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.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')
