Loop

Loop

Description

Generic Looping construct.

 

Documentation illustration (illustration unavailable in the archive).

 

This loop has multiple termination conditions :

1. Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M.
2. Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not.

 

Input parameters

 

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

 Graphs in : cluster, ONNX model architecture.

M (optional, heterogeneous) – I : object, a maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional, heterogeneous) – B : object, a boolean termination condition. Optional. Pass empty string to skip.
Documentation illustration (illustration unavailable in the archive). v_initial (variadic) – V : array, the initial values of any loop-carried dependencies (values that change across loop iterations).

Documentation illustration (illustration unavailable in the archive).

 Parameters : cluster,

body : object, the graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies…). It has 1+N+K outputs: (condition, loop carried dependencies…, scan_outputs…). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

 name (optional) : string, name of the node.

Documentation illustration (illustration unavailable in the archive).

Output parameters

 

Documentation illustration (illustration unavailable in the archive). v_final_and_scan_outputs (variadic) – V : array, final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.

Type Constraints

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).