def testing_model(self):
accuracies = []
for i in range (len(self.combination)):
scale = self.combination[i][0][0]
print "scale", scale
size = processing_data_gvs.get_data(scale=scale)
print size
print self.combination[i][0]
self.cnn = convnet_chair.ConvolutionalNeuralNetwork()
self.cnn.get_data(data = self.combination[i][0], size = size)
accuracy = self.cnn.chair_example(verbose = True, save = False)
accuracies. append(accuracy)
index = np.argmax(accuracises)
np.save("paramater_combination.npy", combination)
np.save("accuracy.npy", accuracises)
def testing_model(self):
accuracies = []
# [scale, lr, drop_conv, drop_hidden] = [10, 0.001, 0.2, 0.5]
for data in self.combination:
if self.verbose:
print "data", data
scale = data[0]
if self.verbose:
print "scale", scale
size = processing_data_gvs.get_data(scale=scale)
if self.verbose:
print "size", size
self.cnn = ConvolutionalNeuralNetwork()
self.cnn.get_data(data = data, size = size)
# self.cnn.get_data(data = [scale, lr, drop_conv, drop_hidden], size = size)
accuracy = self.cnn.chair_example(verbose = True, save = False)
accuracies.append(accuracy)
index = np.argmax(accuracies)
np.save("paramater_combination.npy", combination)
np.save("accuracy.npy", accuracies)
