Which of the following statements is false?
A) The following snippet adds an LSTM layer to an RNN named rnn:
Rnn)add(LSTM(units=128, dropout=0.2, recurrent_dropout=0.2))
B) The units argument in Part (a)'s snippet specifies the number of neurons in the layer. The more neurons the more the network can remember. As a guideline, you can start with a value between the length of the sequences you're processing and the number of classes you're trying to predict.
C) The dropout argument specifies the percentage of neurons to randomly disable when processing the layer's input and output. Like the pooling layers in our convnet, dropout is a proven technique that reduces underfitting.
D) The recurrent_dropout argument specifies the percentage of neurons to randomly disable when the layer's output is fed back into the layer again to allow the network to learn from what it has seen previously.

Question

Quizplus · Accepted Answer

The statement in C is false because it inaccurately describes the function of dropout. Dropout does not specifically relate to pooling layers in convolutional networks (convnets), nor is its primary function to reduce underfitting. Instead, dropout is a regularization technique used to prevent overfitting in neural networks by randomly disabling a fraction of the neurons during training, which helps the model generalize better to unseen data.

Which of the Following Statements Is False