How do you predict using a trained model?

How do you predict using a trained model?

Summary

  1. Load EMNIST digits from the Extra Keras Datasets module.
  2. Prepare the data.
  3. Define and train a Convolutional Neural Network for classification.
  4. Save the model.
  5. Load the model.
  6. Generate new predictions with the loaded model and validate that they are correct.

How does model predict work?

Python predict() function enables us to predict the labels of the data values on the basis of the trained model. Thus, the predict() function works on top of the trained model and makes use of the learned label to map and predict the labels for the data to be tested.

Is it possible to predict with a neural network?

In neural network programming, the training and validation sets should be representative of the actual data the model will be predicting on. It is possible to get a prediction from a neural network model before the network has been trained. In this video, we explain the concept of using an artificial neural network to predict on new data.

What happens when you pass data to a neural network?

Recall from our post on training, testing, and validation sets, that unlike the train and validation data that get passed to the model with their respective labels, when we pass our test data to the model, we do not pass the corresponding labels. So, the model is not aware of the labels for the test set at all.

What kind of neural network do we use?

We used a simple Artificial Neural Network (ANN, aka Multi-Layer Perceptron) as it is capable of capturing complex, non-linear relationships between diverse numerical data, and relatively fast to build and train, compared to more sophisticated architectures like Long Short-Term Memory networks (LSTMs).

Is it possible to train a neural network?

What they do do is to create a neural network with many, many, many nodes –with random weights– and then train the last layer using minimum squares (like a linear regression). They then either prune the neural network afterwards or they apply regularization in the last step (like lasso) to avoid overfitting.