.get_batch_shape() Shape of a single sample from a single event index as a TensorShape

tf.contrib.distributions.MultivariateNormalDiag.mode(name='mode') Mode.

tf.contrib.learn.TensorFlowRNNRegressor.config

tf.contrib.distributions.Dirichlet.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32

tf.contrib.learn.run_feeds(*args, **kwargs) See run_feeds_iter(). Returns a list instead of an iterator.

tf.cholesky(input, name=None) Computes the Cholesky decomposition of one or more square matrices. The

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

tf.contrib.learn.monitors.GraphDump.begin(max_steps=None)