tf.nn.rnn_cell.OutputProjectionWrapper.state_size

tf.contrib.distributions.MultivariateNormalCholesky.is_continuous

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.graph

tf.contrib.distributions.Exponential.lam

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.graph

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.mean(name='mean')

tf.contrib.graph_editor.swap_inputs(sgv0, sgv1) Swap all the inputs of sgv0 and sgv1 (see reroute_inputs).

tf.contrib.rnn.GridLSTMCell.state_tuple_type

tf.contrib.training.SequenceQueueingStateSaver.batch_size

tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.