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)