tf.nn.rnn_cell.LSTMCell.state_size

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.graph

tf.contrib.training.NextQueuedSequenceBatch.sequences A dict mapping keys of input_sequences to split and rebatched data. Returns: A dict mapping keys of input_sequences to tensors. If we had at input: sequences["name"].get_shape() == [None, d1, d2, ...] where None meant the sequence time was dynamic, then for this property: sequences["name"].get_shape() == [batch_size, num_unroll, d1, d2, ...].

tf.contrib.graph_editor.remove_control_inputs(op, cops) Remove the control inputs cops from co. Warning: this function is directly manipulating the internals of the tf.Graph. Args: op: a tf.Operation from which to remove the control inputs. cops: an object convertible to a list of tf.Operation. Raises: TypeError: if op is not a tf.Operation ValueError: if any cop in cops is not a control input of op.

tf.contrib.graph_editor.SubGraphView.remap_inputs(new_input_indices) Remap the inputs of the subgraph. If the inputs of the original subgraph are [t0, t1, t2], remapping to [2,0] will create a new instance whose inputs is [t2, t0]. Note that this is only modifying the view: the underlying tf.Graph is not affected. Args: new_input_indices: an iterable of integers representing a mapping between the old inputs and the new ones. This mapping can be under-complete and must be without repetitions.

tf.contrib.distributions.QuantizedDistribution.variance(name='variance') Variance.

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

class tf.FixedLenFeature Configuration for parsing a fixed-length input feature. To treat sparse input as dense, provide a default_value; otherwise, the parse functions will fail on any examples missing this feature. Fields: shape: Shape of input data. dtype: Data type of input. default_value: Value to be used if an example is missing this feature. It must be compatible with dtype.

tf.contrib.distributions.MultivariateNormalFull.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.Gamma.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.