Why does my Keras model seem to only use part of my Fashion MNIST dataset during training, even though it\'s processing 1875 batches?

Keras Training Limitations: Resolving Partial Dataset Usage

When training a neural network model using Keras, it's crucial to ensure that the entire dataset is utilized during training. However, in some instances, users may encounter issues where only a fraction of the data is used. This article explores a specific case where a model trained on the Fashion MNIST dataset uses only a portion of the available data, providing a comprehensive explanation and solution.

The provided code snippet utilizes the model.fit() method with default parameters, which includes a batch size of 32. This means that during each iteration or epoch, the model processes 32 samples from the training dataset. In the case of the Fashion MNIST dataset, which consists of 60,000 samples, the model would need to iterate through the entire dataset multiple times to complete training. However, the output shown in the console indicates that the model is completing one epoch in 1875 iterations.

This discrepancy arises because the model.fit() method reports the number of batches processed during training, not the total number of samples. Therefore, in this case, the model is training on 1875 batches, each containing 32 samples, resulting in a total of 1875 * 32 = 60,000 samples. This means that the model is indeed utilizing the entire dataset for training, despite the misleading progress bar that displays "1875/1875" during each epoch.

To avoid confusion and accurately track the progress of the training process, it is recommended to calculate and display the number of samples processed per epoch. This can be achieved by modifying the code to print the progress as follows:

<code class="python">for epoch in range(10):
    print(f"Current Epoch: {epoch + 1}")
    for batch_idx in range(1875):
        model.train_step((train_images[batch_idx * 32 : (batch_idx + 1) * 32],
                          train_labels[batch_idx * 32 : (batch_idx + 1) * 32]))
        print(f"Batch {batch_idx + 1} processed.")</code>

Using this approach, the console will display the progress in terms of both batches and samples, providing a clear understanding of the training process and confirming that the model is utilizing the entire dataset as intended.

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