def modelSubmission():
debug('In model submission method')
if request.method == 'POST':
n = network.get_network(networks, request.form['network'])
if n == None:
debug("Error, can't find the network")
return
if request.form['btn'] == 'Train':
debug("Training network")
method = request.form['method']
number_of_epochs_logistic = int(request.form['epochsLogistic'])
number_of_epochs_multilayer = int(request.form['epochsMultilayer'])
k = int(request.form['k'])
learning_rate_deep = float(request.form['learningRate'])
layers = int(request.form['layers'])
stop_at_100_accuracy = request.form.getlist('100')
if not n.initialized or method != n.method_initialized:
n.initialize(method, learning_rate_deep, layers)
n.train(method,
number_of_epochs_logistic,
number_of_epochs_multilayer,
stop_at_100_accuracy=stop_at_100_accuracy)
#todo : update progress
#t = threading.Thread(target=train_and_redirect, args=(n,))
#t.start()
elif request.form['btn'] == 'Reset':
n.initialized = False
n.best_accuracy = 0
n.accuracy = 0
n.epochs_for_best_accuracy = 0
n.epochs_for_accuracy = 0
n.method_initialized = None
n.trained = False
elif request.form['btn'] == 'Delete':
n.deleteModel()
networks.remove(n)
global summary_initialization, summary_training, summary_output
summary_initialization = False
summary_training = True
summary_output = False
return redirect('/')
def output(path):
debug('In output method')
if request.method == 'POST':
n = network.get_network(networks, path)
debug('network = ' + str(n))
inputs = []
for i in range(n.number_of_inputs):
inputs.append(request.form[str(i)])
debug('input ' + str(i) + ' = ' + request.form[str(i)])
global result
result = n.get_output(map(int, inputs))
global summary_initialization, summary_training, summary_output
summary_initialization = False
summary_training = False
summary_output = True
return redirect('/')
def output(path):
debug('In output method')
if request.method == 'POST':
n = network.get_network(networks, path)
debug('network = ' + str(n))
inputs = []
for i in range(n.number_of_inputs):
inputs.append(request.form[str(i)])
debug('input ' + str(i) + ' = ' + request.form[str(i)])
global result
result = n.get_output(map(int, inputs))
global summary_initialization, summary_training, summary_output
summary_initialization = False
summary_training = False
summary_output = True
return redirect('/')
def modelSubmission():
debug('In model submission method')
if request.method == 'POST':
n = network.get_network(networks, request.form['network'])
if n == None:
debug("Error, can't find the network")
return
if request.form['btn'] == 'Train':
debug("Training network")
method = request.form['method']
number_of_epochs_logistic = int(request.form['epochsLogistic'])
number_of_epochs_multilayer = int(request.form['epochsMultilayer'])
k = int(request.form['k'])
learning_rate_deep = float(request.form['learningRate'])
layers = int(request.form['layers'])
stop_at_100_accuracy = request.form.getlist('100')
if not n.initialized or method != n.method_initialized:
n.initialize(method, learning_rate_deep, layers)
n.train(method, number_of_epochs_logistic, number_of_epochs_multilayer, stop_at_100_accuracy=stop_at_100_accuracy)
#todo : update progress
#t = threading.Thread(target=train_and_redirect, args=(n,))
#t.start()
elif request.form['btn'] == 'Reset':
n.initialized = False
n.best_accuracy = 0
n.accuracy = 0
n.epochs_for_best_accuracy = 0
n.epochs_for_accuracy = 0
n.method_initialized = None
n.trained = False
elif request.form['btn'] == 'Delete':
n.deleteModel()
networks.remove(n)
global summary_initialization, summary_training, summary_output
summary_initialization = False
summary_training = True
summary_output = False
return redirect('/')
