scikit-learn是Python中最好的机器学习库，而PyTorch又为咱们构建模型提供了不便的操作，是否将它们的长处整合起来呢？在本文中，咱们将介绍如何应用 scikit-learn中的网格搜寻性能来调整 PyTorch 深度学习模型的超参数:
- 如何包装 PyTorch 模型以用于 scikit-learn 以及如何应用网格搜寻
- 如何网格搜寻常见的神经网络参数，如学习率、Dropout、epochs、神经元数
- 在本人的我的项目上定义本人的超参数调优试验
如何在 scikit-learn 中应用 PyTorch 模型
要让PyTorch 模型能够在 scikit-learn 中应用的一个最简略的办法是应用skorch包。这个包为 PyTorch 模型提供与 scikit-learn 兼容的 API。 在skorch中，有分类神经网络的NeuralNetClassifier和回归神经网络的NeuralNetRegressor。
pip install skorch要应用这些包装器，必须应用 nn.Module 将 PyTorch 模型定义为类，而后在结构 NeuralNetClassifier 类时将类的名称传递给模块参数。 例如：
class MyClassifier(nn.Module): def __init__(self): super().__init__() ... def forward(self, x): ... return x # create the skorch wrapper model = NeuralNetClassifier( module=MyClassifier )NeuralNetClassifier 类的构造函数能够取得传递给 model.fit() 调用的参数（在 scikit-learn 模型中调用训练循环的办法），例如轮次数和批量大小等。 例如：
model = NeuralNetClassifier( module=MyClassifier, max_epochs=150, batch_size=10 )NeuralNetClassifier类的构造函数也能够承受新的参数，这些参数能够传递给你的模型类的构造函数，要求是必须在它后面加上module__(两个下划线)。这些新参数可能在构造函数中带有默认值，但当包装器实例化模型时，它们将被笼罩。例如:
import torch.nn as nn from skorch import NeuralNetClassifier class SonarClassifier(nn.Module): def __init__(self, n_layers=3): super().__init__() self.layers = [] self.acts = [] for i in range(n_layers): self.layers.append(nn.Linear(60, 60)) self.acts.append(nn.ReLU()) self.add_module(f"layer{i}", self.layers[-1]) self.add_module(f"act{i}", self.acts[-1]) self.output = nn.Linear(60, 1) def forward(self, x): for layer, act in zip(self.layers, self.acts): x = act(layer(x)) x = self.output(x) return x model = NeuralNetClassifier( module=SonarClassifier, max_epochs=150, batch_size=10, module__n_layers=2 )咱们能够通过初始化一个模型并打印来验证后果:
print(model.initialize()) #后果如下： <class 'skorch.classifier.NeuralNetClassifier'>[initialized]( module_=SonarClassifier( (layer0): Linear(in_features=60, out_features=60, bias=True) (act0): ReLU() (layer1): Linear(in_features=60, out_features=60, bias=True) (act1): ReLU() (output): Linear(in_features=60, out_features=1, bias=True) ), )在scikit-learn中应用网格搜寻
网格搜寻是一种模型超参数优化技术。它只是简略地穷尽超参数的所有组合，并找到给出最佳分数的组合。在scikit-learn中，GridSearchCV类提供了这种技术。在结构这个类时，必须在param_grid参数中提供一个超参数字典。这是模型参数名和要尝试的值数组的映射。
默认应用精度作为优化的分数，但其余分数能够在GridSearchCV构造函数的score参数中指定。GridSearchCV将为每个参数组合构建一个模型进行评估。并且应用默认的3倍穿插验证，这些都是能够通过参数来进行设置的。
上面是定义一个简略网格搜寻的例子:
param_grid = { 'epochs': [10,20,30] } grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3) grid_result = grid.fit(X, Y)通过将GridSearchCV构造函数中的n_jobs参数设置为 -1示意将应用机器上的所有外围。否则，网格搜寻过程将只在单线程中运行，这在多核cpu中较慢。
运行结束就能够在grid.fit()返回的后果对象中拜访网格搜寻的后果。best_score提供了在优化过程中察看到的最佳分数，best_params_形容了获得最佳后果的参数组合。
示例问题形容
咱们的示例都将在一个小型规范机器学习数据集上进行演示，该数据集是一个糖尿病发生分类数据集。这是一个小型数据集，所有的数值属性都很容易解决。
如何调优批大小和训练的轮次
在第一个简略示例中，咱们将介绍如何调优批大小和拟合网络时应用的epoch数。
咱们将简略评估从10到100的不批大小，代码清单如下所示:
