def prob_image(args):
list_predictions = list()
pred = Prediction(args.proto_path,args.bin_path)
max_value = 512
curr_value = 0
with open(args.images,'r') as file_image:
list_images = list()
list_good_class = list()
for line in file_image:
splitted = line.split('\t')
list_images.append(splitted[2].strip())
curr_value = curr_value + 1
if curr_value < max_value:
continue
else:
#predict using value
predictions = pred.predict_multi(list_images)
list_images = list()
list_good_class = list()
curr_value = 0
list_predictions.append(predictions)
print predictions.shape
if len(list_images) > 0:
#predict using value
predictions = pred.predict_multi(list_images)
list_predictions.append(predictions)
all_pred = np.vstack(list_predictions)
np.save('fine_sand.npy',all_pred)
np.savetxt('probabilities_kaggle_aug_fine_m.txt', all_pred, fmt='%1.9e')
def test_accuracy_multi(args):
# Compute Accuracy of images from input file
# Code predict many images at one time. The value is set by max_value
# Same value should be set in proto file
# It is done because of speed reason
pred = Prediction(args.proto_path,args.bin_path)
max_value = 512
curr_value = 0
list_all_result = list()
list_good_class_all = list()
with open(args.images,'r') as file_image:
list_images = list()
list_good_class = list()
for line in file_image:
splitted = line.split(' ')
#if not os.path.isfile(splitted[0].strip()):
#continue
list_good_class.append(int(splitted[1]))
list_images.append(splitted[0].strip())
curr_value = curr_value + 1
if curr_value < max_value:
continue
else:
#predict using value
predictions = pred.predict_multi(list_images)
list_all_result.append(predictions)
list_good_class_all.extend(list_good_class)
list_good_class = list()
list_images = list()
curr_value = 0
#predict last package of data, which is smaller than max_value
if len(list_images) > 0:
predictions = pred.predict_multi(list_images)
list_all_result.append(predictions)
#test loss
list_good_class_all.extend(list_good_class)
y_truth = np.asarray(list_good_class_all)
y_pred = np.vstack(list_all_result)
y_pred_label = np.argmax(y_pred, axis=1)
print y_truth.shape, y_pred.shape
print "Accuracy: ", accuracy_score(y_truth,y_pred_label)
print "Loss: ",multiclass_log_loss(y_truth,y_pred)
plot_confusion_matrix(y_truth,y_pred_label)
np.save('casia_deep.npy',y_pred)
