本文介绍了如何将Scikit-Learn-Keras模型保存到持久性文件中(pickle/hd5/json/yaml)的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有以下代码，使用 Keras Scikit学习包装器:
I have the following code, using Keras Scikit-Learn Wrapper:
from keras.models import Sequential
from sklearn import datasets
from keras.layers import Dense
from sklearn.model_selection import train_test_split
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
import pickle
import numpy as np
import json
def classifier(X, y):
"""
Description of classifier
"""
NOF_ROW, NOF_COL = X.shape
def create_model():
# create model
model = Sequential()
model.add(Dense(12, input_dim=NOF_COL, init='uniform', activation='relu'))
model.add(Dense(6, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# evaluate using 10-fold cross validation
seed = 7
np.random.seed(seed)
model = KerasClassifier(build_fn=create_model, nb_epoch=150, batch_size=10, verbose=0)
return model
def main():
"""
Description of main
"""
iris = datasets.load_iris()
X, y = iris.data, iris.target
X = preprocessing.scale(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
model_tt = classifier(X_train, y_train)
model_tt.fit(X_train,y_train)
#--------------------------------------------------
# This fail
#--------------------------------------------------
filename = 'finalized_model.sav'
pickle.dump(model_tt, open(filename, 'wb'))
# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(X_test, Y_test)
print(result)
#--------------------------------------------------
# This also fail
#--------------------------------------------------
# from keras.models import load_model
# model_tt.save('test_model.h5')
#--------------------------------------------------
# This works OK
#--------------------------------------------------
# print model_tt.score(X_test, y_test)
# print model_tt.predict_proba(X_test)
# print model_tt.predict(X_test)
# Output of predict_proba
# 2nd column is the probability that the prediction is 1
# this value is used as final score, which can be used
# with other method as comparison
# [ [ 0.25311464 0.74688536]
# [ 0.84401423 0.15598579]
# [ 0.96047372 0.03952631]
# ...,
# [ 0.25518912 0.74481088]
# [ 0.91467732 0.08532269]
# [ 0.25473493 0.74526507]]
# Output of predict
# [[1]
# [0]
# [0]
# ...,
# [1]
# [0]
# [1]]
if __name__ == '__main__':
main()
如代码中所述，它在此行失败:
As stated in the code there it fails at this line:
pickle.dump(model_tt, open(filename, 'wb'))
出现此错误:
pickle.PicklingError: Can't pickle <function create_model at 0x101c09320>: it's not found as __main__.create_model
我该如何解决?
推荐答案
编辑1 :有关保存模型的原始答案
Edit 1 : Original answer about saving model
使用HDF5:
# saving model
json_model = model_tt.model.to_json()
open('model_architecture.json', 'w').write(json_model)
# saving weights
model_tt.model.save_weights('model_weights.h5', overwrite=True)
# loading model
from keras.models import model_from_json
model = model_from_json(open('model_architecture.json').read())
model.load_weights('model_weights.h5')
# dont forget to compile your model
model.compile(loss='binary_crossentropy', optimizer='adam')
编辑2 :具有虹膜数据集的完整代码示例
Edit 2 : full code example with iris dataset
# Train model and make predictions
import numpy
import pandas
from keras.models import Sequential, model_from_json
from keras.layers import Dense
from keras.utils import np_utils
from sklearn import datasets
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
iris = datasets.load_iris()
X, Y, labels = iris.data, iris.target, iris.target_names
X = preprocessing.scale(X)
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
y = np_utils.to_categorical(encoded_Y)
def build_model():
# create model
model = Sequential()
model.add(Dense(4, input_dim=4, init='normal', activation='relu'))
model.add(Dense(3, init='normal', activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def save_model(model):
# saving model
json_model = model.to_json()
open('model_architecture.json', 'w').write(json_model)
# saving weights
model.save_weights('model_weights.h5', overwrite=True)
def load_model():
# loading model
model = model_from_json(open('model_architecture.json').read())
model.load_weights('model_weights.h5')
model.compile(loss='categorical_crossentropy', optimizer='adam')
return model
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.3, random_state=seed)
# build
model = build_model()
model.fit(X_train, Y_train, nb_epoch=200, batch_size=5, verbose=0)
# save
save_model(model)
# load
model = load_model()
# predictions
predictions = model.predict_classes(X_test, verbose=0)
print(predictions)
# reverse encoding
for pred in predictions:
print(labels[pred])
请注意，我仅使用Keras，而不使用包装器.它只会在一些简单的事情上增加一些复杂性.另外，代码不是自愿的，因此您可以了解整个情况.
Please note that I used Keras only, not the wrapper. It only add some complexity in something simple. Also code is volontary not factored so you can have the whole picture.
您还说过要输出1或0.在此数据集中这是不可能的，因为您有3个输出暗度和类(虹膜setosa，虹膜多色，虹膜virginica).如果只有2个类，则使用Sigmoid输出功能，输出的dim和class将为0或1.
Also, you said you want to output 1 or 0. It is not possible in this dataset because you have 3 output dims and classes (Iris-setosa, Iris-versicolor, Iris-virginica). If you had only 2 classes then your output dim and classes would be 0 or 1 using sigmoid output fonction.
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