I used the Core ML Converter to convert a Caffe AlexNet model to a Core ML model. The model works just fine and outputs correct classification results. However, I do not know how to access the output of a layer inside the CNN model. Say for example I want to know what the output of one of the convolution layer (e.g. conv5) is. Caffe let you do so easily, but I could not find documentation on how to do this using Core ML.
Does Core ML allow access to outputs of layers inside the CNN model like Caffe does?
Related
I have a simple tensorflow model with lstm layers. I want to convert the model to .mlmodel format. However, I think, as of now, CoreML does not support LSTM layers and hence I need to write a custom lstm layer in swift.
How can I write that custom layer?
Why not try with Keras and use different tensorflow or other back-ends? the Keras has one of the easiest interfaces.
I suggest reading the tensorflow model using keras. Then use the link below to convert the keras to core-ml.
Try to make it simple. not complicated
https://heartbeat.fritz.ai/using-coremltools-to-convert-a-keras-model-to-core-ml-for-ios-d4a0894d4aba?gi=76f8b08071e9
I have trained and tested the pytorch based GAN model and now I want to deploy it to Android.
I have read about converting cnn model to onnx format and then to cafe2
but I don't know how to use it if there is more than one model i.e. generator, discriminator, encoder-decoder models.
i have a "simple" problem after loading Keras model.
During training my network has following structure
model.summary()
Output from command line after defining architecture
After training + saving + loading my network appears to have following structure:
Output from command line after loading
Unfortunately I'm unable to find the way to access the sequential layer. My goal is to visualize the feature maps which are within the sequential layer.
I will provide code in case it will be necessary.
Thank you,
Vaclav
I am trying to understand the caffe library. For that I run through step by step for feature_extraction.cpp and classification.cpp.
In those cpp files, I found out layers, prototxt file, caffemodel, net.cpp, caffe.pb.cc, caffe.pb.hfiles.
I know caffe is formed using different layers. So those layer files inside layer folder are used.
prototxt file is meant for the structure of a particular network such as googlenet, alexnet etc. Different net has different structure.
caffemodel is the trained model using caffe library for a specific type of net structure.
What do those net.cpp, caffe.pb.cc do? I mean how to understand their roles in forming this caffe deep learning network.
You understand correctly that caffe implements deep learning by stacking "layers" one on top of the other to form a "net".
'net.cpp'
Each layer works as a "functional block" and its behavior/implementation is defined in src/caffe/layers/<layer>.cpp, src/caffe/layers/<layer>.cu and include/caffe/layers/<layer>.hpp.
The code that actually "stack" all the layers into a net can be found (mostly) in net.cpp.
'caffe.pb.h', 'caffe.pb.cc'
In order to define the specific structure of a specific deep net architecture (e.g., AlexNet, GoogLeNet, ResNet etc.) caffe uses protocol-buffers library. The specific format of caffe protocol buffer is defined in src/caffe/proto/caffe.proto. The caffe.proto is "compiled" using google protobuffer compiler to produce 'caffe.pb.h' and 'caffe.pb.cc' c++ code for parsing and processing caffe prototxt and caffemodel files.
I've developed a model using Libsvm in Matlab. I've choose best parameters using CV and I obtained the model training the whole dataset. I use normalization to get better results:
maximum=max(TR)+0.00001;
minimum=min(TR);
for i=1:size(TR,2)
training(1:size(TR,1),i)=double(TR(1:size(TR,1),i)-maximum(i))/(maximum(i)-minimum(i));
end
Now how can I use directly my model to obtain classification for new data? I mean for records that haven't class label. Do I have to manually build functions from model information?
Are you using libsvmtrain to train on your training data? If so, there is an output argument that you can use to classify test/future data. Then pass that output structure to svmpredict along with test data.