tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.name

tf.nn.rnn_cell.OutputProjectionWrapper.__call__(inputs, state, scope=None) Run the cell and output projection on inputs, starting

tf.get_session_tensor(handle, dtype, name=None) Get the tensor of type dtype by feeding a tensor handle.

tf.contrib.learn.LinearRegressor.__repr__()

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

tf.contrib.learn.DNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See

tf.contrib.distributions.Beta.a_b_sum Sum of parameters.

tf.contrib.distributions.Dirichlet.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.learn.monitors.PrintTensor.epoch_begin(epoch) Begin epoch. Args: