tf.contrib.framework.add_arg_scope(func) Decorates a function with args so it can be used within an arg_scope. Args: func: function to decorate. Returns: A tuple with the decorated function func_with_args().

class tf.nn.rnn_cell.BasicRNNCell The most basic RNN cell.

string tensorflow::TensorShapeUtils::ShapeListString(const gtl::ArraySlice< TensorShape > &shapes)

tf.SparseTensor.indices The indices of non-zero values in the represented dense tensor. Returns: A 2-D Tensor of int64 with shape [N, ndims], where N is the number of non-zero values in the tensor, and ndims is the rank.

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

tf.contrib.distributions.NormalWithSoftplusSigma.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None) Most basic RNN: output = new_state = activation(W * input + U * state + B).

tf.contrib.distributions.DirichletMultinomial.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.learn.LinearClassifier.get_estimator()

tf.contrib.distributions.TransformedDistribution.is_continuous