Which of the following is a way to prevent overfitting in a Keras model?

Keras - Quiz(MCQ)

Adding more layers

Adding dropout layers

Decreasing the batch size

Increasing the learning rate

Correct Answer : Adding dropout layers

Explanation : Dropout layers randomly drop out some of the neurons during training, which can help prevent overfitting.

Recently Updated in Keras Questions

Which of the following is a fully connected layer whose output is sent back to the input?

RNN layer

Pooling layer

SimpleRNN layer

Embedding layer

Correct Answer : SimpleRNN layer

Explanation : SimpleRNN layer is a fully connected layer whose output is sent back to the input.

When sequential.pop() method raises a TypeError?

When there is no layer within the model

When there is the single layer within the model

When there are more than 10 layers within the model

None of the above

Correct Answer : When there is the single layer within the model

Explanation : sequential.pop() method raises a TypeError When there is a single layer within the model.

What does sequential.pop() method do?

It helps in the removal of the last layer from the model.

It helps in the removal of any layer from the model.

It helps in the removal of the first layer from the model.

All of the above

Correct Answer : It helps in the removal of the last layer from the model.

Explanation : Sequential.pop() functions help in the removal of the last layer from the model.

New Technologies MCQ's

Machine Learning

Artificial Intelligence