import random import numpy as np import torch import torch.nn as nn import torch.optim as optim from skorch import NeuralNetClassifier from sklearn.model_selection import GridSearchCV # load the dataset, split into input (X) and output (y) variables dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8] X = torch.tensor(X, dtype=torch.float32) y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1) # PyTorch classifier class PimaClassifier(nn.Module): def __init__(self): super().__init__() self.layer = nn.Linear(8, 12) self.act = nn.ReLU() self.output = nn.Linear(12, 1) self.prob = nn.Sigmoid() def forward(self, x): x = self.act(self.layer(x)) x = self.prob(self.output(x)) return x # create model with skorch model = NeuralNetClassifier( PimaClassifier, criterion=nn.BCELoss, optimizer=optim.Adam, verbose=False ) # define the grid search parameters param_grid = { 'batch_size': [10, 20, 40, 60, 80, 100], 'max_epochs': [10, 50, 100] } 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))后果如下:
Best: 0.714844 using {'batch_size': 10, 'max_epochs': 100} 0.665365 (0.020505) with: {'batch_size': 10, 'max_epochs': 10} 0.588542 (0.168055) with: {'batch_size': 10, 'max_epochs': 50} 0.714844 (0.032369) with: {'batch_size': 10, 'max_epochs': 100} 0.671875 (0.022326) with: {'batch_size': 20, 'max_epochs': 10} 0.696615 (0.008027) with: {'batch_size': 20, 'max_epochs': 50} 0.714844 (0.019918) with: {'batch_size': 20, 'max_epochs': 100} 0.666667 (0.009744) with: {'batch_size': 40, 'max_epochs': 10} 0.687500 (0.033603) with: {'batch_size': 40, 'max_epochs': 50} 0.707031 (0.024910) with: {'batch_size': 40, 'max_epochs': 100} 0.667969 (0.014616) with: {'batch_size': 60, 'max_epochs': 10} 0.694010 (0.036966) with: {'batch_size': 60, 'max_epochs': 50} 0.694010 (0.042473) with: {'batch_size': 60, 'max_epochs': 100} 0.670573 (0.023939) with: {'batch_size': 80, 'max_epochs': 10} 0.674479 (0.020752) with: {'batch_size': 80, 'max_epochs': 50} 0.703125 (0.026107) with: {'batch_size': 80, 'max_epochs': 100} 0.680990 (0.014382) with: {'batch_size': 100, 'max_epochs': 10} 0.670573 (0.013279) with: {'batch_size': 100, 'max_epochs': 50} 0.687500 (0.017758) with: {'batch_size': 100, 'max_epochs': 100}能够看到'batch_size': 10, 'max_epochs': 100达到了约71%的精度的最佳后果。
如何调整训练优化器
上面咱们看看如何调整优化器，咱们晓得有很多个优化器能够抉择比方SDG,Adam等，那么如何抉择呢？
残缺的代码如下:
import numpy as np import torch import torch.nn as nn import torch.optim as optim from skorch import NeuralNetClassifier from sklearn.model_selection import GridSearchCV # load the dataset, split into input (X) and output (y) variables dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8] X = torch.tensor(X, dtype=torch.float32) y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1) # PyTorch classifier class PimaClassifier(nn.Module): def __init__(self): super().__init__() self.layer = nn.Linear(8, 12) self.act = nn.ReLU() self.output = nn.Linear(12, 1) self.prob = nn.Sigmoid() def forward(self, x): x = self.act(self.layer(x)) x = self.prob(self.output(x)) return x # create model with skorch model = NeuralNetClassifier( PimaClassifier, criterion=nn.BCELoss, max_epochs=100, batch_size=10, verbose=False ) # define the grid search parameters param_grid = { 'optimizer': [optim.SGD, optim.RMSprop, optim.Adagrad, optim.Adadelta, optim.Adam, optim.Adamax, optim.NAdam], } 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))输入如下:
