2020/11/02 10:11

# 过拟合问题实战

## 1.构建数据集

import matplotlib.pyplot as plt
# 导入数据集生成工具
import numpy as np
import seaborn as sns
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers, Sequential, regularizers
from mpl_toolkits.mplot3d import Axes3D


def load_dataset():
# 采样点数
N_SAMPLES = 1000
# 测试数量比率
TEST_SIZE = None

# 从 moon 分布中随机采样 1000 个点，并切分为训练集-测试集
X, y = make_moons(n_samples=N_SAMPLES, noise=0.25, random_state=100)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=42)
return X, y, X_train, X_test, y_train, y_test



make_plot 函数可以方便地根据样本的坐标 X 和样本的标签 y 绘制出数据的分布图：

def make_plot(X, y, plot_name, file_name, XX=None, YY=None, preds=None, dark=False, output_dir=OUTPUT_DIR):
# 绘制数据集的分布， X 为 2D 坐标， y 为数据点的标签
if dark:
plt.style.use('dark_background')
else:
sns.set_style("whitegrid")
axes = plt.gca()
axes.set_xlim([-2, 3])
axes.set_ylim([-1.5, 2])
axes.set(xlabel="$x_1$", ylabel="$x_2$")
plt.title(plot_name, fontsize=20, fontproperties='SimHei')
if XX is not None and YY is not None and preds is not None:
plt.contourf(XX, YY, preds.reshape(XX.shape), 25, alpha=0.08, cmap=plt.cm.Spectral)
plt.contour(XX, YY, preds.reshape(XX.shape), levels=[.5], cmap="Greys", vmin=0, vmax=.6)
# 绘制散点图，根据标签区分颜色m=markers
markers = ['o' if i == 1 else 's' for i in y.ravel()]
mscatter(X[:, 0], X[:, 1], c=y.ravel(), s=20, cmap=plt.cm.Spectral, edgecolors='none', m=markers, ax=axes)
# 保存矢量图
plt.savefig(output_dir + '/' + file_name)
plt.close()

def mscatter(x, y, ax=None, m=None, **kw):
import matplotlib.markers as mmarkers
if not ax: ax = plt.gca()
sc = ax.scatter(x, y, **kw)
if (m is not None) and (len(m) == len(x)):
paths = []
for marker in m:
if isinstance(marker, mmarkers.MarkerStyle):
marker_obj = marker
else:
marker_obj = mmarkers.MarkerStyle(marker)
path = marker_obj.get_path().transformed(
marker_obj.get_transform())
paths.append(path)
sc.set_paths(paths)
return sc

X, y, X_train, X_test, y_train, y_test = load_dataset()
make_plot(X,y,"haha",'月牙形状二分类数据集分布.svg')


## 2.网络层数的影响

def network_layers_influence(X_train, y_train):
# 构建 5 种不同层数的网络
for n in range(5):
# 创建容器
model = Sequential()
# 创建第一层
# 添加 n 层，共 n+2 层
for _ in range(n):
# 创建最末层
# 模型装配与训练
model.fit(X_train, y_train, epochs=N_EPOCHS, verbose=1)
# 绘制不同层数的网络决策边界曲线
# 可视化的 x 坐标范围为[-2, 3]
xx = np.arange(-2, 3, 0.01)
# 可视化的 y 坐标范围为[-1.5, 2]
yy = np.arange(-1.5, 2, 0.01)
# 生成 x-y 平面采样网格点，方便可视化
XX, YY = np.meshgrid(xx, yy)
preds = model.predict_classes(np.c_[XX.ravel(), YY.ravel()])
print(preds)
title = "网络层数：{0}".format(2 + n)
file = "网络容量_%i.png" % (2 + n)
make_plot(X_train, y_train, title, file, XX, YY, preds, output_dir=OUTPUT_DIR + '/network_layers')


network_layers_influence(X_train, y_train)


