def __init__(self, model_name): """ Arguments: model_name (str): Name of trained supervised model for first step. """ self._model_name = model_name self._model, self._feat_list, self._scaler, self._model_params = load_model( self._model_name)
def test_process(): output_dir_for_model = './data/model.pkl.gz' vocab_file = 'ende_32k.subword' vocab_dir = '/home/dimitris/Documents/tutorials/NLP_specialization_coursera/course4_attension_models/week1new/data/' EOS_index = 1 model = load_model(output_dir_for_model) result = sampling_decode("i love you", model, 0.0, vocab_file, vocab_dir, EOS_index) print(result)
def load_model(): # model = utils.load_model('matches_f85_1hop', feature_amount=85)[0] model = utils.load_model('matches_f85_th81', feature_amount=85)[0] return model
import numpy as np from glob import glob from keras.applications.resnet50 import preprocess_input from ml_utils import load_model, path_to_tensor # Took this strange approach with hardcoding path length from notebook # Hope there are more elegant ways to do it in python # But for now I better save some time) dog_names = [item[23:-1] for item in sorted(glob('../dogImages/train/*/'))] dog_detector = load_model('dog_detector') bottleneck = load_model('bottleneck') loaded_model = load_model('model') def predict_dog_labels(img_path): # returns prediction vector for image located at img_path img = preprocess_input(path_to_tensor(img_path)) return np.argmax(dog_detector.predict(img)) def detect_dog(img_path): prediction = predict_dog_labels(img_path) return ((prediction <= 268) & (prediction >= 151)) def predict_breed(img_path): # extract bottleneck features bottleneck_feature = bottleneck.predict(path_to_tensor(img_path)) # obtain predicted vector predicted_vector = loaded_model.predict(bottleneck_feature)
def mouse_detector(board, board_size, px_size, screen): logistic_reg_model = load_model("data/LR.sav") knn_model = load_model("data/KNN.sav") lda_model = load_model("data/LDA.sav") nnet_model = load_model("data/NN.sav") svd_model = load_model("data/SVD.sav") stroke = 3 running = True is_drawing = False prediction = '' while running: for event in pygame.event.get(): mouse_pos = pygame.mouse.get_pos() draw_screen(screen, board, board_size, event.type, interaction_handler(screen, mouse_pos, board_size)) draw_prediction(screen, board_size, prediction) strokeDraw = pygame.font.Font('freesansbold.ttf', 20).render('Stroke = ' + str(stroke), True, (0, 0, 0)) stroke_rect = strokeDraw.get_rect() stroke_rect.center = (screen.get_size()[0] * 4 / 5, screen.get_size()[1] * 19 / 20) if event.type == pygame.MOUSEBUTTONDOWN: if (interaction_handler(screen, mouse_pos, board_size) == Click_Element.RESET): prediction = '' board = init_board() elif (interaction_handler(screen, mouse_pos, board_size) == Click_Element.MLR): prediction = logistic_regression_prediction( logistic_reg_model, board) elif (interaction_handler(screen, mouse_pos, board_size) == Click_Element.KNN): prediction = knn_prediction(knn_model, board) elif (interaction_handler(screen, mouse_pos, board_size) == Click_Element.LDA): prediction = lda_prediction(lda_model, board) elif (interaction_handler(screen, mouse_pos, board_size) == Click_Element.SVD): prediction = svd_prediction(svd_model, board) elif (interaction_handler(screen, mouse_pos, board_size) == Click_Element.NN): prediction = nnet_prediction(nnet_model, board) is_drawing = True elif event.type == pygame.KEYDOWN: if event.key == pygame.K_KP_MINUS and stroke > 1: stroke -= 2 elif event.key == pygame.K_KP_PLUS and stroke < 5: stroke += 2 elif event.type == pygame.MOUSEMOTION: if is_drawing and mouse_pos[0] < board_size and mouse_pos[ 1] < board_size: index = board_position_handler(mouse_pos, px_size) if index != None: draw_stroke(board, index[0], index[1], stroke) elif event.type == pygame.MOUSEBUTTONUP: is_drawing = False if event.type == pygame.QUIT: running = False screen.blit(strokeDraw, stroke_rect) pygame.display.update()
def load_model(): model = utils.load_model(profile['model'], feature_amount=85)[0] return model