Keras + TensorFlow Model

Question

I'm currently creating a model and while creating it I came with some questions. Does training the same model with the same data multiple times leads to better precision of those objects, since your training it every time? And what could be the issue when sometimes the object gets 90% precision and when I re-run it gets lower precision or even not predicting the right object? Is it because of Tensorflow running on the GPU?

Answer 1

I will guess that you are doing image recognition and that you want to identify images (objects) using a neuronal network made with Keras. You should train it once, but during training you will do several epochs, meaning the algorithm adapts the weights in several rounds (epochs). For each round it goes over all training images. Once trained, you can use the model to identify images/objects.

You can evaluate the accuracy of your trained model over the same training set, but it is better to use a different set (see train_test_split from sklearn for instance).

Training is a stochastic process, meaning that every time you train your network it will be different in the end. Hence, you will get different accurcies. The stochasticity comes from different initial weights or from using stochastic gradient descent methods for instance.

The question does not appear to have anything to do with Keras or TensorFlow but basic understandting of how neuronal networks work. There is no connection to running Tensorflow on the GPU. You will also not get better precision by training with the same objects. If you train your model on a dataset for a very long time (many epochs), you might get into overfitting. Then, the accuracy of your model on this training dataset will be very high, but the model will have low accuracy on other datasets.

Answer 2

A common technique is split your date in train and validation datasets, then repeatedly train your model using EarlyStopping. This will train on the training dataset, then calculate the loss against the validation dataset, and then keep training until no further improvement is seen. You can set a patience parameter to wait for X epochs without an improvement to stop training (and optionally save the best model)

• https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/

Another trick is image augmentation with ImageDataGenerator which will generate synthetic data for you (rotations, shifts, mirror images, brightness adjusts, noise etc). This can effectively increase the amount of data you have to train with, thus reducing overfitting.

• https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/