# why the accuracy result and the loss result of an ANN model is inconsistent?

I trained a model based on an ANN and the accuracy is 94.65% almost every time while the loss result is 12.06%. Now my question is shouldn't the loss of the model be (100-94 = 6%) or near it? Why it is giving a result of 12% when the accuracy is 94%?

• ANN model specification:

1. Trained and tested data= 96,465 (training data = 80%, testing data = 20%)
2. 1 Input layer= 5 nodes, 2 Hidden layers= 24 nodes each, 1 Output layer= 5 nodes
3. Activation function: a. Rectified linear (ReLu) function in hidden layers b. Softmax function in output layer
5. Loss function: Sparse categorical crossentropy
6. Batch size: 100
7. Epochs: 30

• Accuracy: it is a number of correctly classified samples out of all data samples and it is measured in percent.
• Loss: is a distance measure between the predicted output and ground truth (simply a measure between two probability distributions). To calculate this distance We use cross-entropy loss in multi-classification problem.It is not measured in percent.