## 3.Dropout的影响

def dropout_influence(X_train, y_train):
# 构建 5 种不同数量 Dropout 层的网络
for n in range(5):
# 创建容器
model = Sequential()
# 创建第一层
counter = 0
# 网络层数固定为 5
for _ in range(5):
# 添加 n 个 Dropout 层
if counter < n:
counter += 1

# 输出层
# 模型装配
# 训练
model.fit(X_train, y_train, epochs=N_EPOCHS, verbose=1)
# 绘制不同 Dropout 层数的决策边界曲线
# 可视化的 x 坐标范围为[-2, 3]
xx = np.arange(-2, 3, 0.01)
# 可视化的 y 坐标范围为[-1.5, 2]
yy = np.arange(-1.5, 2, 0.01)
# 生成 x-y 平面采样网格点，方便可视化
XX, YY = np.meshgrid(xx, yy)
preds = model.predict_classes(np.c_[XX.ravel(), YY.ravel()])
title = "无Dropout层" if n == 0 else "{0}层 Dropout层".format(n)
file = "Dropout_%i.png" % n
make_plot(X_train, y_train, title, file, XX, YY, preds, output_dir=OUTPUT_DIR + '/dropout')

dropout_influence(X_train, y_train)



## 4.正则化的影响

def build_model_with_regularization(_lambda):
# 创建带正则化项的神经网络
model = Sequential()
# 2-4层均是带 L2 正则化项
# 输出层
return model



def plot_weights_matrix(model, layer_index, plot_name, file_name, output_dir=OUTPUT_DIR):
# 绘制权值范围函数
# 提取指定层的权值矩阵
weights = model.layers[layer_index].get_weights()[0]
shape = weights.shape
# 生成和权值矩阵等大小的网格坐标
X = np.array(range(shape[1]))
Y = np.array(range(shape[0]))
X, Y = np.meshgrid(X, Y)
# 绘制3D图
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
plt.title(plot_name, fontsize=20, fontproperties='SimHei')
# 绘制权值矩阵范围
ax.plot_surface(X, Y, weights, cmap=plt.get_cmap('rainbow'), linewidth=0)
# 设置坐标轴名
ax.set_xlabel('网格x坐标', fontsize=16, rotation=0, fontproperties='SimHei')
ax.set_ylabel('网格y坐标', fontsize=16, rotation=0, fontproperties='SimHei')
ax.set_zlabel('权值', fontsize=16, rotation=90, fontproperties='SimHei')
# 保存矩阵范围图
plt.savefig(output_dir + "/" + file_name + ".svg")
plt.close(fig)



def regularizers_influence(X_train, y_train):
for _lambda in [1e-5, 1e-3, 1e-1, 0.12, 0.13]:  # 设置不同的正则化系数
# 创建带正则化项的模型
model = build_model_with_regularization(_lambda)
# 模型训练
model.fit(X_train, y_train, epochs=N_EPOCHS, verbose=1)
# 绘制权值范围
layer_index = 2
plot_title = "正则化系数：{}".format(_lambda)
file_name = "正则化网络权值_" + str(_lambda)
# 绘制网络权值范围图
plot_weights_matrix(model, layer_index, plot_title, file_name, output_dir=OUTPUT_DIR + '/regularizers')
# 绘制不同正则化系数的决策边界线
# 可视化的 x 坐标范围为[-2, 3]
xx = np.arange(-2, 3, 0.01)
# 可视化的 y 坐标范围为[-1.5, 2]
yy = np.arange(-1.5, 2, 0.01)
# 生成 x-y 平面采样网格点，方便可视化
XX, YY = np.meshgrid(xx, yy)
preds = model.predict_classes(np.c_[XX.ravel(), YY.ravel()])
title = "正则化系数：{}".format(_lambda)
file = "正则化_%g.svg" % _lambda
make_plot(X_train, y_train, title, file, XX, YY, preds, output_dir=OUTPUT_DIR + '/regularizers')


regularizers_influence(X_train, y_train)



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