optuna.visualization.plot_intermediate_values(study) 所有不同的颜色显示每个试验的损失曲线。 切片图 代码语言:javascript 代码运行次数:0 运行 AI代码解释 optuna.visualization.plot_slice(study) 等高线图 以目标值作为轮廓绘制参数对。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 optuna.visualization...
optimize(objective, n_trials=100, timeout=600) plot_optimization_history(study).show() 八,各种调参可视化函数范例 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import optuna from optuna.visualization import plot_contour from optuna.visualization import plot_intermediate_values from optuna....
intermediate_values={}, distributions={'learning_rate': FloatDistribution(high=0.01, log=True, low=0.001, step=None), 'optimizer': CategoricalDistribution(choices=('Adam', 'RMSprop', 'SGD')), 'weight_decay': FloatDistribution(high=0.001, log=True, low=0.0001, step=None), 'n_layers'...
你不用自己处理这些重要性数据,Optuna 已经提供了同optuna.importance.get_param_importances具有相同接口的函数optuna.visualization.plot_param_importances。它将返回一个 Plotly 图表,这对于可视化分析很有帮助。 fig = optuna.visualization.plot_param_importances(study) fig.show() 下面是一幅使用 PyTorch 编写的...
plot_param_importances(study,target=lambdat:t.values[0],target_name="Flops") 超参数对Acc的影响 plot_param_importances(study,target=lambdat:t.values[1],target_name="Acc") 3. References [1]https://optuna.org/ [2]https://github.com/optuna/optuna...
optuna.visualization.plot_param_importances(study) optuna.visualization.plot_slice(study) optuna.visualization.plot_parallel_coordinate(study) 以上就是使用optuna调优Pytorch模型的全部过程,本文的源代码在这里,并且有完整的运行结果,如果你想自己调整或者学习,请看这个连接:https://www.kaggle.com/code/averma111...
visualization.plot_optimization_history(studies).show() In the above sample code, we compare and display the performance of the four kinds of samplers using a two-dimensional Sphere function, which is part of a group of benchmark functions widely used in the black-box optimization research ...
optuna.visualization.plot_slice(study) optuna.visualization.plot_parallel_coordinate(study) 以上就是使用optuna调优Pytorch模型的全部过程,本文的源代码在这里,并且有完整的运行结果,如果你想自己调整或者学习,请看这个连接 https://www.kaggle.com/code/averma111/pytorch-ps3e15-optuna ...
# This allows users to modify the generated figure for their demand by using API of the visualization library. # The following example replaces figure titles drawn by Plotly-based :func:`~optuna.visualization.plot_intermediate_values` manually. fig = plot_intermediate_values(study) fig.update_lay...
The plot shows us the importance of each hyperparameter on improving the score of our objective function. As an example in the visualization above, it turns out that the number of units in the first layer is the most important hyperparameter, while the number of layers is the less important...