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
Related
Is this SavedModel just for Tensorflow front-end applications or it can be use to reload model in keras format. I created it using tf.saved_model.save and now I don't know what to make of it.
Following the guide above I was able to load a SavedModel directory, and it seemingly no use, not trainable nor use to predict input like model.predict, and that the only thing I have since I lost the h5 file in my files **cough trashbin **cough.
Note: I noticed this guide tell me to use tf.keras.models.load_model('inceptionv3')
and it return this
error
You have saved the model using tf.saved_model.save and so the correct way to load it back is tf.saved_model.load('inceptionv3'). This is also suggested in your error image.
After loading model, you can try doing prediction as follows:
model = tf.saved_model.load('inceptionv3')
out = model(inputs)
I'm having trouble working out how to save the full model on the below link.
https://www.tensorflow.org/tutorials/text/image_captioning
I want to use the below code but I cant workout what "model" is in the below. I tried saving the checkpoints but thats not really what I want to do. I realise this is probably a really easy question but for some reason I cant do it.
# Save the entire model as a SavedModel.
!mkdir -p saved_model
model.save('saved_model/my_model')
I am new to Machine Learning and TensorFlow so I'm sorry and please correct me if my understanding is wrong. I have this project, developing a real time traffic-light detection with TensorFlow.
I've been working with pre-trained TensorFlow models such as SSD Mobilenet and Faster R-CNN Resnet. However, the expected accuracy result have not yet reached. I already considered to add some more data to the dataset (my dataset contains +/-1000 images), but because it is more work to add more data (since I have to do another data taking and label all images), which could take days. I want to consider another option.
Is there any way to modify TensorFlow models architecture so I could optimize and make it focused for only traffic light detection? I've been looking through TensorFlow models folder and could not find in which file these model architectures defined.
Any help will be appreciated. Thank you
What you need is fine-tuning a pretrained model on your traffic light dataset. Given you already have about 1000 images for just one class, this is a descent dataset.
In order to perform fine-tuning, there are some important steps to do.
First you need to transform your data into tfrecord format. Follow this tutorial to generate tfrecord files. This is actually a difficult step.
Create a label_map.pbtxt for your model, since it is only traffic light, what you need is this. Here are sample label_map files.
item {
name: "traffic-light"
id: 1
display_name: "traffic-light"
}
Then you need to prepare a pipeline config file for the model. Since you want to have a real-time detector, I suggest you use SSD-mobilenet models. Some sample config files are available here. You can take one of this sample configs and modify some fields to get the pipeline config for your model.
Suppose you choose ssd_mobilenet, then you can modify this config file, ssd_mobilenet_v2_coco.config. Specifically you need to modify these fields:
num_classes: you need to change from 90 to 1 since you only have traffic lights to detect.
input_path: (in both train_input_reader and eval_input_reader), point this to the tfrecord file you created.
label_map_path: (in both train_input_reader and eval_input_reader), point this to the label_map file you created.
fine_tune_checkpoint: set this path to a downloaded pretrained ssd-mobilenet model.
Depending on your training results, you may need to further adjust some of the fields in the config file, but after the training your model will focus only on traffic light class and will likely have a high accuracy.
All the specific tutorials can be found on the repo site. If you have any further questions, you can ask on stackoverflow with tag: object-detection-api and a lot people would help.
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 need the graph file(.pb file) of pretrained openimages dataset. After downloading the model there are only 3 files
labelmap.txt
model.ckpt
model.ckpt.meta
Where can I find the .pb file?
The model.ckpt.meta contains both the graph and the weights. you have to import it using import_metagraph functionality.
Then you should be able to export the graph defn in pb format, if you really need it.
However I was unable to load the ckpt.meta, due to a google-specific op defined in the model. Please let me know if you can figure that out.