Best: 0.721354 using {'optimizer': <class 'torch.optim.adamax.Adamax'>} 0.674479 (0.036828) with: {'optimizer': <class 'torch.optim.sgd.SGD'>} 0.700521 (0.043303) with: {'optimizer': <class 'torch.optim.rmsprop.RMSprop'>} 0.682292 (0.027126) with: {'optimizer': <class 'torch.optim.adagrad.Adagrad'>} 0.572917 (0.051560) with: {'optimizer': <class 'torch.optim.adadelta.Adadelta'>} 0.714844 (0.030758) with: {'optimizer': <class 'torch.optim.adam.Adam'>} 0.721354 (0.019225) with: {'optimizer': <class 'torch.optim.adamax.Adamax'>} 0.709635 (0.024360) with: {'optimizer': <class 'torch.optim.nadam.NAdam'>}能够看到对于咱们的模型和数据集Adamax优化算法是最佳的，准确率约为72%。
如何调整学习率
尽管pytorch外面学习率打算能够让咱们依据轮次动静调整学习率，然而作为样例，咱们将学习率和学习率的参数作为网格搜寻的一个参数来进行演示。在PyTorch中，设置学习率和动量的办法如下:
optimizer = optim.SGD(lr=0.001, momentum=0.9)在skorch包中，应用前缀optimizer__将参数路由到优化器。
import numpy as np import torch import torch.nn as nn import torch.optim as optim from skorch import NeuralNetClassifier from sklearn.model_selection import GridSearchCV # load the dataset, split into input (X) and output (y) variables dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8] X = torch.tensor(X, dtype=torch.float32) y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1) # PyTorch classifier class PimaClassifier(nn.Module): def __init__(self): super().__init__() self.layer = nn.Linear(8, 12) self.act = nn.ReLU() self.output = nn.Linear(12, 1) self.prob = nn.Sigmoid() def forward(self, x): x = self.act(self.layer(x)) x = self.prob(self.output(x)) return x # create model with skorch model = NeuralNetClassifier( PimaClassifier, criterion=nn.BCELoss, optimizer=optim.SGD, max_epochs=100, batch_size=10, verbose=False ) # define the grid search parameters param_grid = { 'optimizer__lr': [0.001, 0.01, 0.1, 0.2, 0.3], 'optimizer__momentum': [0.0, 0.2, 0.4, 0.6, 0.8, 0.9], } 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))后果如下：
Best: 0.682292 using {'optimizer__lr': 0.001, 'optimizer__momentum': 0.9} 0.648438 (0.016877) with: {'optimizer__lr': 0.001, 'optimizer__momentum': 0.0} 0.671875 (0.017758) with: {'optimizer__lr': 0.001, 'optimizer__momentum': 0.2} 0.674479 (0.022402) with: {'optimizer__lr': 0.001, 'optimizer__momentum': 0.4} 0.677083 (0.011201) with: {'optimizer__lr': 0.001, 'optimizer__momentum': 0.6} 0.679688 (0.027621) with: {'optimizer__lr': 0.001, 'optimizer__momentum': 0.8} 0.682292 (0.026557) with: {'optimizer__lr': 0.001, 'optimizer__momentum': 0.9} 0.671875 (0.019918) with: {'optimizer__lr': 0.01, 'optimizer__momentum': 0.0} 0.648438 (0.024910) with: {'optimizer__lr': 0.01, 'optimizer__momentum': 0.2} 0.546875 (0.143454) with: {'optimizer__lr': 0.01, 'optimizer__momentum': 0.4} 0.567708 (0.153668) with: {'optimizer__lr': 0.01, 'optimizer__momentum': 0.6} 0.552083 (0.141790) with: {'optimizer__lr': 0.01, 'optimizer__momentum': 0.8} 0.451823 (0.144561) with: {'optimizer__lr': 0.01, 'optimizer__momentum': 0.9} 0.348958 (0.001841) with: {'optimizer__lr': 0.1, 'optimizer__momentum': 0.0} 0.450521 (0.142719) with: {'optimizer__lr': 0.1, 'optimizer__momentum': 0.2} 0.450521 (0.142719) with: {'optimizer__lr': 0.1, 'optimizer__momentum': 0.4} 0.450521 (0.142719) with: {'optimizer__lr': 0.1, 'optimizer__momentum': 0.6} 0.348958 (0.001841) with: {'optimizer__lr': 0.1, 'optimizer__momentum': 0.8} 0.348958 (0.001841) with: {'optimizer__lr': 0.1, 'optimizer__momentum': 0.9} 0.444010 (0.136265) with: {'optimizer__lr': 0.2, 'optimizer__momentum': 0.0} 0.450521 (0.142719) with: {'optimizer__lr': 0.2, 'optimizer__momentum': 0.2} 0.348958 (0.001841) with: {'optimizer__lr': 0.2, 'optimizer__momentum': 0.4} 0.552083 (0.141790) with: {'optimizer__lr': 0.2, 'optimizer__momentum': 0.6} 0.549479 (0.142719) with: {'optimizer__lr': 0.2, 'optimizer__momentum': 0.8} 0.651042 (0.001841) with: {'optimizer__lr': 0.2, 'optimizer__momentum': 0.9} 0.552083 (0.141790) with: {'optimizer__lr': 0.3, 'optimizer__momentum': 0.0} 0.348958 (0.001841) with: {'optimizer__lr': 0.3, 'optimizer__momentum': 0.2} 0.450521 (0.142719) with: {'optimizer__lr': 0.3, 'optimizer__momentum': 0.4} 0.552083 (0.141790) with: {'optimizer__lr': 0.3, 'optimizer__momentum': 0.6} 0.450521 (0.142719) with: {'optimizer__lr': 0.3, 'optimizer__momentum': 0.8} 0.450521 (0.142719) with: {'optimizer__lr': 0.3, 'optimizer__momentum': 0.9}对于SGD，应用0.001的学习率和0.9的动量取得了最佳后果，准确率约为68%。
如何激活函数
激活函数管制单个神经元的非线性。咱们将演示评估PyTorch中可用的一些激活函数。
import numpy as np import torch import torch.nn as nn import torch.nn.init as init import torch.optim as optim from skorch import NeuralNetClassifier from sklearn.model_selection import GridSearchCV # load the dataset, split into input (X) and output (y) variables dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8] X = torch.tensor(X, dtype=torch.float32) y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1) # PyTorch classifier class PimaClassifier(nn.Module): def __init__(self, activation=nn.ReLU): super().__init__() self.layer = nn.Linear(8, 12) self.act = activation() self.output = nn.Linear(12, 1) self.prob = nn.Sigmoid() # manually init weights init.kaiming_uniform_(self.layer.weight) init.kaiming_uniform_(self.output.weight) def forward(self, x): x = self.act(self.layer(x)) x = self.prob(self.output(x)) return x # create model with skorch model = NeuralNetClassifier( PimaClassifier, criterion=nn.BCELoss, optimizer=optim.Adamax, max_epochs=100, batch_size=10, verbose=False ) # define the grid search parameters param_grid = { 'module__activation': [nn.Identity, nn.ReLU, nn.ELU, nn.ReLU6, nn.GELU, nn.Softplus, nn.Softsign, nn.Tanh, nn.Sigmoid, nn.Hardsigmoid] } 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))后果如下：
Best: 0.699219 using {'module__activation': <class 'torch.nn.modules.activation.ReLU'>} 0.687500 (0.025315) with: {'module__activation': <class 'torch.nn.modules.linear.Identity'>} 0.699219 (0.011049) with: {'module__activation': <class 'torch.nn.modules.activation.ReLU'>} 0.674479 (0.035849) with: {'module__activation': <class 'torch.nn.modules.activation.ELU'>} 0.621094 (0.063549) with: {'module__activation': <class 'torch.nn.modules.activation.ReLU6'>} 0.674479 (0.017566) with: {'module__activation': <class 