I am doing transfer-learning/retraining using Tensorflow Inception V3 model. I have 6 labels. A given image can be one single type only, i.e, no multiple class detection is needed. I have three queries:
Which activation function is best for my case? Presently retrain.py file provided by tensorflow uses softmax? What are other methods available? (like sigmoid etc)
Which Optimiser function I should use? (GradientDescent, Adam.. etc)
I want to identify out-of-scope images, i.e. if users inputs a random image, my algorithm should say that it does not belong to the described classes. Presently with 6 classes, it gives one class as a sure output but I do not want that. What are possible solutions for this?
Also, what are the other parameters that we may tweak in tensorflow. My baseline accuracy is 94% and I am looking for something close to 99%.
Since you're doing single label classification, softmax is the best loss function for this, as it maps your final layer logit values to a probability distribution. Sigmoid is used when it's multilabel classification.
It's always better to use a momentum based optimizer compared to vanilla gradient descent. There's a bunch of such modified optimizers like Adam or RMSProp. Experiment with them to see what works best. Adam is probably going to give you the best performance.
You can add an extra label no_class, so your task will now be a 6+1 label classification. You can feed in some random images with no_class as the label. However the distribution of your random images must match the test image distribution, else it won't generalise.
Related
I have an image dataset with soft labels (i.e. the images don't belong to a single class, but rather I have a probability distribution saying that there's a 66% chance this image belong in one class and 33% chance it belongs in some other class).
I am struggling to figure out how to setup my PyTorch code to allow this to be represented by the model and outputted correctly. The probabilities are saved in a csv file. I have looked at the PyTorch docs and other resources which mention the cross entropy loss function but I am still unclear how to import the data successfully and make use of soft labels.
What you are trying to solve is a multi-label classification task, i.e. instances can be classified with more than one label at a time. You cannot use torch.CrossEntropyLoss since it only allows for single-label targets. So you have two options:
Either use a soft version of the nn.CrossEntropyLoss function, this can be done by implementing the loss by hand allowing for soft targets. You can find such implementation on Soft Cross Entropy in PyTorch.
Or consider the task a multiple "independent" binary classification tasks, in this case, you would use nn.BCEWithLogitsLoss (this layer contains a sigmoid function).
Pytorch CrossEntropyLoss Supports Soft Labels Natively Now
Thanks to the Pytorch team, I believe this problem has been solved with the current version of the torch CROSSENTROPYLOSS.
You can directly input probabilities for each class as target (see the doc).
Here is the forum discussion that pushed this enhancement.
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 have a machine learning model built that tries to predict weather data, and in this case I am doing a prediction on whether or not it will rain tomorrow (a binary prediction of Yes/No).
In the dataset there is about 50 input variables, and I have 65,000 entries in the dataset.
I am currently running a RNN with a single hidden layer, with 35 nodes in the hidden layer. I am using PyTorch's NLLLoss as my loss function, and Adaboost for the optimization function. I've tried many different learning rates, and 0.01 seems to be working fairly well.
After running for 150 epochs, I notice that I start to converge around .80 accuracy for my test data. However, I would wish for this to be even higher. However, it seems like the model is stuck oscillating around some sort of saddle or local minimum. (A graph of this is below)
What are the most effective ways to get out of this "valley" that the model seems to be stuck in?
Not sure why exactly you are using only one hidden layer and what is the shape of your history data but here are the things you can try:
Try more than one hidden layer
Experiment with LSTM and GRU layer and combination of these layers together with RNN.
Shape of your data i.e. the history you look at to predict the weather.
Make sure your features are scaled properly since you have about 50 input variables.
Your question is little ambiguous as you mentioned RNN with a single hidden layer. Also without knowing the entire neural network architecture, it is tough to say how can you bring in improvements. So, I would like to add a few points.
You mentioned that you are using "Adaboost" as the optimization function but PyTorch doesn't have any such optimizer. Did you try using SGD or Adam optimizers which are very useful?
Do you have any regularization term in the loss function? Are you familiar with dropout? Did you check the training performance? Does your model overfit?
