def test_setWeights(self): weightDict = {} for i in range(208): weightDict['weight' + str(i)] = np.random.normal() model = MoodNeuralNetwork() self.assertNotEqual(weightDict, model.getWeights()) self.assertTrue(model.setWeights(weightDict)) self.assertEqual(weightDict, model.getWeights())
def test_getWeights(self): weightDict, biasDict = {}, {} for i in range(208): weightDict['weight' + str(i)] = np.random.normal() if i < 21: biasDict['weight' + str(i)] = np.random.normal() model = MoodNeuralNetwork(weights=weightDict, biases=biasDict) self.assertEqual(weightDict, model.getWeights()) self.assertTrue(model.getWeights())
def setWeightsWeights(self, weights_list=False): if weights_list: if len(weights_list) != 208: return False self.weights_int_list = ",".join(str(x) for x in weights_list) else: model = MoodNeuralNetwork() weightDict = model.getWeights() weights = [] for i in range(len(weightDict)): weights.append(weightDict["weight" + str(i)]) self.setWeightsWeights(weights) return True
def retrain(self): weightDict, biasDict = self.getWeightBiasDictionaries() model = MoodNeuralNetwork(weights=weightDict, biases=biasDict) input_data, mood_data = self.transformUserData(7) model.train(input_data, mood_data) weightDict = model.getWeights() weights = [] for i in range(len(weightDict)): weights.append(weightDict["weight" + str(i)]) self.setWeightsWeights(weights) biasDict = model.getBiases() biases = [] for i in range(len(biasDict)): biases.append(biasDict["bias" + str(i)]) self.setWeightsBias(biases) return True