def test_train(self):
weightDict, biasDict = {}, {}
for i in range(208):
weightDict['weight' + str(i)] = i
if i < 21:
biasDict['bias' + str(i)] = i
network = MoodNeuralNetwork(weights=weightDict, biases=biasDict)
sample_data = np.array([[2,1,4,5,6,2,6,7,3,6,6]])
true = np.array([30])
prediction = network.feedforward(sample_data[0])
loss1 = network.loss(true, prediction)
network.train(sample_data, true)
prediction = network.feedforward(sample_data[0])
loss2 = network.loss(true, network.roundClass(prediction))
self.assertTrue(loss2 < loss1)
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
