0
$\begingroup$

I want to build a model that classifies 473 classes -product categories-, but I'm facing a problem with loss not decreasing.


Data

I have almost 3,000 data points for each class -473 classes- (data size is almost 1.5 million) The data is a sequence of 5 words [iPhone, Pro, Max, 0, 0] and of course they're numbered [345, 344, 123, 0, 0]

Examples:

Input: [iPhone, Pro, Max, 0, 0]
Output: iPhone

Input: [Go, Pro, Camera, New, 0]  
Output: GoPro

Input: [LG, TV, 50, Inches, Used]
Output: LG_TV

Input: [Apple, Watch, 42, mm, 0]
Output: Apple_Watch

Loss

Epoch: 1, Loss: 5.607430, Val Loss: 5.538741
Epoch: 2, Loss: 5.493465, Val Loss: 5.516405 
Epoch: 3, Loss: 5.487641, Val Loss: 5.513667
Epoch: 4, Loss: 5.474956, Val Loss: 5.508683
Epoch: 5, Loss: 5.472722, Val Loss: 5.508304
Epoch: 6, Loss: 5.472691, Val Loss: 5.510557
Epoch: 7, Loss: 5.472782, Val Loss: 5.508627
Epoch: 8, Loss: 5.472320, Val Loss: 5.533378
Epoch: 9, Loss: 5.472340, Val Loss: 5.520573 

I've tried to train it for 50 epochs, but the loss still not decreasing.


Model

I'm using PyTorch

LSTMClassifier(
  (embedding): Embedding(15278, 200)
  (lstm): LSTM(200, 256, num_layers=2, batch_first=True, dropout=0.5)
  (dropout): Dropout(p=0.3, inplace=False)
  (dense): Linear(in_features=256, out_features=473, bias=True)
)

The loss function is: CrossEntropyLoss


Hyperparameters

Batch size: 512
Embedding Dim: 200
Vocabualry size: 15,278
LSTM Layers: 2
Hidden Dims: 256
Optimizer: Adam
Learning rate: 0.002

Can you please direct me to the problem? Is the model weak? Or is it the data having a problem?

$\endgroup$
  • $\begingroup$ Hi Khaled, can you explain how you are going to use this model and why you decided to model this classification task as a sequence of words? $\endgroup$ – Sammy Feb 26 at 11:19
  • $\begingroup$ @Sammy I'm going to predict the class label from the input: e.g. "iPhone Pro Max for Sale" prediction would be "iPhone". Because LSTMs can only learn from sequences, I constructed the input to be in a from of a sequence. $\endgroup$ – Khaled Feb 26 at 11:58
  • $\begingroup$ Can you provide some additional examples of what your inputs and corresponding labels look like? $\endgroup$ – Sammy Feb 26 at 12:44
  • 1
    $\begingroup$ I have updated my answer with more examples. $\endgroup$ – Khaled Feb 26 at 13:59
0
$\begingroup$

One way to approach this is to increase model capacity and see if/when your LSTM is able to learn the train data. In this first step it is ok to overfit (also see this recent question and answer for this approach) since you can add regularization/decrease model capacity later.

These are the parameters I would tweak:

  • find a good learning rate along a log-scale (try at least 0.1, 0.01 and 0.0001)
  • increase the number of LSTM layers, e.g. to 5
  • increase the hidden dimension, e.g. to 1024
  • slightly increase the embedding dimension, e.g. to 300

If training this model takes too long, tune the learning rate based on your current model and then make the other adjustments. However, I suggest to not make the other adjustments in smaller steps or one by one as that can take a lot of time.

| improve this answer | |
$\endgroup$
  • $\begingroup$ Thank you @Sammy for your suggestions. I'll try them. $\endgroup$ – Khaled Feb 26 at 16:26

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.