Keras is a widely-used deep learning library, praised for its ease of use and flexibility in constructing and training neural networks. Among its numerous functionalities are fit_generator and evaluate_generator , which allow users to train and evaluate models on data that is provided by a Python generator. However, users sometimes observe discrepancies in the accuracy results obtained from these two functions. This can be puzzling, especially when expecting a model's performance to be consistent across training and evaluation phases. This article delves into the reasons behind this difference and provides insights into how both functions work under the hood.

Understanding fit_generator

and evaluate_generator

fit_generator

The fit_generator method is employed to train a model using Python generators. It is especially useful when dealing with large datasets that cannot entirely fit into memory. Here is a simple example:

• Steps Per Epoch: The total number of batches of samples or iterations to be drawn from the generator before declaring one epoch complete.
• Epochs: The number of complete passes through the training dataset.
• Steps: Indicates the number of steps (batches of samples) to yield from the generator before stopping the evaluation.
• Training-time Augmentation: When using fit_generator , data augmentation can be applied on-the-fly. Common transformations include random rotations, shifts, flips, etc., which increase the robustness of the model during training. However, evaluate_generator often does not apply the same transformations, primarily because you typically want a straightforward evaluation on the unaltered validation set.
• Preprocessing Variations: Ensure that any preprocessing steps (like scaling, normalization) are consistent between training and evaluation. Divergence here can lead to accuracy variations.
• Observations During Evaluation: When switching to evaluate_generator , which usually uses an untouched validation set, the generalization capability can significantly differ, highlighting overfitting issues.
• Batch Sizes: The training batches and evaluation batches may differ in size, impacting metrics like accuracy especially if dataset sizes are not perfectly divisible by the batch size.
• Evaluation Steps: When using evaluate_generator , if the number of steps is not appropriately set to cover the entire validation dataset, it might lead to an incorrect computation of metrics.
• Averaging Methods: The way accuracy and other metrics are computed and averaged over the batches during training and evaluation can subtly differ due to rounding errors, especially if the batch sizes result in an uneven number of samples.
• Batch-Level Metrics: fit_generator often records metrics after each batch which are then averaged per epoch. However, evaluate_generator calculates metrics over the entire looped dataset in one go.