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This section includes 10 Mcqs, each offering curated multiple-choice questions to sharpen your Technical MCQs knowledge and support exam preparation. Choose a topic below to get started.
1. |
Which of the following returns all the layers of the model as list? |
A. | model.inputs |
B. | model.layers |
C. | model.outputs |
D. | model.get_weights |
E. | |
Answer» C. model.outputs | |
2. |
A ____________ requires shape of the input (input_shape) to understand the structure of the input data. |
A. | Keras layer |
B. | Keras Module |
C. | Keras Model |
D. | Keras Time |
Answer» B. Keras Module | |
3. |
Which of the following are correct initializers in keras? |
A. | keras.initializers.Initializer() |
B. | keras.initializers.Zeros() |
C. | keras.initializers.Ones() |
D. | All of the above |
Answer» E. | |
4. |
What are advanced activation functions in keras ? |
A. | LeakyReLU |
B. | PReLU |
C. | Both A and B |
D. | None of the above |
Answer» D. None of the above | |
5. |
A flatten operation on a tensor reshapes the tensor to have a shape that is equal to the number of elements contained in the tensor. |
A. | TRUE |
B. | FALSE |
C. | Can be true or false |
D. | Can not say |
Answer» B. FALSE | |
6. |
What is true about Keras? |
A. | Keras is an API designed for human beings, not machines. |
B. | Keras follows best practices for reducing cognitive load |
C. | it provides clear and actionable feedback upon user error |
D. | All of the above |
Answer» E. | |
7. |
__________ is a regularization technique for neural network models proposed by Srivastava, it is a technique where randomly selected neurons are ignored during training. |
A. | Callout |
B. | Digout |
C. | Dropout |
D. | Knimeout |
Answer» D. Knimeout | |
8. |
Who invented keras? |
A. | Michael Berthold |
B. | Adam Paszke |
C. | Sam Gross |
D. | François Chollet |
Answer» E. | |
9. |
Is keras a library? |
A. | Yes |
B. | No |
C. | Can be yes or no |
D. | Can not say |
Answer» B. No | |
10. |
_________ is a high level API built on TensorFlow. |
A. | PyBrain |
B. | Keras |
C. | PyTorch |
D. | Theano |
Answer» C. PyTorch | |