tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.graph

tf.contrib.distributions.DirichletMultinomial.alpha Parameter defining this distribution.

tf.contrib.distributions.Laplace.parameters Dictionary of parameters used by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.graph

tf.nn.rnn_cell.LSTMStateTuple.__repr__() Return a nicely formatted representation string

tf.contrib.distributions.WishartCholesky.name Name prepended to all ops created by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.name

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.dtype

tf.contrib.distributions.Dirichlet.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.learn.TensorFlowRNNRegressor.__init__(rnn_size, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, attn_length=None, attn_size=None, attn_vec_size=None, n_classes=0, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1) Initializes a TensorFlowRNNRegressor instance. Args: rnn_size: The size for rnn cell, e.g. size of your word embe