I have successfully used LeNet model to train my own dataset using Siamese Network using this tutorial. Now I want to use AlexNet as I believe it is more powerful than LeNet. Can someone provide guidelines or tutorial to use the AlexNet in Siamese Network.
You should go through this github repository. Specially the models and examples section. You can get the implementation of AlexNet in caffe here.
N.B. Please don't flag this post because of its length or sharing link. Links that I shared in this answer contains large code which I cannot post as an answer.
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I have a large set of Training data which consists of various texts. They should be the input for my neural network. I have no output, or I don't know what to put as output.
Anyway, after the learning phase I want the neural network to create new texts based on the training data.
I read about this like „I made a bot watch 1000 hours of xy and asked it to write a new xy“.
Now my question is, what kind of machine learning is this? I am not looking for instructions on how to write it, but just a hint on how to find some keywords or tutorials. My Google searches so far were useless.
Your problem can usually be solved by an Encoder-Decoder architecture. This architecture would learn a set of latent vectors from your input, then try to output in whatever form you want. This architecture can be built with RNN, LSTM or CNN. Nowadays, attention-based models like transformers are more common among the big names. If you want to do text generation, you can start by reading about Generative Adversarial Networks (GANs).
After searching questions on SO and reddit, I can't figure out how to train a multiple input, multiple output classifier on a ML Text Classifier. I can train a single input, single output text classifier but that doesnt fit my use case.
Any help would be appreciated. I understand that there's no code to post, and that this is sort of a "show me how" question, but this information seems not readily available via searching and elsewhere, and would be beneficial to the community.
The classifier objects provided by Core ML (and Create ML) are for very specific use cases. If you try to do anything more advanced than that, you'll have to create a custom model, such as your own neural network.
I have just started coding with TensorFlow and I have classified Images.
Is there any possibility of making a prediction based on testing data?
How can I predict missing value based on the model?
Question
Does Tensorflow have the capability to read training data and test data from two separate files?
Answer
Yes! Here is an example of processing Iris data, an intro machine learning data set for Tensorflow.
If you look at the code you will see the following lines
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"
The data is clearly separated into training and test files. You don't have to separate your data into different files in Tensorflow, but it certainly supports it and this tutorial link shows how to do it.
Looking for scientific article references for the network architecture presented in Deep MNIST for Experts tutorial (https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html)
I have a similar image processing data and I'm looking for a good vanilla architecture, any recommendations?
Currently the best solution for this problem are wavelet transform based solutions
You probably don't want to look at Deep MNIST for Experts as an example of a good architecture for MNIST or as a scientific baseline. It's more an example of basic Tensorflow building blocks and a nice introduction to convolutional models.
I.e, you should be able to get equal or better results with a model with 5% of the free parameters and less layers.
Some users might see this as opinion-based-question but if you look closely, I am trying to explore use of Caffe as a purely testing platform as opposed to currently popular use as training platform.
Background:
I have installed all dependencies using Jetpack 2.0 on Nvidia TK1.
I have installed caffe and its dependencies successfully.
The MNIST example is working fine.
Task:
I have been given a convnet with all standard layers. (Not an opensource model)
The network weights and bias values etc are available after training. The training has not been done via caffe. (Pretrained Network)
The weights and bias are all in the form of MATLAB matrices. (Actually in a .txt file but I can easily write code to get them to be matrices)
I CANNOT do training of this network with caffe and must used the given weights and bias values ONLY for classification.
I have my own dataset in the form of 32x32 pixel images.
Issue:
In all tutorials, details are given on how to deploy and train a network, and then use the generated .proto and .caffemodel files to validate and classify. Is it possible to implement this network on caffe and directly use my weights/bias and training set to classify images? What are the available options here? I am a caffe-virgin so be kind. Thank you for the help!
The only issue here is:
How to initialize caffe net from text file weights?
I assume you have a 'deploy.prototxt' describing the net's architecture (layer types, connectivity, filter sizes etc.). The only issue remaining is how to set the internal weights of caffe.Net to pre-defined values saved as text files.
You can get access to caffe.Net internals, see net surgery tutorial on how this can be done in python.
Once you are able to set the weights according to your text file, you can net.save(...) the new weights into a binary caffemodel file to be used from now on. You do not have to train the net if you already have trained weights, and you can use it for generating predictions ("test").