sklearn.model_selection.ParameterSampler 使用给定分布生成参数，进行参数搜索

我会带着你远行 2021-06-24 15:59 182阅读 0赞

from sklearn.model_selection import ParameterSampler
    #定义分布：
    class UniformDistribution(object):
        def __init__(self, low, high):
            assert low <= high
            self.low = low
            self.high = high
    
        def rvs(self, size=None, random_state=None):
            uniform = random_state.uniform if random_state else np.random.uniform
            return uniform(self.low, self.high, size)
    
    
    class LogUniformDistribution(object):
        def __init__(self, low, high):
            assert low <= high
            self.low = low
            self.high = high
    
        def rvs(self, size=None, random_state=None):
            uniform = random_state.uniform if random_state else np.random.uniform
            return np.exp(uniform(np.log(self.low), np.log(self.high), size))

# 生成参数并训练
    random_search_mode = True
    random_state = None
    num_runs = 10000
    # 是否搜索
    if random_search_mode:
        #参数分布
        param_distributions = dict(
            learning_rate=LogUniformDistribution(low=5e-5, high=2e-4),
            alpha=UniformDistribution(low=4, high=15),
            l2_weight_decay=LogUniformDistribution(low=1e-6, high=5e-2),
    # optimizer=['RMSProp', 'Adam'], # 'RMSPeop' or 'Adam'
    # out_dim=[128, 256, 512],
        )
        # 静态参数
        static_params = dict(
            num_epochs=15,
            crop_size=224,
        )
    
        # 从分布生成参数
        sampler = ParameterSampler(param_distributions, num_runs, random_state)
    
        # 遍历参数，训练
        for random_params in sampler:
            params = {}
            params.update(random_params)
            params.update(static_params)
    
            stop = train(__file__, lossfun_one_batch, params,
                         save_distance_matrix)
            if stop:
                break