'torch.nn.modules.activation.GELU'>} 0.558594 (0.149189) with: {'module__activation': <class 'torch.nn.modules.activation.Softplus'>} 0.675781 (0.014616) with: {'module__activation': <class 'torch.nn.modules.activation.Softsign'>} 0.619792 (0.018688) with: {'module__activation': <class 'torch.nn.modules.activation.Tanh'>} 0.643229 (0.019225) with: {'module__activation': <class 'torch.nn.modules.activation.Sigmoid'>} 0.636719 (0.022326) with: {'module__activation': <class 'torch.nn.modules.activation.Hardsigmoid'>}ReLU激活函数取得了最好的后果，准确率约为70%。
如何调整Dropout参数
在本例中，咱们将尝试在0.0到0.9之间的dropout百分比(1.0没有意义)和在0到5之间的MaxNorm权重束缚值。
import numpy as np import torch import torch.nn as nn import torch.nn.init as init import torch.optim as optim from skorch import NeuralNetClassifier from sklearn.model_selection import GridSearchCV # load the dataset, split into input (X) and output (y) variables dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8] X = torch.tensor(X, dtype=torch.float32) y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1) # PyTorch classifier class PimaClassifier(nn.Module): def __init__(self, dropout_rate=0.5, weight_constraint=1.0): super().__init__() self.layer = nn.Linear(8, 12) self.act = nn.ReLU() self.dropout = nn.Dropout(dropout_rate) self.output = nn.Linear(12, 1) self.prob = nn.Sigmoid() self.weight_constraint = weight_constraint # manually init weights init.kaiming_uniform_(self.layer.weight) init.kaiming_uniform_(self.output.weight) def forward(self, x): # maxnorm weight before actual forward pass with torch.no_grad(): norm = self.layer.weight.norm(2, dim=0, keepdim=True).clamp(min=self.weight_constraint / 2) desired = torch.clamp(norm, max=self.weight_constraint) self.layer.weight *= (desired / norm) # actual forward pass x = self.act(self.layer(x)) x = self.dropout(x) x = self.prob(self.output(x)) return x # create model with skorch model = NeuralNetClassifier( PimaClassifier, criterion=nn.BCELoss, optimizer=optim.Adamax, max_epochs=100, batch_size=10, verbose=False ) # define the grid search parameters param_grid = { 'module__weight_constraint': [1.0, 2.0, 3.0, 4.0, 5.0], 'module__dropout_rate': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] } 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))后果如下：
Best: 0.701823 using {'module__dropout_rate': 0.1, 'module__weight_constraint': 2.0} 0.669271 (0.015073) with: {'module__dropout_rate': 0.0, 'module__weight_constraint': 1.0} 0.692708 (0.035132) with: {'module__dropout_rate': 0.0, 'module__weight_constraint': 2.0} 0.589844 (0.170180) with: {'module__dropout_rate': 0.0, 'module__weight_constraint': 3.0} 0.561198 (0.151131) with: {'module__dropout_rate': 0.0, 'module__weight_constraint': 4.0} 0.688802 (0.021710) with: {'module__dropout_rate': 0.0, 'module__weight_constraint': 5.0} 0.697917 (0.009744) with: {'module__dropout_rate': 0.1, 'module__weight_constraint': 1.0} 0.701823 (0.016367) with: {'module__dropout_rate': 0.1, 'module__weight_constraint': 2.0} 0.694010 (0.010253) with: {'module__dropout_rate': 0.1, 'module__weight_constraint': 