AI 모델 결과 활용_tensorflow

dnn_loss = pd.DataFrame(history.history)[['loss', 'val_loss']]
sns.lineplot(data = dnn_loss)
h_model = load_model('model')
print(f'result: { h_model  .predict(h)}')

c_model= tf.keras.models.load_model('model')
c_model .predict(c)

from keras.models import load_model

dnn_loss = pd.DataFrame(history.history)[['loss', 'val_loss']]
o_model= load_model('model')
o_model .predict(o)

from keras.models import load_model
m_model = load_model('model')
print( m_model  .predict(a), m_model  .predict(b), m_model  .predict(c))

plt.plot(history.history['val_accuracy'])
plt.plot(history.history['val_loss'])
plt.title('Model Valid Accuracy/Loss')
plt.xlabel('Epochs')
plt.ylabel('Accuracy/Loss')
plt.legend(['val_accuracy', 'val_loss'])
plt.show()