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Accuracy pendant training

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This commit is contained in:
eynard
2022-01-06 12:13:22 +01:00
parent 151343b7bd
commit 58d2d70f2b
4 changed files with 34 additions and 10 deletions
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12
sobek/network.py
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@@ -60,14 +60,14 @@ class network:
def train(self, inputs, desiredOutputs, learningRate, batchSize, epochs=1, visualize=False):
def train(self, inputs, desiredOutputs, learningRate, batchSize, epochs=1, accuracyInputs=False, accuracyDesiredOutputs=False, visualize=False):
if (type(inputs) != list or type(desiredOutputs) != list):
raise TypeError("The inputs and desired outputs must be lists of numpy arrays !")
if (len(inputs) != len(desiredOutputs)):
raise ValueError("The inputs and desired outputs lists must have the same amount of data ! " + str(len(inputs)) + " != " + str(len(desiredOutputs)))
if (len(inputs) == 0):
raise ValueError("The list is empty !")
if (visualize == False):
if (visualize != False):
if (self.__inputLayerSize != 2):
raise ValueError("Visualization is only possible for 2 inputs networks")
if (len(self.weights[-1]) != 1):
@@ -126,6 +126,14 @@ class network:
errorSumsBiases[layerNumber] = np.multiply(errorSumsBiases[layerNumber], -(learningRate/len(inputBatch)))
self.biases[layerNumber] = np.add(self.biases[layerNumber], errorSumsBiases[layerNumber])
'''errorSum = 0
for layer in errorSumsBiases:
errorSum += np.sum(np.abs(layer))
print(errorSum)'''
if (accuracyInputs and accuracyDesiredOutputs):
print(self.accuracy(accuracyInputs, accuracyDesiredOutputs))
if (visualize):
ani = animation.ArtistAnimation(fig, vizualisationData, interval=100)
plt.show()