I use the
#tensorflow/tfjs-node
package to train a TensorFlow model and would like to save it as tflite.
I'm able to save it with
model.save('file://./myModel');
and get a myModel folder with the model.json and the weights.bin
But how do I convert this to a tflite model using tfjs-node?
Related
Having followed Chris McCormick's tutorial for creating a BERT Fake News Detector (link here), at the end he saves the PyTorch model using the following code:
output_dir = './model_save/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
As he says himself, it can be reloaded using from_pretrained(). Currently, what the code does is create an output directory with 6 files:
config.json
merges.txt
pytorch_model.bin
special_tokens_map.json
tokenizer_config.json
vocab.json
So how can I use the from_pretrained() method to load the model with all of its arguments and respective weights, and which files do I use from the six?
I understand that a model can be loaded as such (from PyTorch documentation):
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()
but how can I make use of the files in the output directory to do this?
Any help is appreciated!
I am running into the problem whenever I try to load custom trained NER model of spacy inside docker container.
Note:
I am using latest spacy version 3.0 and trained that NER model using CLI commands of spacy, first by converting Train data format into .spacy format
The error throws as following(You can check error in image as hyperlinked):
config validation error
My trained model file structure looks like this:
custom ner model structure
But while run that model without docker it works perfectly. What wrong I have done in this process. Plz help me to resolve the error.
Thank you in advance.
I am saving keras model
model.save('model.h5')
in databricks, but model is not saving,
I have also tried saving in /tmp/model.h5 as mentioned here
but model is not saving.
The saving cell executes but when I load model it shows no model.h5 file is available.
when I do this dbfs_model_path = 'dbfs:/FileStore/models/model.h5' dbutils.fs.cp('file:/tmp/model.h5', dbfs_model_path)
OR try loading model
tf.keras.models.load_model("file:/tmp/model.h5")
I get error message java.io.FileNotFoundException: File file:/tmp/model.h5 does not exist
The problem is that Keras is designed to work only with local files, so it doesn't understand URIs, such as dbfs:/, or file:/. So you need to use local paths for saving & loading operations, and then copy files to/from DBFS (unfortunately /dbfs doesn't play well with Keras because of the way it works).
The following code works just fine. Note that dbfs:/ or file:/ are used only in the calls to the dbutils.fs commands - Keras stuff uses the names of local files.
create model & save locally as /tmp/model-full.h5:
from tensorflow.keras.applications import InceptionV3
model = InceptionV3(weights="imagenet")
model.save('/tmp/model-full.h5')
copy data to DBFS as dbfs:/tmp/model-full.h5 and check it:
dbutils.fs.cp("file:/tmp/model-full.h5", "dbfs:/tmp/model-full.h5")
display(dbutils.fs.ls("/tmp/model-full.h5"))
copy file from DBFS as /tmp/model-full2.h5 & load it:
dbutils.fs.cp("dbfs:/tmp/model-full.h5", "file:/tmp/model-full2.h5")
from tensorflow import keras
model2 = keras.models.load_model("/tmp/model-full2.h5")
I am trying to build a speech recognition model following this tutorial
https://www.analyticsvidhya.com/blog/2019/07/learn-build-first-speech-to-text-model-python/
there is 2 part, the first is a training model which output is the input of the second part ( testing model)
at the end of the training model, there is this part which should save the result of the training
model = Model(inputs, outputs)
model.summary()
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, min_delta=0.0001)
mc = ModelCheckpoint('best_model.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='max')
so the result should be saved in this file "best_model.hdf5"
this model run without any error but I didn't found any file created
when I tried to load the model in testing model, I got an error message that this file wasn't found
any help please ?
keras version installed: 2.3.1
update 1:
I tried to know the location at which your code is running using:
print(os.getcwd())
I got the same direction of model file, I tried to put this location in the code to save in it and to load from it but still there is no file created and I got the same error message
update 2:
I add
print(os.listdi())
after ModelCheckpoint function and also I didn't find it
How about try to define the checkpoint_file_path separately and use that variable in the function call? This mostly happens because of the "/" before the file path name. so you can try "/best_model.hdf5"
What I am trying to do is to convert my trained CNN to TfLite and use it in my android app. AFAIK I need the .pbtxt in order to freeze the parameters and do the conversion.
However when I save my network using this standard code:
saver = tf.train.Saver(max_to_keep=4)
saver.save(sess=session, save_path="some_path", global_step=step)
I only get the
.data
.index
.meta
checkpoint
files. No pbtxt.
Is there a way to convert the trained network to tflite without a pbtxt or can I obtain the pbtxt from those files?
Thank you
Simply execute:
tf.train.write_graph(session.graph.as_graph_def(),
"path",
'model.pb',
as_text=False)
to get a .pb or
tf.train.write_graph(session.graph.as_graph_def(),
"path",
'model.pbtxt',
as_text=True)
to get the text version.