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我正在使用一个基本的 CNN 模型对我的数据进行分类。我的输入数据的维度是 (325, 20, 244,244)。我使用的代码如下：

model = Sequential()
model.add(Dense(2, activation='relu', input_shape=X_train.shape[1:]))
model.add(Dense(2, activation='sigmoid'))

optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
epochs = [10, 50, 100]
param_grid = dict(epochs=epochs, optimizer=optimizer)

model.compile(loss='binary_crossentropy', metrics=['accuracy'])
grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy', n_jobs=-1, refit='boolean')
grid_result = grid.fit(X_train, Y_train, validation_data=(X_test, Y_test))
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

我得到的输出是：

grid_result = grid.fit(X_train, Y_train, validation_data=(X_test, Y_test))

Traceback (most recent call last):

  File "<ipython-input-16-bb553189f3ee>", line 1, in <module>
    grid_result = grid.fit(X_train, Y_train, validation_data=(X_test, Y_test))

  File "C:\Users\Student\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py", line 633, in fit
    base_estimator = clone(self.estimator)

  File "C:\Users\Student\Anaconda3\lib\site-packages\sklearn\base.py", line 60, in clone
    % (repr(estimator), type(estimator)))

TypeError: Cannot clone object '<tensorflow.python.keras.engine.sequential.Sequential object at 0x0000025993610B08>' (type <class 'tensorflow.python.keras.engine.sequential.Sequential'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.

谁能告诉我代码有什么问题以及如何纠正它。

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1 回答 1

5

在此链接中：用于 sklearn 的 Tensorflow Keras 包装器和 Keras包装器

您可以看到 tensorflow keras 有一个包装器，用于将 keras 模型与 sklearn 一起使用。

因此，您必须使用KerasClassifier(build_fn=None, **sk_params)where build_fn 应该是您对模型进行编码的函数，并且该函数采用您想要调整的参数。

所以你应该像这样编码你的模型：

def getModel(optimizer):
    model = Sequential()
    model.add(Dense(2, activation='relu', input_shape=X_train.shape[1:]))
    model.add(Dense(2, activation='sigmoid'))
    model.compile(optimizer=optimizer , loss = tf.losses.categorical_crossentropy , metrics=['accuracy'])
    return model


optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
epochs = [10, 50, 100]

param_grid = dict(epochs=epochs, optimizer=optimizer)

Kmodel = KerasClassifier(build_fn=getModel, verbose=1)
grid = GridSearchCV(estimator=Kmodel, param_grid=param_grid, scoring='accuracy', n_jobs=-1, refit='boolean')
grid_result = grid.fit(X_train, Y_train)

有关 mnist 上 KerasClassifier 的编码示例，您可以访问github

于 2020-02-22T14:14:19.373 回答