一次基于TensorFlow手写数字识别模型的训练

环境:

• tensorflow-estimator==2.7.0
• Python 3.9.7
• jupyter notebook
• mnist数据集

import numpy as np
import pandas as pd
from keras.utils import np_utils

from keras.datasets import mnist

(train_image,train_label), (test_image,test_label) = mnist.load_data()

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 2s 0us/step
11501568/11490434 [==============================] - 2s 0us/step

进行数据的预处理

#主意当前CNN和MLP对数据的第一步预处理差别，因CNN要进行卷积运算，需保持数组样式，不能同MLP一样转成一维784个元素
train_image_4D = train_image.reshape(60000, 28, 28, 1).astype(float)
test_image_4D = test_image.reshape(10000, 28, 28, 1).astype(float)

#标准化处理
train_image_4D_normalize = train_image_4D / 255
test_image_4D_normalize = test_image_4D / 255

#使用一位有效编码来处理标签
train_label_onehotencoding = np_utils.to_categorical(train_label)
test_label_onehotencoding = np_utils.to_categorical(test_label)

建立CNN卷积神经网络

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Conv2D, MaxPooling2D, Flatten  #卷积层,池化层,平坦层

model = Sequential()

添加卷积层1

# filters:滤镜个数 kernel_size:滤镜尺寸 
# padding='same'表示不改变图片大小 input_shape:输入图片的尺寸 activation激活函数
model.add(Conv2D(filters=16, kernel_size=(5,5), padding='same', input_shape=(28,28,1), activation='relu'))

添加池化层1

model.add(MaxPooling2D(pool_size = (2,2))) #将28*28的图片缩减一半变为14*14

添加卷积层2

# filters:滤镜个数 kernel_size:滤镜尺寸 
# padding='same'表示不改变图片大小 input_shape:输入图片的尺寸 activation激活函数
model.add(Conv2D(filters=36, kernel_size=(5,5), padding='same', activation='relu'))

添加池化层2

model.add(MaxPooling2D(pool_size = (2,2)))

model.add(Dropout(0.25)) #避免过度拟合

添加平坦层

model.add(Flatten())

添加隐藏层

model.add(Dense(units=128, kernel_initializer='normal', activation='relu'))

model.add(Dropout(0.5)) #避免过度拟合

建立隐藏层和输出层之间的关系

model.add(Dense(units=10,kernel_initializer='normal', activation='softmax'))

print(model.summary())

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 28, 28, 16)        416       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 14, 14, 36)        14436     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 7, 7, 36)         0         
 2D)                                                             
                                                                 
 dropout (Dropout)           (None, 7, 7, 36)          0         
                                                                 
 flatten (Flatten)           (None, 1764)              0         
                                                                 
 dense_1 (Dense)             (None, 128)               225920    
                                                                 
 dropout_1 (Dropout)         (None, 128)               0         
                                                                 
 dense_2 (Dense)             (None, 10)                1290      
                                                                 
=================================================================
Total params: 242,062
Trainable params: 242,062
Non-trainable params: 0
_________________________________________________________________
None

#配置下训练模型 loss:损失函数 optimizer:优化器,通过优化让训练的结果能够更快的收敛
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

#train_image_4D_normalize :训练数据 train_label_onehotencoding: 训练标签
#validation_split:取出一定比例的数据用于验证 epoch:训练次数 batch_size: 每次训练取出多少数据用于训练
#verbose: 2表示是示训练过程
train_history = model.fit(train_image_4D_normalize, train_label_onehotencoding, validation_split=0.2, epochs=10, batch_size=200, verbose=2)

Epoch 1/10
240/240 - 19s - loss: 0.5127 - accuracy: 0.8396 - val_loss: 0.1068 - val_accuracy: 0.9671 - 19s/epoch - 80ms/step
Epoch 2/10
240/240 - 19s - loss: 0.1492 - accuracy: 0.9556 - val_loss: 0.0668 - val_accuracy: 0.9793 - 19s/epoch - 78ms/step
Epoch 3/10
240/240 - 19s - loss: 0.1062 - accuracy: 0.9682 - val_loss: 0.0577 - val_accuracy: 0.9831 - 19s/epoch - 78ms/step
Epoch 4/10
240/240 - 19s - loss: 0.0875 - accuracy: 0.9739 - val_loss: 0.0491 - val_accuracy: 0.9839 - 19s/epoch - 78ms/step
Epoch 5/10
240/240 - 21s - loss: 0.0747 - accuracy: 0.9772 - val_loss: 0.0432 - val_accuracy: 0.9867 - 21s/epoch - 89ms/step
Epoch 6/10
240/240 - 20s - loss: 0.0642 - accuracy: 0.9803 - val_loss: 0.0441 - val_accuracy: 0.9881 - 20s/epoch - 83ms/step
Epoch 7/10
240/240 - 20s - loss: 0.0583 - accuracy: 0.9822 - val_loss: 0.0375 - val_accuracy: 0.9903 - 20s/epoch - 82ms/step
Epoch 8/10
240/240 - 20s - loss: 0.0521 - accuracy: 0.9843 - val_loss: 0.0366 - val_accuracy: 0.9896 - 20s/epoch - 84ms/step
Epoch 9/10
240/240 - 19s - loss: 0.0475 - accuracy: 0.9855 - val_loss: 0.0342 - val_accuracy: 0.9905 - 19s/epoch - 78ms/step
Epoch 10/10
240/240 - 19s - loss: 0.0429 - accuracy: 0.9868 - val_loss: 0.0364 - val_accuracy: 0.9902 - 19s/epoch - 77ms/step

import matplotlib.pyplot as plt
def show_train_history(history, train, validation):
    plt.plot (history.history[train])
    plt.plot(train_history.history[validation])
    plt.title('Train History')
    plt.xlabel('Epoch')
    plt.ylabel(train)
    plt.legend(['train', 'validation'], loc = 'upper left')
    plt.show()

show_train_history(train_history, 'accuracy', 'val_accuracy')

show_train_history(train_history, 'loss', 'val_loss') #损失函数

利用10000项测试数据评估下模型的准确率

scores = model.evaluate(test_image_4D_normalize, test_label_onehotencoding)
print(scores)

313/313 [==============================] - 1s 4ms/step - loss: 0.0260 - accuracy: 0.9911: 0s
[0.02601146325469017, 0.991100013256073]

可以看出，卷积神经网络CNN比多层感知器模型MLP要优秀，不但准确率提升明显，而且过度拟合的问题也被有效抑制了

导出训练模型

model.save('CNN_MNIST_MODEL.h5')

测试所导出的模型

from PIL import Image

import numpy as np
from keras.models import load_model

my_model = load_model('CNN_MNIST_MODEL.h5')

img = Image.open('16-4.bmp')
number_data = img.getdata()
number_data_array = np.array(number_data)
number_data_array = number_data_array.reshape(1, 28, 28,1).astype(float)
number_data_normalize = number_data_array / 255

prediction= load_model('CNN_MNIST_MODEL.h5')(number_data_normalize)
np.argmax(prediction)

4

不足:

• CPU占用太满,GPU利用率极低
• 对于图片格式,大小较为吝啬,有待改进