After I call this function like this:
from tensorflow.contrib import learn
#----------------------------------------
#Do some process here
#----------------------------------------
classifier = learn.Estimator(model_fn=bag_of_words_model,model_dir='F:/data')
classifier.fit(feature_train, target_train, steps=1000)
I will have some file in my folder "F:/data" like this
And I wonder Do I have anyway to reuse this model ? Like move to new computer and use this to predict new data. Sorry for my bad English. Thanks for all the answers!! Hope you all have a nice day.
In a script where you want to reuse your model, redefine/import bag_of_words_model again and define
classifier_loaded = learn.Estimator(model_fn=bag_of_words_model, model_dir='F:/data')
Tensorflow will reload the graph and import the weights into the graph and you can just use it as
classifier_loaded.predict(input_fn=input_fn)
or continue the training with
claissifier_loaded.fit(feature_train, target_train)
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!
PyCaret seems like a great AutoML tool. It works, fast and simple and I would like to download the generated pipeline code into .py files to double check and if needed to customize some parts. Unfortunately, I don't know how to make it real. Reading the documentation have not helped. Is it possible or not?
It is not possible to get the underlying code since PyCaret takes care of this for you. But it is up to you as the user to decide the steps that you want your flow to take e.g.
# Setup experiment with user-defined options for preprocessing, etc.
setup(...)
# Create a model (uses training split only)
model = create_model("lr")
# Tune hyperparameters (user can pass a custom tuning grid if needed)
# Again, uses training split only
tuned = tune_model(model, ...)
# Finalize model (so that the best hyperparameters are retrained on the entire dataset
finalize_model(tuned)
# Any other steps you would like to do.
...
Finally, you can save the entire pipeline as a pkl file for use later
# Saves the model + pipeline as a pkl file
save_model(final, "my_best_model")
You may get a partial answer: incomplete with 'get_config("prep_pipe")' in 2.6.10 or in 3.0.0rc1
Just run a setup like in examples, store as a cdf1, and try cdf.pipeline and you may get a text like this: Pipeline(..)
When working with pycaret=3.0.0rc4, you have two options.
Option 1:
get_config("pipeline")
Option 2:
lb = get_leaderboard()
lb.iloc[0]['Model']
Option 1 will give you the transformations done to the data whilst option 2 will give you the same plus the model and its parameters.
Here's some sample code (from a notebook, based on their documentation on the Binary Classification Tutorial (CLF101) - Level Beginner):
from pycaret.datasets import get_data
from pycaret.classification import *
dataset = get_data('credit')
data = dataset.sample(frac=0.95, random_state=786).reset_index(drop=True)
data_unseen = dataset.drop(data.index).reset_index(drop=True)
exp_clf101 = setup(data = data, target = 'default', session_id=123)
best = compare_models()
evaluate_model(best)
# OPTION 1
get_config("pipeline")
# OPTION 2
lb = get_leaderboard()
lb.iloc[0]['Model']
I'm using the pre-trained model:
import fasttext.util
fasttext.util.download_model('en', if_exists='ignore') # English
ft = fasttext.load_model('cc.en.300.bin')
Where can I find an exhaustive list of the values of the hyperparameters used to train the model?
https://fasttext.cc/docs/en/options.html list the default values, that differ from the used one: for example, the dimension of the word vectors is 300 and not 100 (citing https://fasttext.cc/docs/en/crawl-vectors.html that doesn't list them all).
From looking at the _FastText Python model class in Facebook's source...
https://github.com/facebookresearch/fastText/blob/a20c0d27cd0ee88a25ea0433b7f03038cd728459/python/fasttext_module/fasttext/FastText.py#L99
...it looks like, at least when creating a model, all the hyperparameters are added as attributes on the object.
Have you checked if that's the case on your loaded model? For example, does ft.dim report 300, and other parameters like ft.minCount report anything interesting?
Update: As that didn't seem to work, it also looks like the _FastText model wraps an internal instance of a native (not-in-Python) FastText model in its .f attribute. (See a few lines up from the source code I pointed to earlier.)
And that native-instance is set up by the module specified by fasttext_pybind.cc. That code looks like it specified a bunch of read-write class variable, associated with the metaparameters - see for example starting at:
https://github.com/facebookresearch/fastText/blob/a20c0d27cd0ee88a25ea0433b7f03038cd728459/python/fasttext_module/fasttext/pybind/fasttext_pybind.cc#L88
So: does ft.f.minCount or ft.f.dim return anything useful from a post-loaded model ft?
Citing NVS Abhilash from https://github.com/facebookresearch/fastText/issues/887#issuecomment-649018188 the right code to write is:
args_obj = ft.f.getArgs()
for hparam in dir(args_obj):
if not hparam.startswith('__'):
print(f"{hparam} -> {getattr(args_obj, hparam)}")
This will print all the hyperparameters of the trained model!
I am currently trying to implement Mask RCNN by following Matterport repo. I have a doubt regarding implementation of tensorboard.
The dataset is similar to coco dataset. Inside model.py under def train, tensorboard is mentioned as
callbacks = [ keras.callbacks.TensorBoard(log_dir=self.log_dir,histogram_freq=0, write_graph=True, write_images=False)
But What else I should mention for using tensorboard? When I try to run the tensorboard, it say log file not found. I know that there is something I am missing some where!!. Please help me out !
In your model.train() ensure you set custom_callbacks = callbacks parameter.
If you specified these parameters exactly like this, then it means that your issue is that you do not properly open the logs directory.
Open (inside Anaconda/PyCharm) or a separate Python terminal and put the absolute path(to make sure it works):
tensorboard --logdir = my_absolute_path/logs/
I'm using CNTK as the backend for Keras. I'm trying to use my model which I have trained using Keras in C++.
I have trained and saved my model using Keras which is in HDF5. How do I now use CNTK API to save it in their model-v2 format?
I tried this:
model = load_model('model2.h5')
cntk.ops.functions.Function.save(model, 'CNTK_model2.pb')
but i got the following error:
TypeError: save() missing 1 required positional argument: 'filename'
If tensorflow were the backend I would have done this:
model = load_model('model2.h5')
sess = K.get_session()
tf_saver = tf.train.Saver()
tf_saver.save(sess=sess, save_path=checkpoint_path)
How can I achieve the same thing?
As per the comments here, I was able to use this:
import cntk as C
import keras.backend as K
keras_model = K.load_model('my_keras_model.h5')
C.combine(keras_model.model.outputs).save('my_cntk_model')
cntk_model = C.load_model('my_cntk_model')
You can do something like this
model.outputs[0].save('CNTK_model2.pb')
I'm assuming here you have called model.compile (i.e. that's the only case I have tried :-)
The reason you see this error is because keras' cntk backend use a user defined function to do reshape on batch axis, which can't been serialized. We have fixed this issue in CNTK v2.2. Please upgrade your cntk to v2.2, and upgrade keras to last master.
Please see this pull request:
https://github.com/fchollet/keras/pull/7907