def evaluate_sentences(sentences): model = model_loader('my_model_architecture.json','my_model_weights.h5',False) notes = model.predict_from_model(sentences,False) print("Average note for \"The revenant\" :",model.get_note(np.mean(notes)),np.mean(notes))
if(len(line)==2): if(line[0] not in self.sentiment): self.sentences.append((line[0],float(line[1]))) self.sentiment[line[0]] = 0.75; def load_element(filename): with open(filename, 'rb') as handle: b = pickle.load(handle) return b model = model_loader('my_model_architecture-version-test.json','my_model_weights-version-test.h5',False) model.predict_from_model(["I love this shirt, its color is so nice","The move was so awful, the acting was terrible","Supported by a very clever script, Deadpool is deliciously irreverent, subversive and uproariously funny"]) sentences= load_element("resume-tmp") sentences = zip(sentences.keys(),sentences.values()) sentiment= {} for key,value in sentences: sentiment[key] = model.predict_from_model_solo(key) sizeX = 800 sizeY = 700 colors_list= [color(165,66,35),color(219,145,122),color(232,209,8),color(242,233,160),color(121,210,107),color(204,251,196)] fig_bubble = bubble_fig(sizeX,sizeY,sentences,sentiment,colors_list,True,True,True,"phrases_resume") fig_bubble.show_points_final()