# Randomly pick a word from dict : 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 !"
#print "dataset"
#print dataset
# brewer2mpl.get_map args: set name set type number of colors
#bmap = brewer2mpl.get_map('Paired', 'qualitative', datasetSize)
#colors = bmap.mpl_colors
#samp = 0
#for data in dataset:
# plt.subplot(5,2,samp)
# plt.plot(data, color=colors[samp])
# samp += 1
#nn.displayNetwork()
errorEvolution=[]
# First example :
inputdata=dataset[np.random.randint(0,len(dataset)-1)]
nn.inputData(inputdata)
nn.computeOutput()
nn.learn()
iterNb = 1
errorEvolution.append(abs(nn.endError))
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))
