plot_figures.py 文件源码

def plot_pearson(name):
    """Plot the pearsin coeff of  the neurons for each layer"""
    data_array = utils.get_data(name)
    ws = data_array['weights']
    f = plt.figure(figsize=(12, 8))
    axes = f.add_subplot(111)
    #The number of neurons in each layer -
    #TODO need to change it to be auto
    sizes =[10,7, 5, 4,3,2 ]
    #The mean of pearson coeffs of all the layers
    pearson_mean =[]
    #Go over all the layers
    for layer in range(len(sizes)):
        inner_pearson_mean =[]
        #Go over all the weights in the layer
        for k in range(len(ws)):
            ws_current = np.squeeze(ws[k][0][0][-1])
            #Go over the neurons
            for neuron in range(len(ws_current[layer])):
                person_t = []
                #Go over the rest of the neurons
                for neuron_second in range(neuron+1, len(ws_current[layer])):
                    pearson_c, p_val =sis.pearsonr(ws_current[layer][neuron], ws_current[layer][neuron_second])
                    person_t.append(pearson_c)
            inner_pearson_mean.append(np.mean(person_t))
        pearson_mean.append(np.mean(inner_pearson_mean))
    #Plot the coeff
    axes.bar(np.arange(1,7), np.abs(np.array(pearson_mean))*np.sqrt(sizes), align='center')
    axes.set_xlabel('Layer')
    axes.set_ylabel('Abs(Pearson)*sqrt(N_i)')
    rects = axes.patches
    # Now make some labels
    labels = ["L%d (%d nuerons)" % (i, j) for i, j in zip(range(len(rects)), sizes)]
    plt.xticks(np.arange(1,7), labels)