meanError = abs(nn.endError)
alpha = 0.1
while meanError > 0.05:
inputdata=dataset[np.random.randint(0,len(dataset)-1)]
# # Make network learn from input
nn.inputData(inputdata)
nn.computeOutput()
nn.learn()
iterNb = iterNb + 1
errorEvolution.append(abs(nn.endError))
meanError = (1 - alpha) * meanError + alpha * abs(nn.endError)
# # Generate answer (which is way more tricky)
plt.figure()
nn.displayNetwork()
plt.figure()
plt.plot(errorEvolution)
plt.show()
print "Network trained in ", iterNb, "iterations !"
raw_input('Press Enter to continue. ')
print "Testing phase"
testDatasetSize=3 * datasetSize
testDataset=[]
# generate dataset of size 500
for d in range(testDatasetSize):
pat = np.random.randint(0,len(patterns))
sample = [patterns[pat][elem]+0.25*np.random.rand() for elem in range(inputSize)]
