I trained a neural network with google colab.
I saved the neural network using joblib.dump()
I then loaded the model on my PC using joblib.load()
I made a prediction on the exact same sample, using the same model, on both colab and my PC. On colab, it has an output of [[0.51]]. On my pc, it has an output of [[nan]].
The model summary reports that the architecture of the model is the same.
I checked the weights of the model I loaded on my PC, and the model on colab, and the weights are the exact same.
Any ideas as to what I can do? Thank you.
Quick update: even if I change all of my inputs to zero, the prediction is still nan.
As far as I know keras has its own function to save the model such as model.save('file.h5'), and the joblib library is used to save sklearn models.
Related
I'm new to TensorFlow and started training my model in Google Collaboratory. After spending a few hours training my model, I was finally able to download the tflite file, and it's working great! The only issue I have with it is its speed. I've looked into post-training quantization, but it seems as if I still need the Keras model to do that, but all I have left is the actual tflite file itself, as the notebook has since been closed and all data lost. Is there any way I can quantize the file itself?
Thank you in advance for replies.
I tried using the tf.lite.Interpreter to load the model into a keras optimizer, but that didn't work.
I am trying to train a TensorFlow object detection model on a custom dataset on google colab and I have a saved model trained for 5000 steps, is it possible to use saved model to resume training? I am planning to train for another 20000 steps. I am using google colab for training and the training will take around 36 hours, so I'm planning to use checkpoint. How to store best model checkpoints and use them when session runs out?
For resuming training using weights from a saved checkpoint, in your pipeline.config file, change the line containing fine_tune_checkpoint from <path_to_ckpt>/model.ckpt to <path_to_ckpt>/model.ckpt-XXXX where XXXX is your checkpoint number.
As far as saving only best weights is concerned, you can refer to this post and/or this GitHub link
I created a model of darknet53.weights for image classification using my original data in darknet.
(This isn't a YOLO v3 model.)
Is there a way to convert a darknet53.weight to a pytorch pt model?
I tried quoting various codes on github etc., but all of them can convert only YOLOv3 weights file to pytorch's pt model.
I want to compare the accuracy of the darknet53 model created with darknet with other image classification models created with pytorch.
Initially, I tried to make a darknet53 model with pytorch, but that didn't work. Therefore, I created a darknet53 model with darknet.
If anyone knows a good way, please teach me.
Thanks.
I am a novice in using tensorflow and I built a CNN which is trained and tested both with 80-85% accuracy. I tried to save my trained model using model.save('example.h5'), and download the file using files.download('example.h5')
Afterwards I tried to load it to my flask back-end using model = tf.keras.models.load_model('example.h5').
When I tried using it with random images, it's like the model has never been trained before. Any solutions? thank you
A common reason to this might be that you are normalizing your data on training but not before predicting. Make sure you normalize the input data before you run predictions, if you have trained on normalized data.
I built a CNN model for image classification using the Keras library. However training takes many hours. Once I trained my model, how can I use it without training once more? I mean after I trained my model, I want to use it many times.
Because I will use my model in android studio.
Any help is appreciated
Thank YOU...
EDIT
When I wrote this question, I did not know the save model and load.model, in the answers you see the appropriate usage of them.
You can easily save your model after the training process by using:
model.save('my_model.h5')
you can later load that model by using:
model = load_model('my_model.h5')
for more details have a look at the documentation: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model