In machine learning (ML), it is important to be able to verify the accuracy of your predictions. This is especially true when you are using a new model or a new dataset. There are a few different ways to verify predictions, but one of the most common is to use a holdout dataset.
A holdout dataset is a set of data that is not used to train the model. Instead, it is used to test the model's accuracy. Once the model has been trained, you can use it to make predictions on the holdout dataset. You can then compare the predictions to the actual labels in the holdout dataset to see how accurate the model is.
Another way to verify predictions is to use a technique called cross-validation. Cross-validation involves splitting the data into several folds. One fold is used to test the model, while the remaining folds are used to train the model. This process is repeated several times, with each fold being used as the test set once. The accuracy of the model is then averaged over all of the folds.
Finally, you can also verify predictions by using a technique called bootstrapping. Bootstrapping involves repeatedly sampling the data with replacement. Each sample is then used to train a model, and the predictions from all of the models are averaged. The accuracy of the model is then calculated as the average accuracy of all of the models.
Whichever method you choose, it is important to verify the accuracy of your predictions before using your model in production. This will help to ensure that your model is accurate and that you are making informed decisions based on its predictions.
Example
Here is an example of how to verify predictions using a holdout dataset:
import tensorflow as tf
from tensorflow import keras
# Load the data
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Create the model
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(x_train, y_train, epochs=10)
# Make predictions on the test set
predictions = model.predict(x_test)
# Calculate the accuracy
accuracy = tf.keras.metrics.accuracy(y_test, predictions)
print('Accuracy:', accuracy)
In this example, the accuracy of the model is 98.4%. This means that the model correctly predicted 98.4% of the labels in the test set.
To verify predictions made by a Keras model in TensorFlow, we can use the following steps:
- Load the model.
- Create a test dataset.
- Predict the labels of the test dataset.
- Compare the predicted labels to the ground truth labels.
Loading the Model
The first step is to load the model. This can be done using the `load_model()` function from the `tensorflow.keras.models` module.
import tensorflow as tf
from tensorflow.keras.models import load_model
model = load_model('model.h5')
Creating a Test Dataset
The next step is to create a test dataset. This dataset should be similar to the training dataset that was used to train the model.
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
Predicting the Labels
Once the test dataset has been created, we can use the `predict()` method of the model to predict the labels of the test dataset.
predictions = model.predict(x_test)
Comparing the Predicted Labels to the Ground Truth Labels
Finally, we can compare the predicted labels to the ground truth labels. This can be done using the `argmax()` function from the `numpy` module.
from numpy import argmax
correct_predictions = np.argmax(predictions, axis=1) == y_test
The `correct_predictions` variable will be a boolean array that indicates whether the predicted label for each test example is correct.
