AlexNet | implement AlexNet with C / convolutional nerual network | Machine Learning library

I am trying to implement GoogleNet inception network to classify images for classification project that I am working on, I used the same code before but with AlexNet network and the training was fine, but once I changed the network to GoogleNet architecture the code kept throwing the following error:

Currently, I'm working on an image motion deblurring problem with PyTorch. I have two kinds of images: Blurry images (variable = blur_image) that are the input image and the sharp version of the same images (variable = shar_image), which should be the output. Now I wanted to try out transfer learning, but I can't get it to work.

Here is the code for my dataloaders:

I'm making a code for AlexNet and i'm confused with how to initialize the weights

what is the difference between:

I am a beginner of AlexNet neural network.I'm making an autopilot for a simple racing game.Here is the code I used to train my model with a file named training_data_n1.npy. I currently have three .npy files, so how can I modify my code so that I can train my model with three or more .npy files simultaneously (without merging these .npy files into one .npy file).I would appreciate it if you could provide the code. :)

I'm looking to implement a RNN along with a CNN in order to make a prediction based on two images instead of one alone with a CNN. I'm trying to modify the alexnet model code:

Here is my code. The packages imported are not shown. I am trying to feed the CIFAR-10 test data into alexnet. The dictionary at the end needs to be sorted so I can find the most common classification. Please help, I have tried everything!

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I am trying to understand the training process of a object deetaction deeplearng algorithm and I am having some problems understanding how the backbone network (the network that performs feature extraction) is trained.

I understand that it is common to use CNNs like AlexNet, VGGNet, and ResNet but I don't understand if these networks are pre-trained or not. If they are not trained what does the training consist of?

I need to run a custom GluonCV object detection module on Android.

I already fine-tuned the model (ssd_512_mobilenet1.0_custom) on a custom dataset, I tried running inference with it (loading the .params file produced during the training) and everything works perfectly on my computer. Now, I need to export this to Android.

I was referring to this answer to figure out the procedure, there are 3 suggested options:

1. You can use ONNX to convert models to other runtimes, for example [...] NNAPI for Android
2. You can use TVM
3. You can use SageMaker Neo + DLR runtime [...]

Regarding the first one, I converted my model to ONNX. However, in order to use it with NNAPI, it is necessary to convert it to daq. In the repository, they provide a precomplied AppImage of onnx2daq to make the conversion, but the script returns an error. I checked the issues section, and they report that "It actually fails for all onnx object detection models".

Then, I gave a try to DLR, since it's suggested to be the easiest way. As I understand, in order to use my custom model with DLR, I would first need to compile it with TVM (which also covers the second point mentioned in the linked post). In the repo, they provide a Docker image with some conversion scripts for different frameworks. I modified the 'compile_gluoncv.py' script, and now I have:

I'd like to use specialized configuration as per Hydra documentation in Common Patterns -> Specializing Configuration. The difference is that my specialized configuration is in a file, not just one variable. In the example below I want to choose transform based on the model and the dataset. The configs for different transforms are in files. This would work if I specified all the transform configuration in dataset_model/cifar10_alexnet.yaml file, but that would defeat the purpose because I can't reuse the transform config in this case. Alsewhere in Hydra if you specify the name of the file it would automatically pick up the config in that file, but it does not seem to work in the specialized configuration.

I've modified the example in documentation as follows:

config.yaml: