基于手写数字识别数据集的机器学习方法对比研究

基于手写数字识别数据集的机器学习方法对比研究

摘要

研究意义：统计机器学习和深度学习都已被广泛地应用。

主流研究方法：在相同的数据集上进行对比实验。

前人研究存在的问题：在检索范围内，没有发现统计学习方法与深度学习方法对比的工作。

我们的解决手段：本文在手写数字识别数据集（MNIST）上，对比了主流的统计机器学习方法和深度学习方法的表现。

我们解决的还不错：通过实验证明了深度学习在 MNIST 数据集上的效果更好，准确率为97.50%；统计机器学习方法（SVM）准确率为93.71%。

Keywords: 手写数字识别, MNIST, DNN, SVM, 统计机器学习, 深度学习

实验

实验设置

Epoch : 10

Train Data Sample : 60000

Test Data Sample : 10000

Image Shape : (28, 28, 1)

实验结果

预测性能

方法	Acc on Train	Acc on Test	Paramters
DNN	0.9950	0.9808	1,238,730
CNN+MaxPooling	0.9906	0.9742	1,332,810
kernel approximation + LinearSVC	0.9378	0.9371	N/A
SVC	0.9899	0.9792	N/A

执行效率

CPU 80线程，128GB内存，固态硬盘

方法	Training and Inference
DNN	0m 38.849s
CNN+MaxPooling	11m 19.786s
kernel approximation + LinearSVC	0m 20.889s
SVC	10m 54.445s

结论

1.深度学习方法在足量的数据上，可以取得比统计学习方法更高的准确率；

2.CNN+MaxPooling方法在当前的“实验设置”下，过拟合了；

3.在当前的“实验设置”下，DNN方法的效果一致好于CNN+MaxPooling方法；

4.自带核函数的SVM预测效果，好于近似核函数和线性SVM的组合方法；

5.自带核函数的SVM，训练时间和推断时间都远高于近似核函数和线性SVM的组合方法，高于DNN，略低于CNN；

代码

DNN

# encoder=utf-8

from tensorflow import keras
from tensorflow.keras import Model, layers
from tensorflow.keras.utils import to_categorical
import numpy as np


# Load Dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
print(x_train.shape)

# Reshape the data
x_train = np.reshape(x_train, (len(x_train), 28 * 28)) / 255.0
x_test = np.reshape(x_test, (len(x_test), 28 * 28)) / 255.0
print(x_train.shape)

# categorical labels
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
print(y_train.shape)

# Define and build the model
input_img = layers.Input(shape=28*28)
x = layers.Dense(28*28, activation='relu')(input_img)
x = layers.Dense(28*28, activation='sigmoid')(x)
x = layers.Dense(10, activation='softmax')(x)
model = Model(input_img, x)
model.summary()
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics='acc'
)
model.fit(
    x=x_train,
    y=y_train,
    batch_size=128,
    epochs=10
)
loss, metric = model.evaluate(x=x_test, y=y_test, batch_size=128, )
print("cross entropy is %.4f, accuracy is %.4f" % (loss, metric))

CNN + MaxPooling

# encoder=utf-8

from tensorflow import keras
from tensorflow.keras import Model, layers
from tensorflow.keras.utils import to_categorical


# Load Dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
print(x_train.shape)

# normalize the data
x_train = x_train / 255.0
x_test = x_test / 255.0

# categorical labels
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
print(y_train.shape)

# Define and build the model
input_img = layers.Input(shape=(28, 28, 1))
x = layers.Conv2D(28*28, (3, 3))(input_img)
x = layers.MaxPooling2D((2, 2))(x)
x = layers.Flatten()(x)
x = layers.Dense(10, activation='softmax')(x)
model = Model(input_img, x)
model.summary()
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics='acc'
)
model.fit(
    x=x_train,
    y=y_train,
    batch_size=128,
    epochs=10
)
loss, metric = model.evaluate(x=x_test, y=y_test, batch_size=128, )
print("cross entropy is %.4f, accuracy is %.4f" % (loss, metric))

Kernel approximation + LinearSVM

# encoder=utf-8

from tensorflow import keras
import numpy as np
from sklearn.kernel_approximation import Nystroem
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score


# Load Dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
print(x_train.shape)

# Reshape the data
x_train = np.reshape(x_train, (len(x_train), 28 * 28)) / 255.0
x_test = np.reshape(x_test, (len(x_test), 28 * 28)) / 255.0
print(x_train.shape)
print(y_train.shape)

# Define and build the kernel mapping
x = np.concatenate((x_train, x_test))
print(x.shape)

# SVC is too slow to practice, hence we split the SVC into
# approximating kernel map (sklearn.kernel_approximation.Nystroem)
# and linear SVM (sklearn.svm.LinearSVC)
feature_map_nystroem = Nystroem(n_components=28*28)
feature_map_nystroem.fit(x)
x = feature_map_nystroem.transform(x)

x_train = x[:60000]
x_test = x[60000:]
print(x_train.shape)
print(x_test.shape)

cls = LinearSVC()
cls.fit(x_train, y_train)
y_pred = cls.predict(x_train)
ret = accuracy_score(y_train, y_pred)
print(ret)

y_pred = cls.predict(x_test)
ret = accuracy_score(y_test, y_pred)
print(ret)

SVC

# encoder=utf-8

from tensorflow import keras
import numpy as np
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score


# Load Dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
print(x_train.shape)

# Reshape the data
x_train = np.reshape(x_train, (len(x_train), 28 * 28)) / 255.0
x_test = np.reshape(x_test, (len(x_test), 28 * 28)) / 255.0
print(x_train.shape)
print(y_train.shape)


cls = SVC()
cls.fit(x_train, y_train)
y_pred = cls.predict(x_train)
ret = accuracy_score(y_train, y_pred)
print(ret)

y_pred = cls.predict(x_test)
ret = accuracy_score(y_test, y_pred)
print(ret)