在神经网络中的时代网格搜索：每个参数运行3次(Grid search on epochs in neural network: each parameter being run 3 times)

在神经网络中的时代网格搜索：每个参数运行3次(Grid search on epochs in neural network: each parameter being run 3 times)

在进行网格搜索时，我的顺序密集DNN似乎在我的参数网格中贯穿每个参数三次。 我期望它在网格中的每个指定epcohs运行一次：10次，50次和100次。为什么会发生这种情况？

模型架构：

def build_model(): print('building DNN architecture') model = Sequential() model.add(Dropout(0.02, input_shape = (150,))) model.add(Dense(8, init = 'normal', activation = 'relu')) model.add(Dropout(0.02)) model.add(Dense(16, init = 'normal', activation = 'relu')) model.add(Dense(1, init = 'normal')) model.compile(loss = 'mean_squared_error', optimizer = 'adam') print('model succesfully compiled') return model

网格搜索时代：

from sklearn.model_selection import GridSearchCV epochs = [10,50,100] param_grid = dict(epochs = epochs) grid = GridSearchCV(estimator = KerasRegressor(build_fn = build_model), param_grid = param_grid) grid_result = grid.fit(x_train, y_train) grid_result.best_params_

My sequential dense DNN seems to run through each parameter in my parameter grid three times while doing Grid Search. I expect it to run once per specified epcohs in the grid: 10, 50 and 100. Why does this happen?

model architecture:

def build_model(): print('building DNN architecture') model = Sequential() model.add(Dropout(0.02, input_shape = (150,))) model.add(Dense(8, init = 'normal', activation = 'relu')) model.add(Dropout(0.02)) model.add(Dense(16, init = 'normal', activation = 'relu')) model.add(Dense(1, init = 'normal')) model.compile(loss = 'mean_squared_error', optimizer = 'adam') print('model succesfully compiled') return model

Grid search on epochs:

from sklearn.model_selection import GridSearchCV epochs = [10,50,100] param_grid = dict(epochs = epochs) grid = GridSearchCV(estimator = KerasRegressor(build_fn = build_model), param_grid = param_grid) grid_result = grid.fit(x_train, y_train) grid_result.best_params_