How to save final model using keras

Question

I use KerasClassifier to train the classifier   The code is below   import numpy from pandas import read csv from keras models import Sequential from keras layers import Dense from keras wrappers scikit learn import KerasClassifier from keras utils import np utils from sklearn model selection import cross val score from sklearn model selection import KFold from sklearn preprocessing import LabelEncoder from sklearn pipeline import Pipeline   fix random seed for reproducibility seed   7 numpy random seed seed    load dataset dataframe   read csv  iris csv   header None  dataset   dataframe values X   dataset   0 4  astype float  Y   dataset   4    encode class values as integers encoder   LabelEncoder   encoder fit Y  encoded Y   encoder transform Y   print  encoded Y    print encoded Y    convert integers to dummy variables  i e  one hot encoded  dummy y   np utils to categorical encoded Y   print  dummy y    print dummy y    define baseline model def baseline model          create model     model   Sequential       model add Dense 4  input dim 4  init  normal   activation  relu         model add Dense 4  init  normal   activation  relu        model add Dense 3  init  normal   activation  softmax          Compile model     model compile loss  categorical crossentropy   optimizer  adam   metrics   accuracy         return model  estimator   KerasClassifier build fn baseline model  nb epoch 200  batch size 5  verbose 0   global model   baseline model   kfold   KFold n splits 10  shuffle True  random state seed  results   cross val score estimator  X  dummy y  cv kfold  print  Accuracy    2f      2f        results mean   100  results std   100     But How to save the final model for future prediction   I usually use below code to save model     serialize model to JSON model json   model to json   with open  model json    w   as json file      json file write model json    serialize weights to HDF5 model save weights  model h5   print  Saved model to disk     But I don t know how to insert the saving model s code into KerasClassifier s code   Thank you

User · Answer

Generally  we save the model and weights in the same file by calling the save   function  For saving  model compile optimizer  adam                 loss    categorical crossentropy                 metrics     quot accuracy quot     model fit X train  Y train           batch size   32           epochs  10           verbose   2            validation data  X test  Y test     here I have use filename as  quot my model quot   you can choose whatever you want to   model save  quot my model h5 quot    using h5 extension print  quot model saved    quot    For Loading the model  from keras models import load model  model   load model  my model h5   model summary    In this case  we can simply save and load the model without re-compiling our model again  Note - This is the preferred way for saving and loading your Keras model

User · Answer

Saving a Keras model  model          Get model  Sequential  Functional Model  or Model subclass  model save  path to location    Loading the model back  from tensorflow import keras model   keras models load model  path to location    For more information  read Documentation

User · Answer

You can save the best model using keras callbacks ModelCheckpoint   Example  model compile loss  categorical crossentropy   optimizer  adam   metrics   accuracy    model checkpoint callback   keras callbacks ModelCheckpoint  quot best Model h5 quot  save best only True  history   model fit x train y train            epochs 10            validation data  x valid y valid             callbacks  model checkpoint callback    This will save the best model in your working directory

User · Answer

The model has a save method  which saves all the details necessary to reconstitute the model  An example from the keras documentation   from keras models import load model  model save  my model h5      creates a HDF5 file  my model h5  del model    deletes the existing model    returns a compiled model   identical to the previous one model   load model  my model h5

User · Answer

you can save the model and load in this way   from keras models import Sequential  load model from keras contrib losses import import crf loss from keras contrib metrics import crf viterbi accuracy    To save model model save  my model 01 hdf5      To load the model custom objects   CRF   CRF  crf loss  crf loss  crf viterbi accuracy  crf viterbi accuracy     To load a persisted model that uses the CRF layer  model1   load model   home abc my model 01 hdf5   custom objects   custom objects

User · Answer

You can use model save filepath  to save a Keras model into a single HDF5 file which will contain    the architecture of the model  allowing to re-create the model  the weights of the model  the training configuration  loss  optimizer  the state of the optimizer  allowing to resume training exactly where you left off    In your Python code probable the last line should be   model save  m hdf5     This allows you to save the entirety of the state of a model in a single file  Saved models can be reinstantiated via keras models load model     The model returned by load model   is a compiled model ready to be used  unless the saved model was never compiled in the first place    model save   arguments    filepath  String  path to the file to save the weights to  overwrite  Whether to silently overwrite any existing file at the target location  or provide the user with a manual prompt  include optimizer  If True  save optimizer s state together

User · Answer

you can save the model in json and weights in a hdf5 file format      keras library import  for Saving and loading model and weights  from keras models import model from json from keras models import load model    serialize model to JSON    the keras model which is trained is defined as  model  in this example model json   model to json     with open  model num json    w   as json file      json file write model json     serialize weights to HDF5 model save weights  model num h5     files  model num h5  and  model num json  are created which contain our model and weights   To use the same trained model for further testing you can simply load the hdf5 file and use it for the prediction of different data   here s how to load the model from saved files     load json and create model json file   open  model num json    r    loaded model json   json file read   json file close   loaded model   model from json loaded model json     load weights into new model loaded model load weights  model num h5   print  Loaded model from disk    loaded model save  model num hdf5   loaded model load model  model num hdf5     To predict for different data you can use this  loaded model predict classes  your test data here

[python] How to save final model using keras?

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