fix: Resolve fixed-size array issue#1282
Merged
Oceania2018 merged 1 commit intoSciSharp:masterfrom Jan 22, 2025
Merged
Conversation
Replace .ToArray() with .ToList() to allow dynamic modification of network_nodes in MapGraphNetwork()
Replaced .ToArray() with .ToList() to resolve the issue where .Add() was called on a fixed-size array.
This preventing the "Collection was of a fixed size" error when called something like this var model = keras.Model(new Tensors(new Tensor[] { encoder_inputs, decoder_inputs }), outputs: decoder_dense);
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Replace
.ToArray()with.ToList()to allow dynamic modification ofnetwork_nodesinMapGraphNetwork()Replaced
.ToArray()with.ToList()to resolve the issue where.Add()was called on a fixed-size array.This preventing the "Collection was of a fixed size" error when called something like this
var model = keras.Model(new Tensors(new Tensor[] { encoder_inputs, decoder_inputs }), outputs: decoder_dense);