Consider the following code, which evaluates our convnet model using the MNIST test data: [38]: loss, accuracy = cnn.evaluate(X_test, y_test)
10000/10000 [==============================] - 4s 366us/step
[39]: loss
[39]: 0.026809450998473768
[40]: accuracy
[40]: 0.9917
Which of the following statements a), b) or c) is false?
A) You can check the accuracy of a model on data the model has not yet seen. To do so, call the model's evaluate method, which displays as its output how long it took to process the test samples.
B) According to the output of the preceding snippet, our xe "convnet (convolutional neural network)"convnet model is 99.17% accurate when predicting the labels for unseen data.
C) With a little online research, you can find models that can predict MNIST with nearly 100% accuracy.
D) Each of the above statements is true.

Question

Quizplus · Accepted Answer

Each statement provided is true. A) describes the process of evaluating a model on unseen data, which is a common practice in machine learning to test a model's generalization capability. B) accurately interprets the output of the code snippet, indicating the model's high accuracy on the test data. C) is also true, as advancements in deep learning have led to models that achieve near-perfect accuracy on the MNIST dataset.

Consider the Following Code, Which Evaluates Our Convnet Model Using