我刚开始学习Python编程，如果有人能简要解释/提示一下如何将原始代码转换为函数，我将不胜感激。
示例机器学习代码：
# create modelmodel = Sequential()model.add(Dense(neurons, input_dim=8, kernel_initializer='uniform', activation='linear', kernel_constraint=maxnorm(4)))model.add(Dropout(0.2))model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))# Compile modelmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # split into input (X) and output (Y) variablesX = dataset[:,0:8]Y = dataset[:,8]# create modelmodel = KerasClassifier(build_fn=model, epochs=100, batch_size=10, verbose=0)# define the grid search parametersneurons = [1, 5]param_grid = dict(neurons=neurons)grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)grid_result = grid.fit(X, Y)# summarize resultsprint("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))means = grid_result.cv_results_['mean_test_score']stds = grid_result.cv_results_['std_test_score']params = grid_result.cv_results_['params']for mean, stdev, param in zip(means, stds, params): print("%f (%f) with: %r" % (mean, stdev, param))如果我想将这个示例转换成1到2个函数，我应该如何开始？
编辑：
在上面的代码中，我已经为< # create model > 创建了一个函数：
def create_model(neurons=1): # create model model = Sequential() model.add(Dense(neurons, input_dim=8, kernel_initializer='uniform', activation='linear', kernel_constraint=maxnorm(4))) model.add(Dropout(0.2)) model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])return model然后，我需要将 create_model() 传递给 < KerasClassifier(build_fn=create_model etc…) >
如果我像下面这样创建另一个函数，是否正确：
def keras_classifier(model): # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] model = KerasClassifier(build_fn=model, epochs=100, batch_size=10, verbose=0) # define the grid search parameters neurons = [1, 5] param_grid = dict(neurons=neurons) grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3) grid_result = grid.fit(X, Y) # summarize results print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_)) means = grid_result.cv_results_['mean_test_score'] stds = grid_result.cv_results_['std_test_score'] params = grid_result.cv_results_['params'] for mean, stdev, param in zip(means, stds, params): print("%f (%f) with: %r" % (mean, stdev, param))这样做是否正确/可以作为另一个函数中调用的函数？
因为如果我调用这两个函数：
create_model(neurons)keras_classifier(model)我会得到错误 NameError: name ‘model’ is not defined
请问有人能帮我吗？
回答：
我认为你的函数定义存在问题：
def create_model(neurons): ....return model需要改成
def create_model(neurons): .... return model在Python中，缩进非常重要，它们是语法的一部分。请不要编写难看的代码，谢谢 🙂
是的，你可以将模型传递给一个函数，然后该函数将其传递给keras分类器的build_fn=命名变量。你传递给分类器调用的东西本身必须是一个模型对象，所以这样做：
model = KerasClassifier(build_fn=create_model(), epochs=100, batch_size=10, verbose=0)为你的函数创建的模型使用不同的名称，或者在传递给函数时使用不同的名称，可以帮助你跟踪它们。
