inputtext = dictOfWords[np.random.randint(0,len(dictOfWords)-1)] print inputtext inputvec = [0.0 for i in range(len(nn.inputLayer))] counter = 0 for letter in inputtext: inputvec[counter*layerLength + proc.char2val(letter)] = 1.0 counter += 1 # print len(inputvec), len(nn.inputLayer) # Make network learn from input # #for char in inputtext: # # nn.inputData(proc.char2vec(char)) # # nn.computeOutput() # # nn.learn() # # answer += proc.vec2char(nn.outputLayer_f.tolist()) nn.inputData(inputvec) nn.computeOutput() #nn.learn() #answer += proc.vec2char(nn.outputLayer_f.tolist()) # Generate answer (which is way more tricky) iterNb = iterNb + 1 errorEvolution.append(abs(nn.endError)) #print answer #nn.displayNetwork() #plt.show() plt.figure() plt.plot(errorEvolution) plt.show() print "Network trained in ", iterNb, "iterations !"
