Using sample_weight in Keras for sequence labellin

I am working on a sequential labeling problem with unbalanced classes and I would like to use sample_weight to resolve the unbalance issue. Basically if I train the model for about 10 epochs, I get great results. If I train for more epochs, val_loss keeps dropping, but I get worse results. I'm guessing the model just detects more of the dominant class to the detriment of the smaller classes.

The model has two inputs, for word embeddings and character embeddings, and the input is one of 7 possible classes from 0 to 6.

With the padding, the shape of my input layer for word embeddings is (3000, 150) and the input layer for word embeddings is (3000, 150, 15). I use a 0.3 split for testing and training data, which means X_train for word embeddings is (2000, 150) and (2000, 150, 15) for char embeddings. y contains the correct class for each word, encoded in a one-hot vector of dimension 7, so its shape is (3000, 150, 7). y is likewise split into a training and testing set. Each input is then fed into a Bidirectional LSTM.

The output is a matrix with one of the 7 categories assigned for each word of the 2000 training samples, so the size is (2000, 150, 7).

At first, I simply tried to define sample_weight as an np.array of length 7 containing the weights for each class:

count = [list(array).index(1) for arrays in y for array in arrays]
count = dict(Counter(count))
count[0] = 0
total = sum([count[key] for key in count])
count = {k: count[key] / total for key in count}
category_weights = np.zeros(7)
for f in count:
    category_weights[f] = count[f]

But I get the following error ValueError: Found a sample_weight array with shape (7,) for an input with shape (2000, 150, 7). sample_weight cannot be broadcast.

Looking at the docs, it looks like I should instead be passing a 2D array with shape (samples, sequence_length). So I create a (3000, 150) array with a concatenation of the weights of every word of each sequence:

weights = []

for sample in y:
    current_weight = []
    for line in sample:
        current_weight.append(frequency[list(line).index(1)])
    weights.append(current_weight)

weights = np.array(weights)

and pass that to the fit function through the sample_weight parameter after having added the sample_weight_mode="temporal" option in compile().

I first got an error telling me the dimension was wrong, however after generating the weights for only the training sample, I end up with a (2000, 150) array that I can use to fit my model.

Is this a proper way to define sample_weights or am I doing it all wrong ? I can't say I've noticed any improvements from adding the weights, so I must have missed something.

I think you are confusing sample_weights and class_weights. Checking the docs a bit we can see the differences between them:

sample_weights is used to provide a weight for each training sample. That means that you should pass a 1D array with the same number of elements as your training samples (indicating the weight for each of those samples). In case you are using temporal data you may instead pass a 2D array, enabling you to give weight to each timestep of each sample.

class_weights is used to provide a weight or bias for each output class. This means you should pass a weight for each class that you are trying to classify. Furthermore, this parameter expects a dictionary to be passed to it (not an array, that is why you got that error). For example consider this situation:

class_weight = {0 : 1. , 1: 50.}

In this case (a binary classification problem) you are giving 50 times as much weight (or "relevance") to your samples of class 1 compared to class 0. This way you can compensate for imbalanced datasets. Here is another useful post explaining more about this and other options to consider when dealing with imbalanced datasets.

If I train for more epochs, val_loss keeps dropping, but I get worse results.

Probably you are over-fitting, and something that may be contributing to that is the imbalanced classes your dataset has, as you correctly suspected. Compensating the class weights should help mitigate this, however there may still be other factors that can cause over-fitting that escape the scope of this question/answer (so make sure to watch out for those after solving this question).

Judging by your post, seems to me that what you need is to use class_weight to balance your dataset for training, for which you will need to pass a dictionary indicating the weight ratios between your 7 classes. Consider using sample_weight only if you want to give each sample a custom weight for consideration.

If you want a more detailed comparison between those two consider checking this answer I posted on a related question. Spoiler: sample_weight overrides class_weight, so you have to use one or the other, but not both, so be careful with not mixing them.

Update: As of the moment of this edit (March 27, 2020), looking at the source code of training_utils.standardize_weights() we can see that it now supports both class_weights and sample_weights:

Everything gets normalized to a single sample-wise (or timestep-wise) weight array. If both sample_weights and class_weights are provided, the weights are multiplied together.