I am new to AI and I am using Keras and Tensorflow to train CNNs. My dataset is heavily unbalanced and I want to use class weights to counter that.
After a small search in the internet I found out that I can use scikit learn's class weight() and sample weight() to get the class weights and sample weights respectively and it can be passed to model.fit() in Keras. But I am unsure how to implement it programmatically for hot encoded outputs.
Can someone provide sample code explaining how to implement classweights for hot encoded outputs with Keras?
Thanks in advance 😁
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I want to extract features using a pretrained CNN model(ResNet50, VGG, etc) and use the features with a CTC loss function.
I want to build it as a text recognition model.
Anyone on how can i achieve this ?
I'm not sure if you are looking to finetune the pretrained models or to use the models for feature extraction. To do the latter freeze the petrained model weights (there are several ways to do this in PyTorch, the simplest being calling .eval() on the model), and feed the logits from the last layer of the model to your new output head. See the PyTorch tutorial here for a more in depth guide.
I'm trying to do domain adversarial training using gradient-reversal procedure. I have a deep-learning model architecture consisting of 5 dense layers. Now I want to extract the gradient, reverse them and then update the weights. I am not sure how to extract the gradients and finally how to add them to update the weights. I have gone through some example codes but I am still pretty doubtful regarding using Keras backend. Any help with some toy code or example with explanation is really appreciated.
Thank you,
I have to pre train a model for multi label classification. I'm pretraining with cifar10 dataset and I wonder if I have to use for the pre training
'categorical_crossentrpy' (softmax) or 'binary_crossentropy' (sigmoid), since in the first case I have a multi classification problem
You should use softmax because it gives you the probabilities for every class, no matter how many of them are there. Sigmoid, as you have written is used with binary_crossentropy and is used in binary classification (hence binary in the name). I hope it's clearer now.
I've implemented a neural network using Keras. Once trained and tested for final test accuracy, using a matrix with a bunch of rows containing features (plus corresponding labels), I have a model which I should be able to use for prediction.
How can I feed a single unseen example, meaning a feature vector to the model, to obtain a class prediction?
I've looked at their documentation here but could not find a method for it.
What you want is the predict method, it takes a batch of input samples and produces predictions, which are the outputs computer by your network. To feed a single example you can just put it inside a numpy ndarray wrapper.
I have been following the http://deeplearning.net/tutorial/ tutorial on how to train an ANN to classify the MNIST numbers. I am now at the "Convolutional Neural Networks" chapter. I want to use the trained network on single examples (MNIST images) and get the predictions. Is there a way to do that?
I have looked ahead in the tutorial and on google but can't find anything.
Thanks a lot in advance for any kind of help!
The material in the Theano tutorial in the earlier chapters, before reaching the Convolutional Neural Networks (CNN) chapter, give a good overview of how Theano works and some of the components the CNN sample code uses. It might be reasonable to assume that students reaching this point have developed their understanding of Theano sufficiently to figure out how to modify the code to extract the model's predictions. Here's a few hints.
The CNN's output layer, called layer3, is an instance of the LogisticRegression class, introduced in an earlier chapter.
The LogisticRegression class has an attribute called y_pred. The comments next to the code which assigns that attribute's values says
symbolic description of how to compute prediction as class whose
probability is maximal
Looking for places where y_pred is used in the logistic regression sample will highlight a function called predict(). This does for the logistic regression sample what is desired of the CNN example.
If one follows the same approach, using layer3.y_pred as the output of a new Theano function, the model's predictions will become apparent.