本文整理汇总了Python中sklearn.neural_network.MLPRegressor.out_activation_方法的典型用法代码示例。如果您正苦于以下问题：Python MLPRegressor.out_activation_方法的具体用法？Python MLPRegressor.out_activation_怎么用？Python MLPRegressor.out_activation_使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neural_network.MLPRegressor的用法示例。
在下文中一共展示了MLPRegressor.out_activation_方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _create_new_nn
# 需要导入模块: from sklearn.neural_network import MLPRegressor [as 别名]
# 或者: from sklearn.neural_network.MLPRegressor import out_activation_ [as 别名]
def _create_new_nn(self, weights, biases):
mlp = MLPRegressor(hidden_layer_sizes = self._nn_architecture, alpha=10**-10, max_iter=1)
mlp.fit([np.random.randn(self._n_features)], [np.random.randn(self._n_actions)])
mlp.coefs_ = weights
mlp.intercepts_ = biases
mlp.out_activation_ = 'softmax'
return mlp示例2: _create_first_population
# 需要导入模块: from sklearn.neural_network import MLPRegressor [as 别名]
# 或者: from sklearn.neural_network.MLPRegressor import out_activation_ [as 别名]
def _create_first_population(self):
self._current_population = []
for _ in range(self._n_individuals):
mlp = MLPRegressor(hidden_layer_sizes = self._nn_architecture, alpha=10**-10, max_iter=1)
mlp.fit([np.random.randn(self._n_features)], [np.random.randn(self._n_actions)])
mlp.out_activation_ = 'softmax'
self._current_population.append([mlp,0])