def plot_net_performance(X_predict, Y_predict, X_predicted, predictionError):

    import matplotlib.pyplot as plt

    plt.subplot(2,2,1)
    plt.title('dbmixed')
    plt.imshow(X_predict, interpolation='nearest', aspect='auto')
    plt.colorbar()

    plt.subplot(2,2,2)
    plt.title('dbmusic')
    plt.imshow(Y_predict, interpolation='nearest', aspect='auto')
    plt.colorbar()

    plt.subplot(2,2,3)
    plt.title('prediction')
    plt.imshow(X_predicted, interpolation='nearest', aspect='auto')
    plt.colorbar()

    plt.subplot(2,2,4)
    plt.title('predictionerror')
    plt.imshow(predictionError, interpolation='nearest', aspect='auto')
    plt.colorbar()

    plt.show()

def plot_net_logerr(logerr_mean_whole, logerr_over_whole, logerr_under_whole, logerr_var_whole):
    import matplotlib
    import matplotlib.pyplot as plt
    import numpy as np

    labels = ['mean','over','under','var']
    logerr_mean = np.mean(logerr_mean_whole)
    logerr_over = np.mean(logerr_over_whole)
    logerr_under = np.abs(np.mean(logerr_under_whole))
    logerr_var = np.mean(logerr_var_whole)
    means = [logerr_mean, logerr_over, logerr_under, logerr_var]
    x = np.arange(len(labels))
    width = 0.35
    fig, ax = plt.subplots()
    rects1 = ax.bar(x - width/2, means, width, label='CNN_Noise')
    ax.set_ylabel('Mittelwerte')
    ax.set_title('Mittelwerte logerr-Auswertung')
    ax.set_xticks(x)
    ax.set_xticklabels(labels)
    ax.legend()

    fig.tight_layout()

    plt.show()