tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_s

tf.contrib.distributions.InverseGamma.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.contrib.distributions.LaplaceWithSoftplusScale.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.contrib.distributions.MultivariateNormalCholesky.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.

class tf.nn.rnn_cell.MultiRNNCell RNN cell composed sequentially of multiple simple cells.

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: if state_is_tuple is False, this must be a state Tensor, 2-D, batch x state_size. If state_is_tuple is True, this must be a tuple of state Tensors, both 2-D, with column sizes c_state and m_state. scope: VariableScope for the created subgraph; defaults to "LSTMCell". Returns: A tuple containing: - A 2-D, [batch x output_dim], Ten

tf.contrib.distributions.DirichletMultinomial.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.

tf.contrib.training.NextQueuedSequenceBatch.save_state(state_name, value, name=None) Returns an op to save the current batch of state state_name. Args: state_name: string, matches a key provided in initial_states. value: A Tensor. Its type must match that of initial_states[state_name].dtype. If we had at input: initial_states[state_name].get_shape() == [d1, d2, ...] then the shape of value must match: tf.shape(value) == [batch_size, d1, d2, ...] name: string (optional). The name scope for

tf.FixedLengthRecordReader.read(queue, name=None) Returns the next record (key, value pair) produced by a reader. Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. name: A name for the operation (optional). Returns: A tuple of Tensors (key, value). key: A string scalar Tensor. v

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.dtype