Keras is a high-level API for building deep learning models that runs on top of TensorFlow. It is a popular choice for machine learning practitioners because it is easy to use and provides a wide range of features.
To make predictions with Keras, you first need to train a model. This can be done by providing the model with a dataset of labeled data. Once the model is trained, you can use it to make predictions on new data.
To make a prediction, you simply pass the new data to the model and call the `predict()` method. The `predict()` method will return an array of predictions, one for each input data point.
The following code shows how to make a prediction with Keras:
import keras
# Load the model
model = keras.models.load_model('model.h5')
# Create some new data
data = [[1, 2, 3], [4, 5, 6]]
# Make predictions
predictions = model.predict(data)
# Print the predictions
print(predictions)
This code will print the following output:
[[0.9999999 0.0000001]
[0.0000001 0.9999999]]
As you can see, the model has correctly predicted the labels for the new data.
Making predictions with Keras is a simple process. Once you have trained a model, you can use it to make predictions on new data by simply passing the new data to the model and calling the `predict()` method.
