def test_net(train_set, train_labels, valid_set, valid_labels, test_set, learning_rate, \
decrease_constant, size, l2, l1, function) :
"""
Train and validate the neural net with
a given set of parameters.
Returns the final test output.
"""
neuralNet = NeuralNetwork(lr=learning_rate, dc=decrease_constant, sizes=size, L2=l2, L1=l1,
seed=5678, tanh=function, n_epochs=10)
n_classes = 10
print "Training..."
# Early stopping code
best_val_error = np.inf # Begin with infinite error
best_it = 0 # Iteration of the best neural net so far wrt valid error
look_ahead = 5
n_incr_error = 0
for current_stage in range(1,500+1,1):
#Stop training when NN has not improved for 5 turns.
if not n_incr_error < look_ahead:
break
neuralNet.n_epochs = current_stage
neuralNet.train(train_set, train_labels, n_classes)
n_incr_error += 1
outputs, errors, accuracy = neuralNet.test(train_set, train_labels)
print 'Epoch',current_stage,'|',
print 'Training accuracy: ' + '%.3f'%accuracy+',', ' |',
outputs, errors, accuracy = neuralNet.test(valid_set, valid_labels)
print 'Validation accuracy: ' + '%.3f'%accuracy
# Check if this model is better than the previous:
error = 1.0 - accuracy
if error < best_val_error:
best_val_error = error
best_it = current_stage
n_incr_error = 0
best_model = copy.deepcopy(neuralNet) # Save the model.
#TODO Clear train and valid set to free memory.
#Load test set
outputs = best_model.predict(test_set)
def validate_net(train_set, train_labels, valid_set, valid_labels, learning_rate, \
decrease_constant, size, l2, l1, function) :
"""
Train and validate the neural net with
a given set of parameters.
Return the best accuracy.
"""
neuralNet = NeuralNetwork(lr=learning_rate, dc=decrease_constant, sizes=size, L2=l2, L1=l1,
seed=5678, tanh=function, n_epochs=10)
n_classes = 10
print "Training..."
# Early stopping code @Hugo Larochelle (partially)
best_val_error = np.inf # Begin with infinite error
best_it = 0 # Iteration of the best neural net so far wrt valid error
look_ahead = 5
n_incr_error = 0
for current_stage in range(1,500+1,1):
#Stop training when NN has not improved for 5 turns.
if not n_incr_error < look_ahead:
break
neuralNet.n_epochs = current_stage
neuralNet.train(train_set, train_labels, n_classes)
n_incr_error += 1
outputs, errors, train_accuracy = neuralNet.test(train_set, train_labels)
print 'Epoch',current_stage,'|',
print 'Training accuracy: ' + '%.3f'%train_accuracy+',', ' |',
outputs, errors, valid_accuracy = neuralNet.test(valid_set, valid_labels)
print 'Validation accuracy: ' + '%.3f'%valid_accuracy
# Check if this model is better than the previous:
error = 1.0 - valid_accuracy
if error < best_val_error:
best_val_error = error
best_train_accuracy = train_accuracy
n_incr_error = 0
return 1 - best_val_error, best_train_accuracy
parameter_initialization=(pretrained_bs,pretrained_Ws))
print "Fine-tuning..."
# Early stopping code
best_val_error = np.inf
best_it = 0
str_header = 'best_it\t'
look_ahead = 5
n_incr_error = 0
for stage in range(1,500+1,1):
if not n_incr_error < look_ahead:
break
myObject.n_epochs = stage
myObject.train(trainset)
n_incr_error += 1
outputs, costs = myObject.test(trainset)
errors = np.mean(costs,axis=0)
print 'Epoch',stage,'|',
print 'Training errors: classif=' + '%.3f'%errors[0]+',', 'NLL='+'%.3f'%errors[1] + ' |',
outputs, costs = myObject.test(validset)
errors = np.mean(costs,axis=0)
print 'Validation errors: classif=' + '%.3f'%errors[0]+',', 'NLL='+'%.3f'%errors[1]
error = errors[0]
if error < best_val_error:
best_val_error = error
best_it = stage
n_incr_error = 0
best_model = copy.deepcopy(myObject)
outputs_tr,costs_tr = best_model.test(trainset)
columnCount = len(costs_tr.__iter__().next())
nnet = NeuralNetwork()
nnet.train(file_name, model_file, epochs=10000)
elif model == 'best':
best = Best()
best.train(file_name, model_file, epochs=10000)
else:
print 'Specified model not found!!'
else:
if model == 'nearest' or model == 'nnet' or model == 'best':
if model_file.endswith('.txt'):
model_file = model_file + '.npy'
if model == 'nearest':
knn = Knn()
knn.test(file_name, model_file)
elif model == 'nnet':
nnet = NeuralNetwork()
nnet.test(file_name, model_file)
elif model == 'best':
best = Best()
best.test(file_name, model_file)
elif model == 'adaboost':
trial = adaboost.Adaboost(None, file_name)
trial.testing = file_name
test_rotation = trial.prepare_data(trial.testing)[1]
learners = [trial.learner1, trial.learner2]
print "Accuracy on test set: ", trial.get_accuarcy(
test_rotation, trial.test(file_name, model_file)), " %"
else:
print 'Specified model not found!!'
def test(self, file_name, model_file, output_file="best_output.txt"):
nnet = NeuralNetwork()
nnet.test(file_name, model_file, output_file)