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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()