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.
Related
I am training a model using tf.keras and I have many small .npy files with single observations in a folder on local disk. I have build a DataGeneretor(keras.utils.Sequence) class and it works correctly, although I have a warning:
'tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.'
I have found out that I can simply create something like this:
ds = tf.data.Dataset.from_generator(
DataGenerator, args=[...],
output_types=(tf.float16, tf.uint8),
output_shapes=([None,256,256,3], [None,256,256,1]),
)
and then my Keras DataGenerator would work as a single file reader and a TF Dataset as interface to create batches. My question is: does it make any sense? Would it be safer? Would it read next batch during the training of previous batch, when using simple model.fit?
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
I have trained a seq2seq model with 1M samples and saved the latest checkpoint. Now, I have some additional training data of 50K sentence pairs which has not been seen in previous training data. How can I adapt the current model to this new data without starting the training from scratch?
You do not have to re-run the whole network initialization. You may run an incremental training.
Training from pre-trained parameters
Another use case it to use a base model and train it further with new training options (in particular the optimization method and the learning rate). Using -train_from without -continue will start a new training with parameters initialized from a pre-trained model.
Remember to tokenize your 50K corpus the same way you tokenized the previous one.
Also, you do not have to use the same vocabulary beginning with OpenNMT 0.9. See the Updating the vocabularies section and use the appropriate value with -update_vocab option.
I use TF-slim training flower data set, scripts is this. the flower data set has only 5 classes. If I add some new image data to the roses, or add a new classification, what should I do after the train 1000 steps? Do I need to delete already trained data, such as checkpoint files?
There exists a similar question on Data Science Stack Exchange, with an answer that considers your scenario:
Once a model is trained and you get new data which can be used for
training, you can load the previous model and train onto it. For
example, you can save your model as a .pickle file and load it and
train further onto it when new data is available. Do note that for the
model to predict correctly, the new training data should have a
similar distribution as the past data
I do the same in my own project, where I started with a small dataset that grew bigger over the time. After addding new data I retrain the model from the last checkpoint.
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 ?