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.QuantizedDistribution.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.Uniform.std(name='std') Standard deviation.

class tf.errors.DeadlineExceededError Raised when a deadline expires before an operation could complete. This exception is not currently used.

class tf.errors.AlreadyExistsError Raised when an entity that we attempted to create already exists. For example, running an operation that saves a file (e.g. tf.train.Saver.save()) could potentially raise this exception if an explicit filename for an existing file was passed.

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

tf.contrib.distributions.LaplaceWithSoftplusScale.scale Distribution parameter for scale.

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