Why is my Keras model only using a portion of my dataset during training?

Keras Model Only Using a Portion of the Dataset During Training

Problem Description

When training a neural network model using Keras, it is discovered that the model is only using a small subset of the provided dataset for training, instead of the entire dataset. Specifically, the model is using only 1875 entries for training while the full dataset consists of 60,000 entries.

Analysis of the Issue

The issue arises due to a misunderstanding of the output during model fitting. The number 1875 displayed during fitting is not the number of training samples; it represents the number of batches. By default, Keras uses a batch size of 32 for training. Hence, the total number of batches for the given dataset becomes:

60000 / 32 = 1875

As a result, instead of training on the entire dataset, the model is splitting the data into batches of size 32 and iterating through these batches during each epoch.

Resolving the Issue

To use the entire dataset during training, it is necessary to specify a batch size that accommodates all the training samples. This can be achieved by setting the batch_size argument in the model.fit function. For instance, to use the entire dataset, one can specify batch_size=60000:

<code class="python">model.fit(train_images, train_labels, epochs=10, batch_size=60000)</code>

By using this batch size, the model will process all the 60,000 training examples in one batch, effectively utilizing the entire dataset for training.

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