Comments (3)
MarcBS
commented on August 21, 2024
If you are simply interested in converting models and using the normal layers available in Keras then there should be no problem. You should be able to use either TF or TH.
The thing is that we can only assure that the additional multimodal layers that we have developed are working on TH. Not all of them are guaranteed to work on TF.
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AliaMYH
commented on August 21, 2024
The multimodal layers being the LSTMs etc? If my model only has the typical conv, maxpooling and dropout etc it should be fine ?
My goal is to convert the models, and then train them, or use them to make predictions
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lvapeab
commented on August 21, 2024
Closing this, as we now fully support TF.
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Related Issues (20)
- Recurrent dropout is broken in (at least) AttGRUCond HOT 1
- Deconvolution layer not supported, while converting caffemodel weight into keras. HOT 3
- Test WeightNorm optimizers HOT 1
- Use non-standard version of caffe for model conversion. HOT 3
- Bug when run caffe2keras HOT 1
- Hi
- hi when i run test_segmentation.py i receive this error str(n) + ' integers. Received: ' + str(value)) ValueError: The `1st entry of padding` argument must be a tuple of 2 integers. Received: 100
- when i run test_segmentation.py i receive this essor str(n) + ' integers. Received: ' + str(value)) ValueError: The `1st entry of padding` argument must be a tuple of 2 integers. Received: 100
- Conversion of PoolingND Layer
- python3 support for caffe2keras HOT 1
- Error when converting with merge layer
- the accuracy decrease appearently after converting caffe to keras. HOT 1
- Error when converting with Scale layer HOT 1
- TypeError: __call__() takes 2 positional arguments but 4 were given HOT 4
- no module named 'keras_applications' HOT 1
- TypeError: expected bytes, str found HOT 4
- Negative dimension size for 'pool2/MaxPool' (op: 'MaxPool') with input shapes:[?,1,112,128].
- Hi, I'm just wondering why you coded like this.
- Caffe2keras.py: ValueError: need more than 3 values to unpack in convert.py
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