def test_cntk_fastrcnn_eval_evalCorrect(nb):
if os.getenv("OS") == "Windows_NT" and sys.version_info[0] == 2:
pytest.skip(
'tests with Python 2.7 on Windows are not stable in the CI environment. '
)
# Make sure that the number of detections is more than 0
detectionCells = [
cell for cell in nb.cells if cell.cell_type == 'code'
and len(cell.outputs) > 0 and 'text' in cell.outputs[0] and re.search(
r'Number of detections: (\d+)', get_output_stream_from_cell(cell))
]
assert len(detectionCells) == 1
number_of_detections = int(
re.search(r'Number of detections: (\d+)',
get_output_stream_from_cell(detectionCells[0])).group(1))
assert (number_of_detections > 0)
#Make sure that the last cells was ran successfully
testCells = [
cell for cell in nb.cells if cell.cell_type == 'code'
and len(cell.outputs) > 0 and 'text' in cell.outputs[0]
and re.search('Evaluation result:', get_output_stream_from_cell(cell))
]
assert len(testCells) == 1
def test_cntk_how_to_train_eval_correct(nb):
testCells = [cell for cell in nb.cells if cell.cell_type == 'code']
assert len(testCells) == 5
for c in testCells[1:]:
text = get_output_stream_from_cell(c)
print(text)
assert re.search(expectedOutput, text)
def test_cntk_106B_lstm_timeseries_with_iot_data_evalCorrect(nb, device_id):
testCell = [cell for cell in nb.cells
if cell.cell_type == 'code' and re.search('# Print the test error', cell.source)]
assert len(testCell) == 1
text = get_output_stream_from_cell(testCell[0])
m = re.match(r"mse for test: (?P<actualEvalError>\d+\.\d+)\r?$", text)
assert np.isclose(float(m.group('actualEvalError')), expectedEvalErrorByDeviceId[device_id], atol=0.00002)
def test_csv_correct(nb):
testCells = [cell for cell in nb.cells
if cell.cell_type == 'code']
assert len(testCells) == 6
text = get_output_stream_from_cell(testCells[5])
print(text)
assert re.search(expectedOutput, text)
def test_cntk_how_to_train_eval_correct(nb):
testCells = [cell for cell in nb.cells
if cell.cell_type == 'code']
assert len(testCells) == 5
for c in testCells[1:]:
text = get_output_stream_from_cell(c)
print(text)
assert re.search(expectedOutput, text)
def test_cntk_106B_lstm_timeseries_with_iot_data_evalCorrect(nb, device_id):
if os.getenv("OS")=="Windows_NT" and sys.version_info[0] == 2:
pytest.skip('tests with Python 2.7 on Windows are not stable in the CI environment. ')
testCell = [cell for cell in nb.cells
if cell.cell_type == 'code' and re.search('# Print the test error', cell.source)]
assert len(testCell) == 1
text = get_output_stream_from_cell(testCell[0])
m = re.match(r"mse for test: (?P<actualEvalError>\d+\.\d+)\r?$", text)
assert np.isclose(float(m.group('actualEvalError')), expectedEvalErrorByDeviceId[device_id], atol=0.00002)
def test_cntk_how_to_train_eval_correct(nb):
if os.getenv("OS")=="Windows_NT" and sys.version_info[0] == 2:
pytest.skip('tests with Python 2.7 on Windows are not stable in the CI environment. ')
testCells = [cell for cell in nb.cells
if cell.cell_type == 'code']
assert len(testCells) == 5
for c in testCells[1:]:
text = get_output_stream_from_cell(c)
print(text)
assert re.search(expectedOutput, text)
def test_cntk_203_reinforcement_learning_basics_tasks_are_solved(nb):
if os.getenv("OS") == "Windows_NT" and sys.version_info[0] == 2:
pytest.skip(
'tests with Python 2.7 on Windows are not stable in the CI environment. '
)
testCells = [
cell for cell in nb.cells
if re.search('Task solved in[ :]', get_output_stream_from_cell(cell))
]
assert len(testCells) == 2
def test_csv_correct(nb):
if os.getenv("OS") == "Windows_NT" and sys.version_info[0] == 2:
pytest.skip(
'tests with Python 2.7 on Windows are not stable in the CI environment. '
)
testCells = [cell for cell in nb.cells if cell.cell_type == 'code']
assert len(testCells) == 6
