def draw(self,inputNames = None, outputNames = None):
     ml.draw(self.Vs, self.W, inputNames, outputNames)
 def draw(self,inputNames = None, outputNames = None):
     ml.draw(self.Vs+[self.W], inputNames, outputNames)
Exemple #3
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 def draw(self,inputNames = None, outputNames = None):
     ml.draw(self.Vs+[self.W], inputNames, outputNames)
Exemple #4
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 def draw(self,inputNames = None, outputNames = None):
     weightMatrices = [layer.W for layer in self.layers]
     ml.draw(weightMatrices, inputNames, outputNames)
Exemple #5
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 def draw(self, inputNames = None, outputNames = None):
     ml.draw(self.Vs, self.W, inputNames, outputNames)
Exemple #6
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    plt.legend(('Train Target','Train NN Output'),loc='lower right',
               prop={'size':9})
    plt.subplot(nPlotRows,1,3)
    plt.plot(Xtest,Ttest)
    plt.plot(Xtest,Ytest)
    plt.legend(('Test Target','Test NN Output'),loc='lower right',
               prop={'size':9})
    colors = ('blue','green','red','black','cyan','orange')
    for i in range(len(nnet.nhs)):
        plt.subplot(nPlotRows,1,i+4)
        plt.plot(X,Z[i]) #,color=colors[i])
        plt.ylabel('Hidden Unit Outputs')
        plt.text(8,0, 'Layer {}'.format(i+1))

    plt.subplot(nPlotRows,1,nPlotRows)
    ml.draw(nnet.Vs,nnet.W,['x'],['sine'])
    plt.draw()
    
    # Now train multiple nets to compare error for different numbers of hidden layers

    def experiment(hs,nReps,nIter,X,T,Xtest,Ytest):
        results = []
        for i in range(nReps):
            nnet = NeuralNetwork(1,hs,1)
            nnet.train(X,T,weightPrecision=0,errorPrecision=0,nIterations=nIter)
            # print "SCG stopped after",nnet.getNumberOfIterations(),"iterations:",nnet.reason
            (Y,Z) = nnet.use(X, allOutputs=True)
            Ytest = nnet.use(Xtest)
            rmsetrain = np.sqrt(np.mean((Y-T)**2))
            rmsetest = np.sqrt(np.mean((Ytest-Ttest)**2))
            results.append([rmsetrain,rmsetest])