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()
