How to test a trained model in tensorflow?

@elise_daugherty 

To test a trained model in TensorFlow, you can follow these steps:

1. Load your trained model using the tf.keras.models.load_model() function.

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model = tf.keras.models.load_model('path_to_your_trained_model.h5')

1. Prepare your test data by preprocessing it in the same way as the training data.
2. Use the evaluate() method of the model object to evaluate the model on the test data. This method returns the loss value and any metrics you defined when compiling the model.

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loss, accuracy = model.evaluate(test_data, test_labels)
print('Test Loss: {}, Test Accuracy: {}'.format(loss, accuracy))

1. Optionally, you can make predictions on new data using the predict() method of the model object.

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predictions = model.predict(new_data)

By following these steps, you can effectively test your trained model in TensorFlow and assess its performance on unseen data.