I am working on a project involving neural machine translation (translating English to French).
I have worked through some examples online, and have now finished the model. Once a model is trained using Keras, how do I then get a prediction of a translation without training the entire model again, because with the large dataset I am using, each epoch takes some time and of course, I can't train the model every time I want a translation.
So what is the correct way of then generating predictions on new inputs without training the whole model again?
Thanks
You need to save your model the model and its weights when the fit ends using :
keras.model.save(model_name)
At any time, you can load your trained model using
model = keras.load(model_name)
then perform predictions as
y_pred = model.predict(x_test)
Hope this will be helpful
You can use the .predict() function which you can pass new inputs into it and it give you a prediction. The docs for this function are here: keras
Related
I was given a pickle file that has an xgboost model in it. I also have a model training code. I need to check if that saved model was created by this training code. What should I do?
I tried to get some predictions for a test dataset from both the saved model and the model created by the training code. But the predictions are not a 100% match. Is looking at the model parameters enough? I'm a bit stuck, couldn't find a solution. Thanks in advance.
I am new to deep learning model training and predictions, and I have a conceptual question about how to make predictions after training. I am following this tutorial on Github: https://github.com/jpark621/language-style-transfer. I have successfully completed training on my datasets, and a model was created in the models/ directory. The model contains trained weights, the loss of two autoencoders, and the adversarial loss.
My question is that now that I have these .meta, .index, and .data files, how do I make predictions with the model by providing text as input? The output would also be text (with the writing style modified by the Autoencoder model).
Any tips/resources/pointers would be helpful! Thanks so much.
I have already trained a deep learning model with some data and it performs well with the test data. Now, how do I retrain this model when I get new data?
You can Save your model using
keras.model.save(yourModel, 'fileName.hdf5')
After you got the data you can load your saved model
model = keras.model.load_model('fileName.hdf5')
model.fit()
The training will continue from last saved weights, optimizer and loss.
I'm working on a machine learning classification task in which I have trained many models with different algorithms in scikit-learn and Random Forest Classifier performed the best. Now I want to train the model further with new examples but if I train the same model by calling the fit method on new examples then it will start training the model from beginning by erasing the old parameters.
So, how can I train the trained model by training it with new examples in scikit-learn?
I got some idea by reading online to pickle and unpickle the model but how would it help I don't know.
You should use incremental learning and estimators implementing the partial_fit API.
RandomForrestClassifier has a flag warm_start. Note that this will not give the same results as if you train on both sets at once.
Append the new data to your existing dataset, and train over the whole thing. Might want to reserve some of the new data for your testset.
I have trained a cnn model using tf.estimator and tf.data.TFRecordDataset, which define a model in model_fn funcition and input in input_fn function. Also using an one-shot iterator to get one batch examples at a time.
Now I have trained model files(ckpt, meta, index) in a directory. What I want to do is predicting a image's label based on the trained model without training and evaluation again. The image can be numpy array but not possible a TFRecords file(which used when traing).
I can't find an effictive solution after trying all day. I only can get the value of weights and biases and don't know how to make my predicting image and model compatible.
FYI, my training code is here.
The similar question is Prediction from model saved with tf.estimator.Estimator in Tensorflow
, but no accepted answer and my model input is using the dataset api.
So reaaally need help. Thanks.
I have answered a similar question here.
To make predictions using a custom input, you need to use the built-in predict method of Estimators:
estimator = tf.estimator.Estimator(model_fn, ...)
predict_input_fn = ... # define this using tf.data
predict_results = estimator.predict(predict_input_fn)
for idx, prediction in enumerate(predict_results):
print(idx)
for key in prediction:
print("...{}: {}".format(key, prediction[key]))