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')
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 kinda new to TensorFlow and Keras, so please excuse any accidental stupidity, but I have an issue. I've been trying to load in models from the TensorFlow Detection Zoo, but haven't had much success.
I can't figure out how to read these saved_model folders (they contain a saved_model.pb file, and an assets and variables folder), so that they're accepted by Keras. Nor can I figure out a way to convert these models so that they may be loaded in. I've tried converting the SavedModel to ONNX, and then convert the ONNX-model to Keras, but that didn't work. Trying to load the original model as a saved_model, and then trying to to save this loaded model in another format gave me no success either.
Since you are new to Tensorflow (and I guess deep learning) I would suggest you stick with the API because the detection zoo models best interface with the object detection API. If you have already downloaded the model, you just need to export it using the exporter_main_v2.py script. This article explains it very well link.
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 want to use Tensorboard with a non tensorflow app. I can see how to make the graph using GraphDef and associated classes but not sure how to write it out so that Tensorboard will read it. This means that I have the graph in a serialized form and it's not the python graph class from tensorflow.
For seeing graph in tensorboard, you need weight, tensor name and structure of the graph.
I dont understand your question totally, but if you are able to create graph.pb file then , it will be simple to see that thing in tensorboard you have to run this file here.
actually here we are creating dummy graph structure using
graph_def = graph_pb2.GraphDef()
and then giving our .pb file to set all these weight and name
graph_def.ParseFromString('pb file directory to read'.read())
import_graph_def(graph_def)
let me know more detail so that i can help you better way.
I am using tflearn and I am new to it. I created a model and saved it. I am also able to load that model. But still I have a question unanswered.
What does model.load exactly do and how to persist that in memory even after the script ends?
model.load(model_file='path') will load pre-saved model from path.
Suppose if you save a model using model.save('my_tf_model') and that will save the built model as my_tf_model.meta my_tf_model.index my_tf_model.data-NNNNN-of-NNNNN
you would retrieve this model using model.load('my_tf_model') and then use it for prediction.