I am working with this this github repo. They ask us to download and save a pre_trained vgg16 model in nets folder. For this I saved the checkpoints of a pretrained vgg16. Then in the code I noticed they are loading the model this way:
if vgg16_npy_path is None:
vgg16_npy_path = './nets/vgg16.npy'
self.data_dict = np.load(vgg16_npy_path, encoding='latin1').item()
I read a bit about how to save weights of a tensorflow model as .npy but it seemed overly complicated. Is it possible to just save the checkpoints and load it instead of the numpy format. Does it affect anything later?
Related
i have had big troubles today with saving formats while training a style-transfer neural network.
The task is already solved i feel, i only need to save my model and load it again. But i can't find a proper way to do it.
I used the following code from github to train a style-transfer network:
https://github.com/nikhilagrawal2000/Neural-Style-Transfer-with-Eager-Execution/blob/master/Neural_Style_Transfer_with_Eager_Execution.ipynb
I already succesfully trained the network.
Now, i saved the model using the following line:
model.save("/tmp/nst/test.h5")
For applying the saved neural network though, i need to use the network in a .ckpt format.
Can someone tell me how to switch the data formats between h5 and .ckpt ?
Or is there a specific save method for keras, so i can save it as .ckpt?
(--> pseudocode: model.save_cpkt("/tmp/nst/test.ckpt")
Would be extremely happy if someone could explain that to me, i tried it for several hours now without success.
You can save the weights in checkpoint format using:
model.save_weights("modelcheckpoint",save_format="tf")
You can read more about saving weights or models and chepoints here
I have downloaded the .weights and .cfg file for YOLOv3 from darknet (link: https://pjreddie.com/darknet/yolo/) I want to create a model and assign the weights from these files, and I want to save the model with the assigned weights to a .h5 file so that I can load the .h5 model into Keras by using keras.models.load_model().
Please help.
You should check the instructions given in this repository. This is basically the keras implementation of YOLOv3 (Tensorflow backend).
Download YOLOv3 weights from YOLO website.
Convert the Darknet YOLO model to a Keras model.
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
As you have already downloaded the weights and configuration file, you can skip the first step. Download the convert.py script from repository and simply run the above command.
Note: Above command assumes that yolov3.cfg, yolov3.weights and model_data(folder) are present at the same path as convert.py.
For people getting error from this try changing the layers part in 'convert.py'
Not sure if it was version problem but changing the way converter.py file was loading 'keras.layers' solved all errors for me
I am working on tensorflow project. I have successfully train, test and make prediction using python flask. But in order to make prediction each time i have to again load full model using checkpoints. If I save the model .h5 format, I don't need to load the dataset to predict the datasets. I am don't know how to save the tensorflow model in .h5 format using checkpoints. If anybody know how to do, please help me or forward me any link if possible.
Thanks.
You can save and restore tensorflow models using tf.train.Saver class.
Although this doesn't store the models in .h5 format.
You can refer to these sections for further clarity:
Save
Restore
Hope this helps!
I would like to be able to load in all weights and biases of a TensorFlow checkpoint, apply some mathematical operations to the weights and biases (such as thresholding, scaling, etc), and then save a new TensorFlow checkpoint with the same structure as the original but with the edited weights and biases. Using an answer to this question I am able to successfully load a TensorFlow checkpoint and view the names and contents of the tensors within it, but I am unable to make changes to these tensors. This post asks a similar question and the asker is referred to this documentation on MetaGraphs, but I am new to TensorFlow and so I don't see how this information could be helpful.
So, what is the most effective way to load, edit, and then save TensorFlow checkpoints?
I am doing transfer learning in tensorflow with vgg16. I am only training one small layer on top of the 500MB of weights that I got from the numpy npz file on that website. When I save my model, I am specifying just the weights I am training, so the model file is small - however the meta file is 500MB. From reading stackoverflow: what-is-the-tensorflow-checkpoint-meta-file it sounds safe to remove the meta files, but can I configure tensorflow to not write them?
You could try the tf.train.Saver.save with the optional write_meta_graph=False param.
https://www.tensorflow.org/versions/r0.11/api_docs/python/state_ops.html#Saver