Do you have a baseline model/algorithm so that you can compare whether 80% accuracy is good or not?
150 epochs just for a binary classification task looks too much. Why don't you start from an off-the-shelf classifier model? You can find several examples of regression, classification in this tutorial.
I have 3 predictive models and I am evaluating there performance with a confusion matrix.
I am getting the same results for the confusion matrix for each of the 3 models.
I expect that the different models would perform differently and produce different confusion matrices. I am new to predictive modelling, so I suspect I am making a "Rooky mistake" . The full script I am using is sitting in a Jupyter notebook on GiThub here
A screenshot of the code for the 3 models is below
Can some one point out what is going wrong?
Cheers
Mike
As mentioned: make predictions on the test data. But keep in mind that your targets are skewed! So use StratifiedKFolds or something like this.
Also I guess that your data is a bit corrupted. While all models show the same result it may be a big mistake underneath.
Few questions/advises:
1. Did you scale your data?
2. Did you use one-hot-encoding?
2. Use don't Decision Trees but Forests/XGBoost. Easy to overfit with DT.
3. Don't use >2 hidden layers in NN because it's easy to overfit too. Use 2 firstly. And your architecture (30, 30, 30) with 2 target classes seems weird.
4. And if you wish to use >2 hidden layers - go to Keras or TF. You'll find there many features that can help you to not overfit.
That is simply because you are using the same Training data to make predictions. Since your models are already trained on the same data that you are making the predictions on, they will return the same results (and ultimately the same confusion matrix). You need to split your dataset into training and test sets. Then train your classifier on training set and make predictions on test set.
You can use train_test_split in Sklearn to split your dataset into training or test set.
I'd like to create an audio classification system with Keras that simply determines whether a given sample contains human voice or not. Nothing else. This would be my first machine learning attempt.
This audio preprocessor exists. It claims not to be done, but it's been forked a few times:
https://github.com/drscotthawley/audio-classifier-keras-cnn
I don't understand how this one would work, but I'm ready to give it a try:
https://github.com/keunwoochoi/kapre
But let's say I got one of those to work, would the rest of the process be similar to image classification? Basically, I've never fully understood when to use Softmax and when to use ReLu. Would this be similar with sound as it would with images once I've got the data mapped as a tensor?
Sounds can be seen as a 1D image and be worked with with 1D convolutions.
Often, dilated convolutions may do a good work, see Wave Nets
Sounds can also be seen as sequences and be worked with RNN layers (but maybe they're too bulky in amount of data for that)
For your case, you need only one output with a 'sigmoid' activation at the end and a 'binary_crossentropy' loss.
Result = 0 -> no voice
Result = 1 -> there's voice
When to use 'softmax'?
The softmax function is good for multiclass problems (not your case) where you want only one class as a result. All the results of a softmax function will sum 1. It's intended to be like a probability of each class.
It's mainly used at the final layer, because you only get classes as the final result.
It's good for cases when only one class is correct. And in this case, it goes well with the loss categorical_crossentropy.
Relu and other activations in the middle of the model
These are not very ruled. There are lots of possibilities. I often see relu in image convolutional models.
Important things to know are they "ranges". What are the limits of their outputs?
Sigmoid: from 0 to 1 -- at the end of the model this will be the best option for your presence/abscence classification. Also good for models that want many possible classes together.
Tanh: from -1 to 1
Relu: from 0 to limitless (it simply cuts negative values)
Softmax: from 0 to 1, but making sure the sum of all values is 1. Good at the end of models that want only 1 class among many classes.
Oftentimes it is useful to preprocess the audio to a spectrogram:
Using this as input, you can use classical image classification approaches (like convolutional neural networks). In your case you could divide the input audio in frames of around 20ms-100ms (depending on the time resolution you need) and convert those frames to spectograms. Convolutional networks can also be combined with recurrent units to take a larger time context into account.
It is also possible to train neural networks on raw waveforms using 1D Convolutions. However research has shown that preprocessing approaches using a frequency transformation achieve better results in general.