WHAT IS STEPS AND EPOCH?

Steps refer to the number of batches processed by the model during training. A batch is a subset of the training data used to update the modelâ€™s weights. The number of steps defines how many times the model goes through the training data.

On the other hand, an epoch is defined as one complete pass through the entire training dataset. In other words, one epoch means the model has seen the entire training data once. During an epoch, the model goes through multiple batches of the training data, updates its weights, and learns from the data.

DIFFERENCE BETWEEN STEPS AND EPOCH

The main difference between steps and epochs is that epochs refer to the number of times the model sees the entire training dataset, while steps refer to the number of batches processed during training.

For example, suppose you have a training dataset of 10,000 images, and you set the batch size to 100. In that case, each epoch will consist of 100 batches, with each batch containing 100 images. Therefore, to complete one epoch, the model will process 100 batches, each with 100 images, resulting in 10,000 images seen by the model.

Suppose you set the number of steps to 1000 and the batch size to 10. In that case, the model will process 10 images in each batch, resulting in 10,000 images seen by the model after 1000 steps.

RELATIONSHIP BETWEEN STEPS AND EPOCHS

The relationship between steps and epochs in TensorFlow depends on how you define your training process. You can define either the number of steps or the number of epochs for your modelâ€™s training. However, it is essential to understand how the two parameters affect your modelâ€™s performance.

If you define the number of epochs for your modelâ€™s training, TensorFlow will automatically calculate the number of steps required to complete the training. For example, if you set the number of epochs to 10 and the batch size to 100, the model will process 100 batches in each epoch, resulting in 1000 steps for the entire training process.

On the other hand, you can define the number of steps for your modelâ€™s training, and TensorFlow will automatically calculate the number of epochs required to complete the training. For example, if you set the number of steps to 1000 and the batch size to 100, the model will process 100 batches in each step, resulting in 10 epochs for the entire training process.

In general, increasing the number of steps or epochs can lead to better model performance, but it can also increase the training time. It is essential to find the right balance between the two parameters to achieve optimal performance while keeping the training time reasonable.

Steps and epochs are crucial parameters for training deep learning models. Steps refer to the number of batches processed by the model during training, while epochs refer to one complete pass through the entire training dataset. The relationship between the two parameters depends on how you define your training process.

Regards

DP