x_train, y_train = idg.flow(x_train, y_train)
x_test, y_test = idg2.flow(x_test, y_test)
test_generator = idg2.flow(x_pred, shuffle=False)
#모델링
model = XGBClassifier(learning_rate=0.01, n_jobs=-1)
#훈련
learning_hist = model.fit(x_train, y_train)
# predict
# model.save('C:/data/h5/vision_model2.h5') #모델저장2
# model.save_weights('C:/data/h5/vision_model2_weight.h5') #weight저장
# model.load_model('C:/data/h5/vision_model2.h5') #모델불러오기
# model.load_weights('C:/data/h5/vision_model2_weight.h5') #weight불러오기
result = model.predict_generator(test_generator, verbose=True) / 8
# save val_loss
hist = pd.DataFrame(learning_hist.history)
model.summary()
#3.1 시각화
hist = pd.DataFrame(learning_hist.history)
hist['val_loss'].min
hist.columns
plt.title('Training and validation loss')
plt.xlabel('epochs')
plt.plot(hist['val_loss'])
########################### MODEL 3 ###########################
model3 = XGBClassifier()
#TODO fit generator
model3.fit_generator(train_generator,
steps_per_epoch=15,
epochs=50,
validation_data=validation_generator,
validation_steps=5)
model3.evals_result()
#TODO
score = cross_val_score(model3, X_train2, y_train2, cv=5)
#TODO predict generator
y_pred3 = model3.predict_generator(validation_generator, steps=5)
count = 0
for i in range(y_pred3.shape[0]):
# if y_pred3[i] == y_test2[i]:
# count+=1
#TODO compare with actual label
if y_pred3[i] == validation_generator.next():
count += 1
print('Accuracy for model 3 : ' + str((count / y_pred3.shape[0]) * 100))
# ########################### MODEL 4 ###########################
# img_width = 256
# img_height = 256
# img_channel = 3