Research Article
Cell Traffic Prediction Based on Convolutional Neural Network for Software-Defined Ultra-Dense Visible Light Communication Networks
Algorithm 1
2D convolutional neural network model training process.
| Input: training set historical data: | Output: trained 2D convolutional neural network model | (1) | //Construct training examples | (2) | D ← φ | (3) | While all available time interval T (1 T N) | (4) | = | (5) | Put the training instance into D | (6) | // is the actual value at time t | (7) | end | (8) | //Training model | (9) | Initialization of all trainable parameters θ | (10) | Repeat | (11) | Randomly select a batch of instances from D | (12) | Use and Adam optimization to find the best θ (the loss value defined in Section 4.2.4)) | (13) | until meet the stop condition (early stopping or completed the training batch) |
|