In machine learning (ML), accuracy is a measure of how well a model predicts the correct output for a given input. It is calculated by dividing the number of correct predictions by the total number of predictions.
In Keras, a popular ML library for Tensorflow, accuracy can be evaluated using the `evaluate()` method. This method takes two arguments: the test data and the labels for the test data. The `evaluate()` method returns a dictionary containing the model's loss and accuracy on the test data.
The following code shows how to evaluate the accuracy of a Keras model:
import keras
# Load the model
model = keras.models.load_model('model.h5')
# Load the test data
test_data = keras.datasets.mnist.load_data()[1]
# Get the labels for the test data
test_labels = keras.utils.to_categorical(test_data[1])
# Evaluate the model
loss, accuracy = model.evaluate(test_data[0], test_labels)
print('Loss:', loss)
print('Accuracy:', accuracy)
The output of the code will be something like this:
Loss: 0.006002531011447372
Accuracy: 99.92%
In this example, the model has an accuracy of 99.92% on the test data. This means that the model correctly predicted the output for 99.92% of the test data.
It is important to note that accuracy is not always the best measure of a model's performance. For example, if a model is only trained on a very small dataset, it may have a high accuracy on that dataset, but it may not generalize well to new data. In these cases, it is important to use other measures of performance, such as the F1 score or the ROC AUC.
However, accuracy is a simple and easy-to-understand metric that can be a good starting point for evaluating the performance of a Keras model.
