Keras is a high-level API for building deep learning models in TensorFlow. It provides a simple and intuitive way to define models, and it also includes a number of pre-trained models that can be used for a variety of tasks.
Once you have trained a model, you can use it to make predictions on new data. To do this, you first need to load the model into memory. You can do this using the `load_model()` function. Once the model is loaded, you can use it to make predictions by calling the `predict()` method.
The `predict()` method takes a batch of data as input and returns a prediction for each data point. The predictions are returned as a NumPy array.
Here is an example of how to use a trained model to make predictions:
import tensorflow as tf
from tensorflow import keras
# Load the model
model = keras.models.load_model('model.h5')
# Make predictions
predictions = model.predict(data)
The `data` variable in the example code is a NumPy array that contains the data that you want to make predictions for. The `predictions` variable will contain the predictions for each data point in the `data` array.
Once you have made the predictions, you can use them to do whatever you need to do. For example, you could use the predictions to classify data, to make recommendations, or to control a machine.
