Keras MCQs and Answers With Explanation: Keras is a popular deep-learning framework that has gained widespread popularity in recent years due to its ease of use and versatility. It is widely used by data scientists and machine learning practitioners for a variety of tasks, including image recognition, natural language processing, and more. To help individuals test their knowledge and understanding of Keras, Keras MCQs have been developed.
Keras MCQs and Answers
These Keras Multiple Choice Questions and Answers provide a quick and easy way for individuals to assess their understanding of the Keras framework and its various components. In this article, we will present the top 30 Keras MCQs and answers with detailed explanations, to help individuals deepen their knowledge and prepare for any Keras quiz they may encounter.
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Keras Multiple Choice Questions and Answers
| Quiz Name | Keras |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 30 Keras MCQs | Practice Online Quiz
1. Which of the following is not a popular deep learning framework?
a) Keras
b) TensorFlow
c) PyTorch
d) Scikit-Learn
Ans: d
Explanation: Keras, TensorFlow, and PyTorch are all popular deep learning frameworks, whereas Scikit-Learn is a machine learning library.
2. Which of the following is true about Keras?
a) Keras is a standalone library
b) Keras is built on top of TensorFlow
c) Keras is built on top of PyTorch
d) Keras is built on top of Scikit-Learn
Ans: b
Explanation: Keras is a high-level neural networks API that is built on top of TensorFlow.
3. Which of the following is not a type of model that can be built using Keras?
a) Sequential model
b) Convolutional model
c) Recurrent model
d) Decision tree model
Ans: d
Explanation: Keras is primarily used for building deep learning models, such as sequential, convolutional, and recurrent models.
4. Which of the following is not a type of layer that can be used in a Keras model?
a) Dense layer
b) Convolutional layer
c) Recurrent layer
d) SVM layer
Ans: d
Explanation: Keras does not have an SVM layer. Instead, you can use a dense layer with a linear activation function to implement an SVM.
5. Which of the following is the correct syntax for importing Keras?
a) import keras
b) import tensorflow.keras
c) from keras import *
d) All of the above are correct
Ans: b
Explanation: While “import keras” is also a valid syntax, it is recommended to use “import tensorflow.keras” because Keras is now part of the TensorFlow library.
6. Which of the following is the correct syntax for creating a sequential model in Keras?
a) model = Sequential()
b) model = SequentialLayer()
c) model = SequentialModel()
d) model = SequentialNetwork()
Ans: a
Explanation: The correct syntax for creating a sequential model in Keras is “model = Sequential()”.
7. Which of the following is the correct syntax for adding a dense layer to a Keras model?
a) model.add(Dense(units=10, activation=’relu’))
b) model.add(DenseLayer(units=10, activation=’relu’))
c) model.add(DenseModel(units=10, activation=’relu’))
d) model.add(DenseNetwork(units=10, activation=’relu’))
Ans: a
Explanation: The correct syntax for adding a dense layer to a Keras model is “model.add(Dense(units=10, activation=’relu’))”.
8. Which of the following activation functions is not available in Keras?
a) Sigmoid
b) Tanh
c) ReLU
d) Linear
Ans: d
Explanation: Linear activation is not a separate activation function in Keras. Instead, it is the default activation function for the last layer of a regression model.
9. Which of the following loss functions is commonly used for binary classification problems in Keras?
a) BinaryCrossentropy
b) CategoricalCrossentropy
c) SparseCategoricalCrossentropy
d) MeanSquaredError
Ans: a
Explanation: The BinaryCrossentropy loss function is commonly used for binary classification problems in Keras.
10. Which of the following loss functions is commonly used for multi-class classification problems in Keras?
a) BinaryCross entropy
b) CategoricalCrossentropy
c) SparseCategoricalCrossentropy
d) MeanSquaredError
Ans: b
Explanation: The CategoricalCrossentropy loss function is commonly used for multi-class classification problems in Keras.
11. Which of the following optimizers is not available in Keras?
a) Adam
b) SGD
c) RProp
d) RandomOptimizer
Ans: d
Explanation: RandomOptimizer is not a built-in optimizer in Keras.
12. Which of the following is the correct syntax for compiling a Keras model?
a) model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])
b) compile.model(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])
c) model.compile(Adam, categorical_crossentropy, [‘accuracy’])
d) model.optimizer(‘adam’).loss(‘categorical_crossentropy’).metrics([‘accuracy’]).compile()
Ans: a
Explanation: The correct syntax for compiling a Keras model is “model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])”.
13. Which of the following is the correct syntax for training a Keras model?
a) model.train(X_train, y_train, epochs=10, batch_size=32)
b) model.fit(X_train, y_train, epochs=10, batch_size=32)
c) model.train_on_data(X_train, y_train, epochs=10, batch_size=32)
d) model.fit_data(X_train, y_train, epochs=10, batch_size=32)
Ans: b
Explanation: The correct syntax for training a Keras model is “model.fit(X_train, y_train, epochs=10, batch_size=32)”.
