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