I built a CNN model for image classification using the Keras library. However training takes many hours. Once I trained my model, how can I use it without training once more? I mean after I trained my model, I want to use it many times.
Because I will use my model in android studio.
Any help is appreciated
Thank YOU...
EDIT
When I wrote this question, I did not know the save model and load.model, in the answers you see the appropriate usage of them.
You can easily save your model after the training process by using:
model.save('my_model.h5')
you can later load that model by using:
model = load_model('my_model.h5')
for more details have a look at the documentation: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model
Related
I am coding my own models for a time but I saw huggingface and started using it. I wanted to know whether I should use the pretrained model or train model (the same hugging face model) with my own dataset. I am trying to make a question answering model.
I have dataset of 10k-20k questions.
The state-of-the-art approach is to take a pre-trained model that was pre-trained on tasks that are relevant to your problem and fine-tune the model on your dataset.
So assuming you have your dataset in English, you should take a pre-trained model on natural language English. You can then fine-tune it.
This will most likely work better than training from scratch, but you can experiment on your own. You can also load a model without the pre-trained weights in Huggingface.
I need to load a pre-trained model in Chainer framework, but as I understood, the saved (.npz) file only contains the weights and I have to reconstruct the model then load the weights into it, and there is no way to load the full model in one command like Tensorflow.
Is this true? I so, anyone with Chainer framework experience can provide some guidance? If not, what is the proper way to load a pre-trained model in the mentioned framework?
Yes, only npz files only contain weights. You need to first construct an instance of the model (a subclass of chainer.Chain), then load weights on it using load_npz. https://docs.chainer.org/en/stable/guides/serializers.html
I am a novice in using tensorflow and I built a CNN which is trained and tested both with 80-85% accuracy. I tried to save my trained model using model.save('example.h5'), and download the file using files.download('example.h5')
Afterwards I tried to load it to my flask back-end using model = tf.keras.models.load_model('example.h5').
When I tried using it with random images, it's like the model has never been trained before. Any solutions? thank you
A common reason to this might be that you are normalizing your data on training but not before predicting. Make sure you normalize the input data before you run predictions, if you have trained on normalized data.
I had a pre-trained model(tensorflow model) which was trained using data from publicly available data set. I had meta file and ckpt file. I’d like to train my tensorflow model using new data from privately obtained data set. I have small dataset, so I’d like to fine-tune my model according to ‘Strategy 2’ or ‘Strategy 3’.
Strategy 2: Train some layers and leave the others frozen.
Strategy 3: Freeze the convolutional base.
Reference site: https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751
However, I couldn’t find sample code which is implemented in a transfer learning and fine-tuning for tensorflow model. There are many examples with keras model. How can I implement in a transfer learning and fine-tuning for my tensorflow model?
If you don't have to use Tensorflow's functions, You can use example code with tf.keras module of Tensorflow 2.0 also..
I found an implementation of the DeepSpeech2 model here: https://github.com/tensorflow/models/tree/master/research/deep_speech#deepspeech2-model
This implementation has only to train and evaluate and no pre-trained models available. So I trained the model with a minimal dataset myself and tried to run inference but I didn't get meaningful results. So here my question is
Does this implementation work well if I train with a large amount of dataset?
Is there a pre-trained model available for this?
Thanks in advance