“Sequential”对象没有属性“loss”-当我使用GridSearchCV调优Keras模型时

Question

下面是我的代码：

    from tensorflow.keras.models import Sequential, Model
    from tensorflow.keras.layers import Input, Dense, Activation, Dropout, BatchNormalization
    from tensorflow.keras.datasets import mnist
    from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
    from sklearn.model_selection import GridSearchCV

    ...
    ...
    ...


    def get_model(dropout_rate=0.2, hidden_units=512):
        model = Sequential()
        model.add(Dropout(dropout_rate, input_shape=(28*28,)))
        model.add(Dense(hidden_units, activation='relu'))
        model.add(BatchNormalization())
        model.add(Dropout(dropout_rate))
        model.add(Dense(hidden_units, activation='relu'))
        model.add(BatchNormalization())
        model.add(Dropout(dropout_rate))
        model.add(Dense(hidden_units, activation='relu'))
        model.add(BatchNormalization())
        model.add(Dropout(dropout_rate))
        model.add(Dense(10, activation='softmax'))
        return model

    model = KerasClassifier(build_fn=get_model, batch_size=128, epochs=10)
    para_dict = {'dropout_rate':[0.2,0.5,0.8], 'hidden_units':[128,256,512,1024]}
    clf = GridSearchCV(model, para_dict, cv=5, scoring='accuracy')
    clf.fit(x_train, y_train)

谢谢你！

Answer 1

上面的build_model函数没有为训练配置模型。您已经添加了loss和其他参数。

您可以使用keras顺序方法compile来编译模型。https://keras.io/models/sequential/

所以您的build_model函数应该是：

loss = 'binary_crossentropy' #https://keras.io/optimizers
optimizer = 'adam'           #https://keras.io/losses
metrics = ['accuracy']
def get_model(dropout_rate=0.2, hidden_units=512):
    model = Sequential()
    model.add(Dropout(dropout_rate, input_shape=(28*28,)))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(10, activation='softmax'))
    model.compile(optimizer = optimizer, loss = loss, metrics = metrics)
    return model