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.
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I am currently turning my Binary Classification Model to a multi-class classification Model. Bare with me.. I am very knew to pytorch and Machine Learning.
Most of what I state here, I know from the following video.
https://www.youtube.com/watch?v=7q7E91pHoW4&t=654s
What I read / know is that the CrossEntropyLoss already has the Softmax function implemented, thus my output layer is linear.
What I then read / saw is that I can just choose my Model prediction by taking the torch.max() of my model output (Which comes from my last linear output. This feels weird because I Have some negative outputs and i thought I need to apply the SOftmax function first, but It seems to work right without it.
So know the big confusing question I have is, when would I use the Softmax function? Would I only use it when my loss doesnt have it implemented? BUT then I would choose my prediction based on the outputs of the SOftmax layer which wouldnt be the same as with the linear output layer.
Thank you guys for every answer this gets.
For calculating the loss using CrossEntropy you do not need softmax because CrossEntropy already includes it. However to turn model outputs to probabilities you still need to apply softmax to turn them into probabilities.
Lets say you didnt apply softmax at the end of you model. And trained it with crossentropy. And then you want to evaluate your model with new data and get outputs and use these outputs for classification. At this point you can manually apply softmax to your outputs. And there will be no problem. This is how it is usually done.
Traning()
MODEL ----> FC LAYER --->raw outputs ---> Crossentropy Loss
Eval()
MODEL ----> FC LAYER --->raw outputs --> Softmax -> Probabilites
Yes you need to apply softmax on the output layer. When you are doing binary classification you are free to use relu, sigmoid,tanh etc activation function. But when you are doing multi class classification softmax is required because softmax activation function distributes the probability throughout each output node. So that you can easily conclude that the output node which has the highest probability belongs to a particular class. Thank you. Hope this is useful!
I'm using Windows 10 machine.
Libraries: Keras with Tensorflow 2.0
Embeddings:Glove(100 dimensions)
I am trying to implement an LSTM architecture for multi-label text classification.
My problem is that no matter how much fine-tuning I do, the results are really bad.
I am not experienced in DL practical implementations that's why I ask for your advice.
Below I will state basic information about my dataset and my model so far.
I can't embed images since I am a new member so they appear as links.
Dataset form+Embedings form+train-test-split form
Dataset's labels distribution
My Implementation of LSTM
Model's Summary
Model's Accuracy plot
Model's Loss plot
As you can see my dataset is really small (~6.000 examples) and maybe that's one reason why I cannot achieve better results. Still, I chose it because it's unbiased.
I'd like to know if there is any fundamental mistake in my code regarding the dimensions, shape, activation functions, and loss functions for multi-label text classification?
What would you recommend to achieve better results on my model? Also any general advice regarding optimizing, methods,# of nodes, layers, dropouts, etc is very welcome.
Model's best val accuracy that I achieved so far is ~0.54 and even if I tried to raise it, it seems stuck there.
There are many ways to get this wrong but the most common mistake is to get your model overfit the training data.
I suspect that 0.54 accuracy means that your model selects the most common label (offensive) for almost all cases.
So, consider one of these simple solutions:
Create balanced training data: like 400 samples from each class.
or sample balanced batches for training (exactly the same number of labels on each training batch)
In addition to tracking accuracy and loss, look at precision-recall-f1 or even better try plotting area under curve, maybe different classes need different thresholds of activation. (If you are using Sigmoid on last layer maybe one class could perform better with 0.2 activations and another class with 0.7)
first try simple model. embedding 1 layer LSTM than classify
how to tokenize text , is vocab size enough ?
try dice loss
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 have a set of sentences and their scores, I would like to train a marking system that could predict the score for a given sentence, such one example is like this:
(X =Tomorrow is a good day, Y = 0.9)
I would like to use LSTM to build such a marking system, and also consider the sequential relationship between each word in the sentence, so the training example shown above is transformed as following:
(x1=Tomorrow, y1=is) (x2=is, y2=a) (x3=a, y3=good) (x4=day, y4=0.9)
When training this LSTM, I would like the first three time steps using a softmax classifier, and the final step using a MSE. It is obvious that the loss function used in this LSTM is composed of two different loss functions. In this case, it seems the Keras does not provide the way to address my problem directly. In addition, I am not sure whether my method to build the marking system is correct or not.
Keras support multiple loss functions as well:
model = Model(inputs=inputs,
outputs=[lang_model, sent_model])
model.compile(optimizer='sgd',
loss=['categorical_crossentropy', 'mse'],
metrics=['accuracy'], loss_weights=[1., 1.])
Based on your explanation, I think you need a model that first, predict a token based on previous tokens, in NLP domain it usually called Language model, and then compute a score which I assume it is a sentiment (it is applicable to other domain).
To do so, you can train your language model with LSTM and pick the last output of LSTM for your ranking task. To this end, you need to define two loss function: categorical_crossentropy for the language model and MSE for the ranking task.
This tutorial would be helpful: https://www.pyimagesearch.com/2018/06/04/keras-multiple-outputs-and-multiple-losses/
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.