def run_tests():
import problem_unittests as t
t.test_folder_path(cifar10_dataset_folder_path)
t.test_normalize(normalize)
t.test_one_hot_encode(one_hot_encode)
t.test_nn_image_inputs(neural_net_image_input)
t.test_nn_label_inputs(neural_net_label_input)
t.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
t.test_con_pool(conv2conv2d_maxpool)
t.test_flatten(flatten)
t.test_fully_conn(fully_conn)
t.test_output(output)
t.test_conv_net(conv_net)
t.test_train_nn(train_neural_network)
def runCifarTest(self):
if isfile(self.floyd_cifar10_location):
self.tar_gz_path = self.floyd_cifar10_location
else:
self.tar_gz_path = 'cifar-10-python.tar.gz'
if not isfile(self.tar_gz_path):
with DLProgress(unit='B',
unit_scale=True,
miniters=1,
desc='CIFAR-10 Dataset') as pbar:
urlretrieve(self.url, self.tar_gz_path, pbar.hook)
if not isdir(self.cifar10_dataset_folder_path):
with tarfile.open(self.tar_gz_path) as tar:
tar.extractall()
tar.close()
tests.test_folder_path(self.cifar10_dataset_folder_path, self)
if not isfile('cifar-10-python.tar.gz'):
with DLProgress(unit='B',
unit_scale=True,
miniters=1,
desc='CIFAR-10 Dataset') as pbar:
urlretrieve('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
'cifar-10-python.tar.gz', pbar.hook)
if not isdir(cifar10_dataset_folder_path):
with tarfile.open('cifar-10-python.tar.gz') as tar:
tar.extractall()
tar.close()
tests.test_folder_path(cifar10_dataset_folder_path)
# ## Explore the Data
# The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:
# * airplane
# * automobile
# * bird
# * cat
# * deer
# * dog
# * frog
# * horse
# * ship
# * truck
#
# Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.
self.last_block = block_num
if not isfile(tar_gz_path):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
urlretrieve(
'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
tar_gz_path,
pbar.hook)
if not isdir(cifar10_dataset_folder_path):
with tarfile.open(tar_gz_path) as tar:
tar.extractall()
tar.close()
tests.test_folder_path(cifar10_dataset_folder_path)
# ## Explore the Data
# The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:
# * airplane
# * automobile
# * bird
# * cat
# * deer
# * dog
# * frog
# * horse
# * ship
# * truck
#
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile(download_link):
with DownloadProgress(unit='B',
unit_scale=True,
miniters=1,
desc='File downloaded') as progress_bar:
urlretrieve(download_link, download_folder + 'my_file.tar.gz',
progress_bar.hook)
tests.test_folder_path(
download_folder) # to test if the file downloaded properl
def main():
given_checksum_output = urlopen(checksum_file_link).read().decode(
'UTF-8').split()[0]
file_checksum_output = os.popen(checksum_format + ' ' + download_folder +
'/' + 'my_file.tar.gz').read().split()[0]
if file_checksum_output == given_checksum_output:
print(
'Congratulations, the file you have downloaded is identical to the source!'
)
else:
print(
'Sorry, the file you have downloaded is not identical to the source!'
)
