Which layer type is responsible for introducing non-linearity in an RNN?Question 22Answera.Activation layerb.Input layerc.Hidden layerd.Output layer

Break Down the Problem

1. Identify the layers in a Recurrent Neural Network (RNN).
2. Determine the function of each layer type.
3. Find out which layer introduces non-linearity.

Relevant Concepts

• RNNs consist of various types of layers: input layer, hidden layer, output layer, and activation functions.
• Non-linearity is typically introduced by activation functions applied within hidden layers.

Analysis and Detail

1. Input Layer: This layer is responsible for receiving input data and passing it on to the next layer without any transformations.
2. Hidden Layer: This layer processes input data and captures the essential features. It commonly uses activation functions to introduce non-linear transformations.
3. Output Layer: This layer produces the final prediction and also relies on activation functions but primarily for converting the hidden layer’s output to a format suitable for making predictions.

Verify and Summarize

The activation functions used in the hidden layers of an RNN (such as tanh or ReLU) apply non-linear transformations to the data flowing through the network. Therefore, the hidden layer is primarily responsible for introducing non-linearity in RNNs.

Final Answer

The correct answer is c. Hidden layer.