Research Article
Road-Type Classification with Deep AutoEncoder
Algorithm 4
Road segment classification using DNN.
| Require: : size of input layer; : size of output layer | | Training set: | | Test set: | | Output: DNN structure: : Optimal weight, : number of layers | | List of labels such that is the class label of the Test set element : | (1) | Training phase: | (2) | Initialise weight parameters structure | (3) | Define number of hidden layers, , and corresponding sizes . | (4) | while optimal parameters are not obtained do | (5) | for each training sample do | (6) | Calculate neurons value using equations (1) and (2). | (7) | Obtain the predicted output using equation (3). | (8) | end for | (9) | Calculate loss using equation (4). | (10) | Obtain the updated weight parameters using equation (5). | (11) | end while | (12) | Store optimal weight parameters . | (13) | Classification phase: | (14) | for each road segment vector do | (15) | Predict class of | (16) | end for | (17) | Return |
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