Introduction

Binary classification using a Convolutional Neural Network (CNN) in Keras is a powerful approach, harnessing the deep structure of CNNs to make binary decisions. However, a common issue many practitioners encounter is when a binarily configured Keras CNN consistently predicts one class, often the positive class represented numerically as 1 . Understanding why this happens is fundamental to diagnosing and correcting the behavior.

Common Causes

1. Imbalanced Dataset: One of the leading causes of a CNN predicting one class is an imbalanced dataset where one class outweighs the other significantly. In such cases, the network may learn to classify all inputs as the majority class to minimize the overall error.
2. Inadequate Model Capacity: The architecture might be too simplistic to capture the nuances needed to differentiate between classes. More complex models with additional layers or units may be required to improve performance.
3. Improper Loss Function: Using a loss function that does not handle class imbalance properly can lead to skewed results. For binary classification, binary_crossentropy is typically used, but with class imbalance, loss_weight or focal loss might be necessary.
4. Bias in Initialization or Training: If the initial weights are skewed or if there are errors during data preprocessing, the model might end up biased towards a particular class.
5. Early Stopping or Non-Convergence: If the model prematurely halts due to early stopping, or fails to converge during training, it might consistently predict one class.

Technical Explanation

Imbalanced Dataset

Consider a dataset with 90% positive samples (labeled 1 ) and 10% negative samples (labeled 0 ). During training, a naive objective would optimize accuracy. By predicting all examples as 1 , the accuracy of the model would be 90%. Although numerically satisfactory, this is not actually effective or desirable.

Solution: • Utilize techniques such as oversampling the minority class, undersampling the majority class, synthetic data generation using SMOTE, and appropriate training sample weighting to balance the dataset. • A confusion matrix can illustrate the imbalance's impact:

Inadequate Model Capacity

A shallow CNN architecture may not offer the necessary complexity to differentiate features across classes effectively.

Solution: • Experiment with deeper architectures, adding more convolutional and dense layers, or increasing the number of filters in the convolutional layers.

Improper Loss

Function

Binary classification typically uses binary_crossentropy , which might not suffice alone in imbalanced scenarios. Focal loss refocuses learning on the harder, less frequent examples.

Solution: • Implement focal loss to accommodate imbalanced classes. Focal loss is defined as:

$L_{FL}(p_t) = -\alpha_t (1 - p_t)^\gamma \log(p_t)$

where $p_t$ is the probability for the true class, $\alpha$ is a balancing factor, and $\gamma$ is a focusing parameter.

Bias in Initialization or Training

Datasets must be carefully preprocessed, and initial weights shouldn't introduce bias toward any class.

Solution: • Implement a strategy for weight initialization that maintains a balance and ensures that the mean activation is zero after activation functions like ReLU.

Additional Considerations

• Validation Data: Separate a validation set to monitor the model’s performance in a balanced manner. It does not directly influence weight updates but can guide adjustments.

• Overfitting and Regularization: Include regularization techniques such as dropout layers to prevent overfitting to the majority class.

• Hyperparameter Tuning: Experiment with batch sizes, learning rates, and optimizer settings to find an optimal training configuration.

Conclusion

A binary Keras CNN consistently predicting one class reflects potential issues with class balance, model configuration, or training methodology. By diagnosing and addressing these elements, we can train more robust models capable of balanced and accurate binary classification.

Summary of Key Points

Through careful assessment, adjustment, and experimentation, CNNs in Keras can be adjusted to provide balanced predictions, even in the face of challenges like class imbalance and inadequate model capacity.