There could be several reasons why a neural network predicts only one class for a multiclass problem, even though it has many classes in its training data set. Here are a few possible explanations:

Imbalanced Training Data: It is possible that the neural network has been trained on an imbalanced dataset, where some classes have many more examples than others. In this case, the network may have learned to always predict the most common class in the dataset, rather than learning to distinguish between the different classes.

Poor Network Architecture: Another possible reason for poor performance of a neural network could be the architecture of the network itself. The network may not be deep enough, or may not have enough hidden units or neurons, to learn the complex relationships between the input features and the multiple output classes.

Inappropriate Loss Function: A neural network's loss function is used to measure the difference between the predicted output and the actual output. If the loss function is not appropriate for the problem at hand, the neural network may struggle to learn the correct output classes.

Incorrect Hyperparameter Tuning: A neural network's performance can be greatly affected by the values of its hyperparameters, such as the learning rate or regularization parameter. If these hyperparameters are not properly tuned, the network may not learn to distinguish between the different output classes.

Insufficient Training Time: Neural networks can take a long time to train, especially for complex problems or large datasets. If the network is not trained for long enough, it may not have had sufficient time to learn the relationships between the input features and the output classes.

In order to address these issues, it is important to carefully review the data, network architecture, loss function, hyperparameters, and training time to identify potential sources of error. Adjustments can be made to these aspects of the network to improve its performance on the multiclass problem. Additionally, further exploration of the data and its characteristics may help identify any underlying patterns that could be used to improve the network's predictions.
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