text = get_output_stream_from_cell(testCells[5])
print(text)
assert re.search(expectedOutput, text)
def test_cntk_fastrcnn_eval_evalCorrect(nb):
# Make sure that the number of detections is more than 0
detectionCells = [cell for cell in nb.cells
if cell.cell_type == 'code' and
len(cell.outputs) > 0 and
'text' in cell.outputs[0] and
re.search('Number of detections: (\d+)', get_output_stream_from_cell(cell))]
assert len(detectionCells) == 1
number_of_detections = int(re.search('Number of detections: (\d+)', get_output_stream_from_cell(detectionCells[0])).group(1))
assert(number_of_detections > 0)
#Make sure that the last cells was ran successfully
testCells = [cell for cell in nb.cells
if cell.cell_type == 'code' and
len(cell.outputs) > 0 and
'text' in cell.outputs[0] and
re.search('Evaluation result:', get_output_stream_from_cell(cell))]
assert len(testCells) == 1
def test_cntk_fastrcnn_eval_evalCorrect(nb):
# Make sure that the number of detections is more than 0
detectionCells = [cell for cell in nb.cells
if cell.cell_type == 'code' and
len(cell.outputs) > 0 and
'text' in cell.outputs[0] and
re.search('Number of detections: (\d+)', get_output_stream_from_cell(cell))]
assert len(detectionCells) == 1
number_of_detections = int(re.search('Number of detections: (\d+)', get_output_stream_from_cell(detectionCells[0])).group(1))
assert(number_of_detections > 0)
#Make sure that the last cells was ran successfully
testCells = [cell for cell in nb.cells
if cell.cell_type == 'code' and
len(cell.outputs) > 0 and
'text' in cell.outputs[0] and
re.search('Evaluation result:', get_output_stream_from_cell(cell))]
assert len(testCells) == 1
def test_cntk_106B_lstm_timeseries_with_iot_data_evalCorrect(nb, device_id):
testCell = [
cell for cell in nb.cells if cell.cell_type == 'code'
and re.search('# Print the test error', cell.source)
]
assert len(testCell) == 1
text = get_output_stream_from_cell(testCell[0])
m = re.match(r"mse for test: (?P<actualEvalError>\d+\.\d+)\r?$", text)
assert np.isclose(float(m.group('actualEvalError')),
expectedEvalErrorByDeviceId[device_id],
atol=0.00002)
def test_cntk_fastrcnn_eval_evalCorrect(nb):
if os.getenv("OS")=="Windows_NT" and sys.version_info[0] == 2:
pytest.skip('tests with Python 2.7 on Windows are not stable in the CI environment. ')
# Make sure that the number of detections is more than 0
detectionCells = [cell for cell in nb.cells
if cell.cell_type == 'code' and
len(cell.outputs) > 0 and
'text' in cell.outputs[0] and
re.search(r'Number of detections: (\d+)', get_output_stream_from_cell(cell))]
assert len(detectionCells) == 1
number_of_detections = int(re.search(r'Number of detections: (\d+)', get_output_stream_from_cell(detectionCells[0])).group(1))
assert(number_of_detections > 0)
#Make sure that the last cells was ran successfully
testCells = [cell for cell in nb.cells
if cell.cell_type == 'code' and
len(cell.outputs) > 0 and
'text' in cell.outputs[0] and
re.search('Evaluation result:', get_output_stream_from_cell(cell))]
assert len(testCells) == 1
def test_cntk_203_reinforcement_learning_basics_tasks_are_solved(nb):
if os.getenv("OS")=="Windows_NT" and sys.version_info[0] == 2:
pytest.skip('tests with Python 2.7 on Windows are not stable in the CI environment. ')
testCells = [cell for cell in nb.cells if re.search('Task solved in[ :]', get_output_stream_from_cell(cell))]
assert len(testCells) == 2
def test_cntk_203_reinforcement_learning_basics_tasks_are_solved(nb):
testCells = [cell for cell in nb.cells if re.search('Task solved in[ :]', get_output_stream_from_cell(cell))]
assert len(testCells) == 2
def test_cntk_203_reinforcement_learning_basics_tasks_are_solved(nb):
testCells = [
cell for cell in nb.cells
if re.search('Task solved in[ :]', get_output_stream_from_cell(cell))
]
assert len(testCells) == 2
