How many dense layers are added to the model in the given code snippet, and what is the purpose of each layer?
In the given code snippet, there are three dense layers added to the model. Each layer serves a specific purpose in enhancing the performance and predictive capabilities of the cryptocurrency-predicting RNN model.
The first dense layer is added after the recurrent layer in order to introduce non-linearity and capture complex patterns in the data. This layer helps in transforming the output of the recurrent layer into a more meaningful representation for further processing. By applying a set of weights and biases, the dense layer performs a linear transformation of the input data and applies an activation function to introduce non-linearity. This allows the model to learn more intricate relationships between the input features and the target variable. The number of neurons in this dense layer determines the dimensionality of the output space.
The second dense layer in the code snippet is added to further refine the learned representations from the previous layer. It helps in extracting higher-level features and patterns by applying another linear transformation and activation function. This additional layer of non-linearity enables the model to capture more abstract and complex dependencies in the cryptocurrency data. The number of neurons in this layer can be adjusted based on the complexity of the problem and the amount of available training data.
The third and final dense layer is added as the output layer of the model. This layer is responsible for producing the final predictions for the cryptocurrency values. The number of neurons in this layer corresponds to the number of output classes or the dimensionality of the target variable. In this case, since the goal is to predict cryptocurrency values, the output layer would typically have a single neuron. The activation function used in the output layer depends on the nature of the problem. For regression tasks, a linear activation function can be used, while for classification tasks, a suitable activation function such as sigmoid or softmax is employed.
By adding these dense layers, the model becomes capable of learning complex representations and making predictions based on the learned features. The non-linear transformations introduced by the dense layers allow the model to capture intricate patterns and relationships in the cryptocurrency data, leading to improved predictive performance.
To summarize, the given code snippet includes three dense layers in the cryptocurrency-predicting RNN model. The first dense layer captures non-linear relationships, the second layer extracts higher-level features, and the third layer serves as the output layer for making predictions. Each layer plays a important role in enhancing the model's predictive capabilities.
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