机器学习回归模型为每个图像预测相同的值

I am a beginner in Machine learning and NN. I am currently working on a project involving training a regression model, saving it and then loading it to make further predictions using that model. However I'm having a problem. Each time that I model.predict on images it gives out the same predictions. I am not entirely sure what the problem is, maybe it's in the training stage or i'm just doing something wrong. I was following this tutorial
Here are some bits from the code: (This part is training the model and saving it)

model = create_cnn(400, 400, 3, regress=True)
opt = Adam(lr=1e-3, decay=1e-3 / 200)
model.compile(loss="mean_absolute_percentage_error", optimizer=opt)

model.fit(X, Y, epochs=70, batch_size=8)
model.save("D:/statispic2/final-statispic_model.hdf5")

下一个代码部分来自加载模型和进行预测。

model = load_model("D:/statispic2/statispic_model.hdf5")  # Loading the model
prediction = model.predict(images_ready_for_prediction) #images ready for prediction include a numpy array 
#that is loaded with the images just like I loaded them for the training stage.
print(prediction_list)

试用后,这是模型的输出预测:

[[0.05169942]  # I gave it 5 images as parameters 
[0.05169942]
[0.05169942]
[0.05169942]
[0.05169942]]

如果有任何不清楚的地方,或者您想查看更多代码,请告诉我。 提前致谢。

评论