def evaluate(): seq = STAE.get_model(re=Config.RELOAD_MODEL) print("got model") dataProvider = DataProvider() images = dataProvider.get_testset(True) print("got data") x_axis_values = [] min_et = 1e9 max_et = 0 for i in range(0, 20): x = np.zeros((1, 256, 256, 10)) x[0] = images[i] output = seq.predict(x) for j in range(0, 10): et = np.sum( np.square(np.subtract(x[0, :, :, j], output[0, :, :, j]))) min_et = min(min_et, et) max_et = max(max_et, et) x_axis_values.append(et) x_axis_values = 1.0 - (x_axis_values - min_et) / max_et x_axis_values = Helpers.movingaverage(x_axis_values, 20) import matplotlib.pyplot as plt plt.plot(x_axis_values) plt.ylabel('regularity score') plt.show()
def setUp(self): self.mj = Helpers.mari_to_janna(test_space) self.ab = Helpers.ace_to_bark(test_space)
def setUp(self): self.data_normal = Helpers.load("ModelReactionTests/test_data_normal.pkl") self.data_no_id = Helpers.load("ModelReactionTests/test_data_no_id.pkl")
def setUp(self): self.data_normal = Helpers.load( 'ModelReactionTests/test_data_normal.pkl') self.data_no_id = Helpers.load( 'ModelReactionTests/test_data_no_id.pkl')