def test_step(self, data): # Unpack the data syclop_data, HR_data = data teacher_features = self.intermediate_laytrain_dataset, test_dataset = create_cifar_dataset(images, labels,res = 8, sample = sample, return_datasets=True, mixed_state = False, add_seed = 0, trajectory_list = trajectory_index, )er_model(HR_data, training=False)[:, : , :, self.feature]
'wb') as file_pi: pickle.dump(validation_data, file_pi) train_data = np.array(train_data) print('loaded feature data from teacher') #%% ############################## load syclop data ################################# print('loading Syclop Data') sample = 10 traject_data_path = path + 'traject_data/' train_dataset, test_dataset = create_cifar_dataset( images, labels, res=8, sample=sample, return_datasets=True, mixed_state=False, add_seed=0, ) train_dataset_x, train_dataset_y = split_dataset_xy(train_dataset, sample=sample) test_dataset_x, test_dataset_y = split_dataset_xy(test_dataset, sample=sample) print('saving trajectory data') #traject_data_path = '/home/labs/ahissarlab/orra/imagewalker/teacher_student/traject_data/' traject_data_path = path + 'traject_data/' with open(traject_data_path + 'traject_{}_train'.format(run_index), 'wb') as file_pi: pickle.dump((train_dataset_x, train_dataset_y), file_pi) with open(traject_data_path + 'traject_{}_test'.format(run_index), 'wb') as file_pi:
print('loaded feature data from teacher') #%% feature_test_data = train_data[45000:] feature_train_data = train_data[:45000][:, :, :] #%% ############################## load syclop data ################################# print('loading Syclop Data') train_dataset, test_dataset = create_cifar_dataset( images, labels, res=res, sample=sample, return_datasets=True, mixed_state=False, add_seed=0, trajectory_list=trajectory_index) train_dataset_x, train_dataset_y = split_dataset_xy(train_dataset, sample=sample) test_dataset_x, test_dataset_y = split_dataset_xy(test_dataset, sample=sample) #%% ##################### Define Student ######################################### epochs = 50 verbose = 2 evaluate_prediction_size = 150 prediction_data_path = path + 'predictions/' shape = feature_test_data.shape
metrics=["sparse_categorical_accuracy"], ) return model rnn_net = convgru_cnn(n_timesteps=sample, cell_size=hidden_size, input_size=res, concat=concat) #%% train_dataset, test_dataset = create_cifar_dataset( images, labels, res=res, sample=sample, return_datasets=True, mixed_state=False, add_seed=0, ) #bad_res_func = bad_res101, up_sample = True) train_dataset_x, train_dataset_y = split_dataset_xy(train_dataset) test_dataset_x, test_dataset_y = split_dataset_xy(test_dataset) #%% print( "##################### Fit {} and trajectories model on training data res = {} ##################" .format(rnn_net.name, res)) rnn_history = rnn_net.fit( train_dataset_x, train_dataset_y,
rnn_net = parallel_gru(n_timesteps=sample, hidden_size=256, input_size=res, cnn_dropout=0.4, rnn_dropout=0.2, lr=1e-4, concat=True) train_accuracy_prll = [] test_accuracy_prll = [] test_no_coor_accuracy_prll = [] for epep in range(num_learning_epochs): train_dataset, test_dataset = create_cifar_dataset( images, labels, res=res, sample=sample, return_datasets=True, add_seed=num_trajectories, mixed_state=True) train_dataset_x, train_dataset_y = split_dataset_xy( train_dataset, sample) test_dataset_x, test_dataset_y = split_dataset_xy(test_dataset, sample) del train_dataset del test_dataset gc.collect() print("##########Fit {} and trajectories model on training data######". format(net.name)) history = net.fit( train_dataset_x, train_dataset_y, batch_size=64,
#%%#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jul 14 14:59:31 2021 @author: orram """ ############################## load syclop data ################################# print('loading Syclop Data') sample = 10 traject_data_path = path +'traject_data/' train_dataset, test_dataset = create_cifar_dataset(images, labels,res = 8, sample = sample, return_datasets=True, mixed_state = False, add_seed = 0, trajectory_list = trajectory_index, ) train_dataset_x, train_dataset_y = split_dataset_xy(train_dataset, sample = sample) test_dataset_x, test_dataset_y = split_dataset_xy(test_dataset,sample = sample) print('loaded trajectory data') #traject_data_path = '/home/labs/ahissarlab/orra/imagewalker/teacher_student/traject_data/' traject_data_path = path +'traject_data/' #%% ##################### Define Student ######################################### epochs = 1