def load_keras_classifier(name, path=ASSETS_PATH):
"""Load a Keras model from disk, as KerasClassifier (sklearn wrapper)"""
model_path, classes_path = keras_model_and_classes_paths(name)
nn = KerasClassifier(build_fn=do_nothing)
# load model and classes
nn.model = keras.models.load_model(model_path)
classes = pickle.load(open(classes_path, 'rb'))
# required for sklearn to believe that the model is trained
nn._estimator_type = "classifier"
nn.classes_ = classes
return nn
layers.Dense(64, activation='relu'),
layers.Dropout(0.2),
layers.Dense(64, activation='relu'),
layers.Dropout(0.2),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer=keras.optimizers.SGD(),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
return model
model1 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)
model2 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)
model3 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)
model1._estimator_type = "classifier"
model2._estimator_type = "classifier"
model3._estimator_type = "classifier"
ensemble_clf = VotingClassifier(estimators=[
('model1', model1), ('model2', model2), ('model3', model3)
], voting='soft')
# Hard Voting Classifier：根据少数服从多数来定最终结果；
# Soft Voting Classifier：将所有模型预测样本为某一类别的概率的平均值作为标准，概率最高的对应的类型为最终的预测结果；
ensemble_clf.fit(x_train, y_train)
y_pred = ensemble_clf.predict(x_test)
print('acc: ', accuracy_score(y_pred, y_test))
# 9、全部使用
from tensorflow.keras import layers