3.0} 0.686198 (0.025976) with: {'module__dropout_rate': 0.1, 'module__weight_constraint': 4.0} 0.679688 (0.026107) with: {'module__dropout_rate': 0.1, 'module__weight_constraint': 5.0} 0.701823 (0.029635) with: {'module__dropout_rate': 0.2, 'module__weight_constraint': 1.0} 0.682292 (0.014731) with: {'module__dropout_rate': 0.2, 'module__weight_constraint': 2.0} 0.701823 (0.009744) with: {'module__dropout_rate': 0.2, 'module__weight_constraint': 3.0} 0.701823 (0.026557) with: {'module__dropout_rate': 0.2, 'module__weight_constraint': 4.0} 0.687500 (0.015947) with: {'module__dropout_rate': 0.2, 'module__weight_constraint': 5.0} 0.686198 (0.006639) with: {'module__dropout_rate': 0.3, 'module__weight_constraint': 1.0} 0.656250 (0.006379) with: {'module__dropout_rate': 0.3, 'module__weight_constraint': 2.0} 0.565104 (0.155608) with: {'module__dropout_rate': 0.3, 'module__weight_constraint': 3.0} 0.700521 (0.028940) with: {'module__dropout_rate': 0.3, 'module__weight_constraint': 4.0} 0.669271 (0.012890) with: {'module__dropout_rate': 0.3, 'module__weight_constraint': 5.0} 0.661458 (0.018688) with: {'module__dropout_rate': 0.4, 'module__weight_constraint': 1.0} 0.669271 (0.017566) with: {'module__dropout_rate': 0.4, 'module__weight_constraint': 2.0} 0.652344 (0.006379) with: {'module__dropout_rate': 0.4, 'module__weight_constraint': 3.0} 0.680990 (0.037783) with: {'module__dropout_rate': 0.4, 'module__weight_constraint': 4.0} 0.692708 (0.042112) with: {'module__dropout_rate': 0.4, 'module__weight_constraint': 5.0} 0.666667 (0.006639) with: {'module__dropout_rate': 0.5, 'module__weight_constraint': 1.0} 0.652344 (0.011500) with: {'module__dropout_rate': 0.5, 'module__weight_constraint': 2.0} 0.662760 (0.007366) with: {'module__dropout_rate': 0.5, 'module__weight_constraint': 3.0} 0.558594 (0.146610) with: {'module__dropout_rate': 0.5, 'module__weight_constraint': 4.0} 0.552083 (0.141826) with: {'module__dropout_rate': 0.5, 'module__weight_constraint': 5.0} 0.548177 (0.141826) with: {'module__dropout_rate': 0.6, 'module__weight_constraint': 1.0} 0.653646 (0.013279) with: {'module__dropout_rate': 0.6, 'module__weight_constraint': 2.0} 0.661458 (0.008027) with: {'module__dropout_rate': 0.6, 'module__weight_constraint': 3.0} 0.553385 (0.142719) with: {'module__dropout_rate': 0.6, 'module__weight_constraint': 4.0} 0.669271 (0.035132) with: {'module__dropout_rate': 0.6, 'module__weight_constraint': 5.0} 0.662760 (0.015733) with: {'module__dropout_rate': 0.7, 'module__weight_constraint': 1.0} 0.636719 (0.024910) with: {'module__dropout_rate': 0.7, 'module__weight_constraint': 2.0} 0.550781 (0.146818) with: {'module__dropout_rate': 0.7, 'module__weight_constraint': 3.0} 0.537760 (0.140094) with: {'module__dropout_rate': 0.7, 'module__weight_constraint': 4.0} 0.542969 (0.138144) with: {'module__dropout_rate': 0.7, 'module__weight_constraint': 5.0} 0.565104 (0.148654) with: {'module__dropout_rate': 0.8, 'module__weight_constraint': 1.0} 0.657552 (0.008027) with: {'module__dropout_rate': 0.8, 'module__weight_constraint': 