Keras实践笔记11——使用简易深度神经网络识别EnglishFnt数据集

有了前面的积累，我们可以开始用一些实际的例子结合着Keras提供的Example进行学习了，后续的例子会使用EnglishFnt这个印刷体数据集进行训练和识别，这个数据集里面存放了从0-9的数字和A-Z的英文字符。

比起手写字符集MNIST，EnglishFnt长得还是很中规中矩的。为了不需要后续每次都重新加载一次数据做预处理，接下来先把这个数据集处理下，变成npy存起来

    def loadEnglishFntData(self):
        labels = []
        datasets = []
        base_path = '../datasets/English/Fnt'
        train_dir = os.listdir(base_path)
        train_dir.sort()
        for dir in train_dir:
            if dir.startswith('.'):
                continue

            print('处理文件夹:[%s]' % dir)
            for root, dirs, files in os.walk(os.path.join(base_path, dir)):
                for file in files:
                    if dir.startswith('.'):
                        continue
                    img = cv2.imread(os.path.join(root, file), cv2.COLOR_BGR2GRAY)
                    img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_CUBIC)
                    labels.append(dir)
                    datasets.append(img)

        datasets = np.array(datasets)
        labels = np.array(labels)

        # 处理数据集
        datasets = datasets.astype('float32')

        # 数据归一化
        datasets /= 255
        datasets = datasets.reshape(datasets.shape[0], 28, 28, 1)

        prepare_labels = []
        for obj in labels:
            prepare_labels.append(int(re.sub("\D", "", obj)) - 1)

        labels = keras.utils.to_categorical(prepare_labels, 62)
        return datasets, labels

这里把图片数据转成了28*28的代销，归一化后塞到了datasets里面，至于labels，我们按照文件夹的名称把数字做成了label，然后再借助了keras的to_categorical 把一维数组拉成了二维数组，接着保存下

dataloader = DataLoader()
datasets, labels = dataloader.loadEnglishFntData()
np.save('fnt_datasets.npy', datasets)
np.save('fnt_labels.npy', labels)

接下来就可以开始训练了，先试一下双层DNN训练的效果

import numpy as np
from keras import Sequential
from keras.callbacks import TensorBoard
from keras.layers import Dense, Dropout, Flatten
from keras.optimizers import RMSprop

X_Train = np.load('../fnt_datasets.npy')
Y_Train = np.load('../fnt_labels.npy')

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=X_Train.shape[1:]))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(62, activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])
model.fit(X_Train, Y_Train,
          batch_size=128,
          epochs=10,
          verbose=1,
          validation_split=0.3,
          callbacks=[TensorBoard(log_dir='./logs', histogram_freq=1)])
model.save('./model.h5')

接下来加载训练好的模型来进行检验，实验图片我们用着三张数字图片进行

import cv2
import keras
import numpy as np

model = keras.models.load_model('./model.h5')
img = cv2.imread('../datasets/22.png', cv2.IMREAD_GRAYSCALE)
img_resize = cv2.resize(img, (28, 28), interpolation=cv2.INTER_CUBIC)
result = model.predict(img_resize.reshape(1, 28, 28, 1))
print(np.argmax(result))

不得不说两层建议的DNN训练10次，效果还是挺不好的....，那我们再做个小实验，用训练出来的模型重新跑一会训练的数据集咧？随手挑一个

import os

import cv2
import keras
import numpy as np

model = keras.models.load_model('./model.h5')

success = 0
failed = 0

for root, dirs, files in os.walk('../../../datasets/English/Fnt/Sample006'):
    for file in files:
        print(file)
        img = cv2.imread(os.path.join(root, file), cv2.COLOR_BGR2GRAY)
        img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_CUBIC)
        result = model.predict(img.reshape(1, 28, 28, 1))
        if np.argmax(result) == 5:
            success += 1
        else:
            failed += 1
print(success)
print(failed)

成功判断出92个，判断失败了924个.....这简直没法用啊，哈哈哈。PS:用MacBookPro来炼丹真是挺费劲的...两层简易的DNN，一个epoch要跑200s=。=