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

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.loss(final_loss, name='Loss')

tf.nn.rnn_cell.LSTMStateTuple.c Alias for field number 0

tf.nn.rnn_cell.BasicLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shap

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

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

tf.contrib.distributions.NormalWithSoftplusSigma.mean(name='mean') Mean.

tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Dirichlet.alpha Shape parameter.

tf.contrib.graph_editor.SubGraphView.op(op_id) Get an op by its index.