def task(task_id):
# get data from task
task_data = utils.get_task_data(task_id)
return render_template(
'task.html',
task_data=task_data)
def add_task():
if request.method == 'GET':
data = {}
task_id = request.args.get('id', None)
if task_id:
# get data from task
data = utils.get_task_data(task_id)
return render_template(
'add_task.html',
data=data)
if request.method == 'POST':
errors = []
data = {}
data['name'] = request.form.get('nameInput', None)
data['project'] = request.form.get('projectInput', None)
data['environment'] = request.form.get('environmentInput', '')
data['url'] = request.form.get('urlInput', None)
data['category'] = request.form.get('categorySelect', None)
data['type'] = request.form.get('typeSelect', None)
data['status_code'] = request.form.get('statusCodeInput', '')
data['request_body'] = request.form.get('requestBodyInput', '')
data['content'] = request.form.get('contentInput', '')
data['interval'] = request.form.get('intervalInput', None)
data['mail_recipients'] = request.form.get('recipientsInput', '')
data['id'] = request.form.get('taskIdInput', None)
print data['id']
# validate name
if not data['name']:
errors.append('Name is required')
elif len(data['name']) > 40:
errors.append('Name must be shorter than 41 characters')
# validate project
if not data['project']:
errors.append('Project is required')
elif len(data['project']) > 40:
errors.append('Project must be shorter than 41 characters')
# validate environment
if len(data['environment']) > 40:
errors.append('Environment must be shorter than 41 characters')
# validate url
if not data['url']:
errors.append('URL is required')
elif len(data['url']) > 150:
errors.append('URL must be shorter than 151 characters')
# validate category
if not data['category'] in ['service', 'web']:
errors.append('Wrong category value')
if data['category'] == 'service':
# validate request body
if not data['request_body']:
errors.append('Request Body is required')
else:
if len(data['request_body']) > 3000:
errors.append('Request Body must be shorter than 3001 characters')
# validate test type
if not data['type'] in ['status_code', 'content']:
errors.append('Wrong Test Type value')
elif data['type'] == 'status_code':
# validate status code
if not data['status_code']:
errors.append('Status Code is required')
elif len(data['status_code']) > 10:
errors.append('Status Code must be shorter than 11 characters')
elif not data['status_code'].isdigit():
errors.append('Status Code must be an integer')
elif data['type'] == 'content':
# validate content
if not data['content']:
errors.append('Content is required')
elif len(data['content']) > 100:
errors.append('Content must be shorter than 101 characters')
# validate interval
if not data['interval']:
errors.append('Interval is required')
else:
if len(data['interval']) > 4:
errors.append('Interal must be shorter than 5 characters')
else:
if not data['interval'].isdigit():
errors.append('Interval must be an integer')
else:
if int(data['interval']) > 9999:
errors.append('Interval must be shorter than 1000')
if int(data['interval']) < 30:
errors.append('Interval must be greater than or equal 30')
if data['mail_recipients']:
if len(data['mail_recipients']) > 3000:
errors.append('Mail Recipients must be shorter than 3001 characters')
if not errors:
# create new task
utils.create_update_task(data)
return redirect('/')
else:
return render_template(
'add_task.html',
errors=errors,
data=data)
encoders = []
decoders = []
# train autoencoders
for task in range(5):
# create autoencoder for the current new task
autoencoder = utils.build_autoencoder(base_autoencoder_name, data_shape,
mean_image, std_image,
autoencoder_hidden_layer_size,
int(n_classes / n_tasks),
weight_decay)
# get train and test data for the current task
X_train_task, y_train_task, X_test_task, y_test_task = utils.get_task_data(
task,
int(n_classes / n_tasks) * n_train_samples_per_class,
int(n_classes / n_tasks) * n_test_samples_per_class, X_train, y_train,
X_test, y_test)
# train the autoencoder for the current task
print(
"---------------- Training autoencoder for the task %d ----------------"
% task)
autoencoder.compile(optimizer='Adagrad',
loss='categorical_crossentropy',
metrics=['accuracy'])
autoencoder.fit(X_train_task,
y_train_task,
batch_size,
epochs,
shuffle=True)
