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Dobiasd
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frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models in C++.
MIT License
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237
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Data required to create json
#425
johnatan151
opened
3 weeks ago
10
Cannot read neural network model file(.json).
#424
Alex555github
opened
3 months ago
1
Update to TensorFlow 2.17
#423
Dobiasd
closed
4 months ago
0
Sudden deviations for converted neural network
#422
MartinTum
closed
4 months ago
10
Access to individual elements (probabilities) of the object containing the result.
#421
Alex555github
closed
3 months ago
1
Remove unused and duplicate code
#420
Dobiasd
closed
7 months ago
0
Replace deprecated subdirs with add_subdirectory
#419
Dobiasd
closed
7 months ago
0
Support RNN and TextVectorization
#418
Alex555github
closed
3 months ago
1
Consider removing support for stateful layers/models
#417
Dobiasd
closed
7 months ago
10
Update to Tensorflow 2.16.1
#416
Dobiasd
closed
7 months ago
0
Support for views, spans, iterators?
#415
stellarpower
opened
8 months ago
4
Assert model type being tf.keras.models.Model, issue #413
#414
Dobiasd
closed
8 months ago
0
Converting a Keras yolov8 model with convert_model.py
#413
alejones
closed
8 months ago
5
example suggestion
#412
alan-man
closed
7 months ago
1
Auto-format C++ code
#411
Dobiasd
closed
10 months ago
0
Update TensorFlow to version 2.15.0
#410
Dobiasd
closed
10 months ago
0
Improve the install of cmake files
#409
trixirt
closed
11 months ago
1
Add support for UnitNormalization
#408
Dobiasd
closed
12 months ago
0
Implement LayerNormalization
#407
Dobiasd
closed
12 months ago
0
Improve softmax speed, fixes #405
#406
Dobiasd
closed
1 year ago
0
Slow Segmentation with MobileNetV3
#405
KevinWang905
closed
1 year ago
5
MSYS2 Compiling/Linking Issues
#404
ftuffner
closed
7 months ago
5
Add support for bicubic interpolation in Upsampling2D layer, issue #401
#403
Dobiasd
closed
1 year ago
1
Fix softmax implementation, calculate max per depth slice, closes issue #393
#402
Dobiasd
closed
1 year ago
0
No love for bicubic interpolation ?
#401
Panjaksli
closed
1 year ago
10
Conan packages of this library are often outdated
#400
Dobiasd
closed
1 year ago
9
Error when installing with conan
#399
cemox35
closed
1 year ago
4
Do not compile when loading the model for conversion, fixes #397
#398
Dobiasd
closed
1 year ago
0
convert_model.py fails when a custom loss function was used in training
#397
dasmysh
closed
1 year ago
1
terminate called after throwing an instance of 'std::runtime_error'
#396
pinyuan-tsai
closed
1 year ago
5
Several compiler errors with Visual Studio 2022 (C++20)
#395
dasmysh
closed
1 year ago
8
Syntax error in hpp file
#394
cemox35
closed
1 year ago
8
Frugally-deep cannot pass check_test_outputs() when both permute and attention layers are used
#393
roberttangswitch
closed
1 year ago
16
Add MultiHeadAttention layer
#392
Dobiasd
closed
10 months ago
0
Add support for AdditiveAttention layer
#391
Dobiasd
closed
1 year ago
0
Add support for score_mode concat in Attention layer
#390
Dobiasd
closed
1 year ago
0
Simplify axis check in Normalization layer
#389
Dobiasd
closed
1 year ago
0
Use the keras model file format instead of legacy h5
#388
Dobiasd
closed
1 year ago
0
Add support for keepdims in global pooling layers
#387
Dobiasd
closed
1 year ago
0
Update Ubuntu in CI from 20.04 to 22.04
#386
Dobiasd
closed
1 year ago
0
Update TensorFlow and Keras to version 2.13.x
#385
Dobiasd
closed
1 year ago
0
Fix zero variance in a Normalization layer
#384
slibay
closed
1 year ago
1
AssertionError: Multiple normalization axes are not supported.
#383
lygooi3
closed
1 year ago
3
Add attention layer
#382
Dobiasd
closed
1 year ago
0
Missing inline keyword in tensor.hpp
#381
Fadi-B
closed
1 year ago
2
Add support for CategoryEncoding layer
#380
Dobiasd
closed
1 year ago
0
Is it possible to run seq2seq model
#379
bil-ash
closed
1 year ago
6
Improve the performance of dense_layer, by mvparakhin, closes #376
#378
Dobiasd
closed
1 year ago
0
Can't import fdeep into header file
#377
simonjeger
closed
1 year ago
3
Suggestion for dense_layer.hpp
#376
mvparakhin
closed
1 year ago
5
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