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.
This question is related to
python
machine-learning
keras
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("best_Model.h5",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.
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")
You can use model.save(filepath) to save a Keras model into a single HDF5 file which will contain:
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:
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)
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 = ["accuracy"])
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 "my_model", you can choose whatever you want to.
model.save("my_model.h5") #using h5 extension
print("model saved!!!")
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.
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
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')
Source: Stackoverflow.com