2.0} 0.428385 (0.111418) with: {'module__dropout_rate': 0.8, 'module__weight_constraint': 3.0} 0.549479 (0.142719) with: {'module__dropout_rate': 0.8, 'module__weight_constraint': 4.0} 0.648438 (0.005524) with: {'module__dropout_rate': 0.8, 'module__weight_constraint': 5.0} 0.540365 (0.136861) with: {'module__dropout_rate': 0.9, 'module__weight_constraint': 1.0} 0.605469 (0.053083) with: {'module__dropout_rate': 0.9, 'module__weight_constraint': 2.0} 0.553385 (0.139948) with: {'module__dropout_rate': 0.9, 'module__weight_constraint': 3.0} 0.549479 (0.142719) with: {'module__dropout_rate': 0.9, 'module__weight_constraint': 4.0} 0.595052 (0.075566) with: {'module__dropout_rate': 0.9, 'module__weight_constraint': 5.0}能够看到，10%的Dropout和2.0的权重束缚取得了70%的最佳精度。
如何调整暗藏层神经元的数量
单层神经元的数量是一个须要调优的重要参数。一般来说，一层神经元的数量管制着网络的示意能力，至多在拓扑的这一点上是这样。
实践上来说：因为通用迫近定理，一个足够大的单层网络能够近似任何其余神经网络。
在本例中，将尝试从1到30的值，步骤为5。一个更大的网络须要更多的训练，至多批大小和epoch的数量应该与神经元的数量一起优化。
import numpy as np import torch import torch.nn as nn import torch.nn.init as init import torch.optim as optim from skorch import NeuralNetClassifier from sklearn.model_selection import GridSearchCV # load the dataset, split into input (X) and output (y) variables dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8] X = torch.tensor(X, dtype=torch.float32) y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1) class PimaClassifier(nn.Module): def __init__(self, n_neurons=12): super().__init__() self.layer = nn.Linear(8, n_neurons) self.act = nn.ReLU() self.dropout = nn.Dropout(0.1) self.output = nn.Linear(n_neurons, 1) self.prob = nn.Sigmoid() self.weight_constraint = 2.0 # manually init weights init.kaiming_uniform_(self.layer.weight) init.kaiming_uniform_(self.output.weight) def forward(self, x): # maxnorm weight before actual forward pass with torch.no_grad(): norm = self.layer.weight.norm(2, dim=0, keepdim=True).clamp(min=self.weight_constraint / 2) desired = torch.clamp(norm, max=self.weight_constraint) self.layer.weight *= (desired / norm) # actual forward pass x = self.act(self.layer(x)) x = self.dropout(x) x = self.prob(self.output(x)) return x # create model with skorch model = NeuralNetClassifier( PimaClassifier, criterion=nn.BCELoss, optimizer=optim.Adamax, max_epochs=100, batch_size=10, verbose=False ) # define the grid search parameters param_grid = { 'module__n_neurons': [1, 5, 10, 15, 20, 25, 30] } 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))后果如下：
Best: 0.708333 using {'module__n_neurons': 30} 0.654948 (0.003683) with: {'module__n_neurons': 1} 0.666667 (0.023073) with: {'module__n_neurons': 5} 0.694010 (0.014382) with: {'module__n_neurons': 10} 0.682292 (0.014382) with: {'module__n_neurons': 15} 0.707031 (0.028705) with: {'module__n_neurons': 20} 0.703125 (0.030758) with: {'module__n_neurons': 25} 0.708333 (0.015733) with: {'module__n_neurons': 30}你能够看到，在暗藏层中有30个神经元的网络取得了最好的后果，准确率约为71%。
总结
在这篇文章中，咱们介绍了如何应用PyTorch和scikit-learn在Python中优化深度学习网络的超参数。如果你对skorch 感兴趣，能够看看他的文档
https://avoid.overfit.cn/post/fda8764b85174b6ca3c9eac4fc6d0db9
作者：Jason Brownlee