14. Which of the following is the correct syntax for evaluating a Keras model?
a) model.evaluate(X_test, y_test)
b) model.eval(X_test, y_test)
c) model.evaluate_on_data(X_test, y_test)
d) model.eval_data(X_test, y_test)
Ans: a
Explanation: The correct syntax for evaluating a Keras model is “model.evaluate(X_test, y_test)”.
15. Which of the following is the correct syntax for making predictions with a Keras model?
a) model.predict(X_new)
b) predict.model(X_new)
c) model.predict_on_data(X_new)
d) model.make_predictions(X_new)
Ans: a
Explanation: The correct syntax for making predictions with a Keras model is “model.predict(X_new)”.
16. Which of the following is not a method of the Keras Sequential class?
a) add()
b) compile()
c) fit()
d) predict()
e) backward()
Ans: e
Explanation: The Sequential class does not have a backward() method. Instead, it uses automatic differentiation to compute gradients during training.
17. Which of the following is a way to prevent overfitting in a Keras model?
a) Adding more layers
b) Increasing the learning rate
c) Decreasing the batch size
d) Adding dropout layers
Ans: d
Explanation: Dropout layers randomly drop out some of the neurons during training, which can help prevent overfitting.
18. Which of the following is a way to increase the capacity of a Keras model?
a) Removing layers
b) Decreasing the number of neurons in each layer
c) Increasing the learning rate
d) Increasing the number of neurons in each layer
Ans: d
Explanation: Increasing the number of neurons in each layer can increase the capacity of the model, allowing it to learn more complex patterns in the data.
19. Which of the following is a way to speed up training of a Keras model?
a) Adding more layers
b) Increasing the batch size
c) Decreasing the learning rate
d) Adding more training data
Ans: b
Explanation: Increasing the batch size can speed up training by allowing the model to process more examples at once, reducing the number of updates needed per epoch.
20. Which of the following Keras layers can be used for image classification tasks?
a) Conv2D
b) LSTM
c) Dense
d) Dropout
Ans: a
Explanation: The Conv2D layer is commonly used for image classification tasks in Keras.
21. Which of the following Keras layers can be used for sequence prediction tasks?
a) Conv2D
b) LSTM
c) Dense
d) Dropout
Ans: b
Explanation: The LSTM layer is commonly used for sequence prediction tasks in Keras.
22. Which of the following Keras layers can be used for text classification tasks?
a) Conv2D
b) LSTM
c) Dense
d) Embedding
Ans: d
Explanation: The Embedding layer is commonly used for text classification tasks in Keras.
23. Which of the following Keras layers can be used for regularization?
a) Conv2D
b) LSTM
c) Dropout
d) MaxPooling2D
Ans: c
Explanation: The Dropout layer is commonly used for regularization in Keras, by randomly dropping out some of the neurons during training.
24. Which of the following is not a pre-processing step for text data in Keras?
a) Tokenization
b) Vectorization
c) Normalization
d) Padding
e) Regularization
Ans: e
Explanation: Regularization is not a pre-processing step for text data in Keras.
25. Which of the following is a common activation function used in Keras?
a) Sigmoid
b) Tanh
c) ReLU
d) All of the above
Ans: d
Explanation: Sigmoid, Tanh, and ReLU are all common activation functions used in Keras.
26. Which of the following is a common type of layer used in Keras for text classification?
a) Conv2D
b) LSTM
c) Dense
d) Embedding
Ans: d
Explanation: The Embedding layer is commonly used for text classification tasks in Keras.
27. Which of the following is a common type of layer used in Keras for image classification?
a) Conv2D
b) LSTM
c) Dense
d) Embedding
Ans: a
Explanation: The Conv2D layer is commonly used for image classification tasks in Keras.
28. Which of the following is a common type of layer used in Keras for sequence prediction?
a) Conv2D
b) LSTM
c) Dense
d) Embedding
Ans: b
Explanation: The LSTM layer is commonly used for sequence prediction tasks in Keras.
29. Which of the following is not a type of Keras layer?
a) Input
b) Output
c) Hidden
d) Dropout
e) Activation
Ans: d
Explanation: Dropout is not a type of Keras layer, it is a regularization technique that can be applied to any layer.
30. Which of the following is not a common loss function used in Keras?
a) Mean Squared Error (MSE)
b) Binary Crossentropy
c) Categorical Crossentropy
d) Gradient Descent
Ans: d
Explanation: Gradient Descent is an optimization algorithm, not a loss function. Mean Squared Error, Binary Crossentropy, and Categorical Crossentropy are all common loss functions used in Keras.
The Keras MCQs and Answers With Explanations presented here will help individuals deepen their knowledge and prepare for any Keras quiz. You can acquire more knowledge by following us at freshersnow.com.