tf.contrib.learn.monitors.ExportMonitor.every_n_step_end(step, outputs)

tf.contrib.learn.monitors.LoggingTrainable.begin(max_steps=None) Called at the beginning of training. When

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

tf.contrib.learn.TensorFlowRNNRegressor.fit(x, y, steps=None, monitors=None, logdir=None) Neural network model from provided

tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGamma') Construct

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

tf.contrib.distributions.Normal.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='Normal') Construct Normal

tf.contrib.distributions.StudentT.allow_nan_stats Python boolean describing behavior when a stat is undefined.

tf.contrib.graph_editor.ph(dtype, shape=None, scope=None) Create a tf.placeholder for the Graph Editor. Note

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