示例#1
0
def unit_test2():    
    mlp = MLP([1,2,1]);
    mlp.layers[1].set_params([[-.3],[4]],[.15,-2])
    mlp.layers[2].set_params([[0,2]],[-1])
    
    

    num_labels=1
    data= [1.0, 1.0]

   
    predicted =  mlp.predict(data[:-num_labels])
    print 'predicted: ', predicted            
    mlp.output_error_calculation(data[-num_labels:])
    mlp.backpropagation()
    
    print 'errors:'
    for layer in mlp.layers:
        print layer.e
    
    mlp.gradient_descend(.5)
    print 'weights:'
    for layer in mlp.layers:
        print zip( layer.b, layer.weight)
示例#2
0
def unit_test():    
    mlp = MLP([2,2,2,2]);
    mlp.layers[1].set_params([[.2,-.1],[.3,-.3]],[.1,-.2])
    mlp.layers[2].set_params([[-.2,-.3],[-.1,.3]],[.1,.2])
    mlp.layers[3].set_params([[-.1,.3],[-.2,-.3]],[.2,.1])
    

    num_labels=2
    data= [.3, .7, .1, 1.0]

   
    predicted =  mlp.predict(data[:-num_labels])
    print 'predicted: ', predicted            
    mlp.output_error_calculation(data[-num_labels:])
    mlp.backpropagation()
    
    print 'errors:'
    for layer in mlp.layers:
        print layer.e
    
    mlp.gradient_descend(.1)
    print 'weights:'
    for layer in mlp.layers:
        print zip( layer.b, layer.weight)