Hello tensorflow Community.
i am new in tesnsorflow , i use tensorflow to classify images now i work with cats_dogs dataset.
i want to save my model after training,and load it in an other program to predict other input
Is there a way to do that ?
Related
I created several custom CNN models in TensorFlow 1.14 and output them as frozen graphs. Then I import the frozen.pb files into 'netron.app' to check their structures. Oddly, there are two elements at the input: x and Identity. Here is a screenshot.custom model input
But when I froze a pre-trained model, such as the mobileNetV2, the input only has the x. pre-trained mobileNetV2 input
Does anyone have a clue why?
Do we have an option to save a trained Gensim Word2Vec model as a saved model using tf 2.0 tf.saved_model.save? In other words, how can I save a trained embedding vector as a saved model signature to work with tensorflow 2.0. The following steps are not correct normally:
model = gensim.models.Word2Vec(...)
model.init_sims(..)
model.train(..)
model.save(..)
module = gensim.models.KeyedVectors.load_word2vec(...)
tf.saved_model.save(
module,
export_dir
)
EDIT:
This example helped me about how to do it : https://keras.io/examples/nlp/pretrained_word_embeddings/
Gensim does not use TensorFlow and it has its own methods for loading and saving models.
You would need to convert Gensim embeddings into a TensorFlow a model which only makes sense if you further plan to use your embeddings within TensorFlow and possibly fine-tune them for your task.
Gensim Word2Vec are two steps in TensorFlow:
Vocabulary lookup: a table that assigns indices to tokens.
Embedding lookup layer that picks up the actual embeddings for the indices.
Then, you can save it as any other TensorFlow model.
I have created text classification model(.pb) using tensorflow. Prediction is good.
Is it possible to check the sentence using for prediction is already used to train the model or not. I need to retrain the model when new sentence is given to model to predict.
I did some research and couldn't find a way to get the train data only with the pb file because that file only stores the features and not the actual train data(obviously),but if you have the dataset,then you can easily verify duh....
I don't think you can ever find the exact train data with only the trained model,cause the model only contains the features and not the actual train data
can we train image data-set progressively,like my previous training dataset is created using 500 images but now i want add more images in to it.
Should we train old dataset using more images ?
In Tensorflow there are checkpoints for this. You import already learned weights for an existing model and continue training on new (or existing) data. You can just add the new images to your dataset. For the repeatability of the training procedure it is useful to create a new record file. Of course you have to refer to the new record file during the training.
I want to train my own Word2Vec model for my text corpus. I can get the code from TensorFlow's tutorial. What I don't know is how to save this model to use for CNN text classification later? Should I use pickle to save it and then read it later?
No pickling is not the way of saving the model in case of tensorflow.
Tensorflow provides with tensorflow serving for saving the models as proto bufs(for exporting the model). The way to save model would be to save the tensorflow session as:
saver.save(sess, 'my_test_model',global_step=1000)
Heres the link for complete answer:
Tensorflow: how to save/restore a model?
You can use pickle to save it to disk. Then when you are creating the CNN model, load the saved word embedding table and use it to initialize the TensorFlow variable that holds the word embeddings for your CNN classifier.