【Python】keras卷积神经网络识别mnist

时间：2021-10-02 04:24:06

卷积神经网络的结构我随意设了一个。

结构大概是下面这个样子：

【Python】keras卷积神经网络识别mnist

代码如下：

import numpy as np

from keras.preprocessing import image

from keras.models import Sequential

from keras.layers import Dense, Dropout, Flatten, Activation

from keras.layers import Conv2D, MaxPooling2D

# 从文件夹图像与标签文件载入数据

def create_x(filenum, file_dir):

    train_x = []

    for i in range(filenum):

        img = image.load_img(file_dir + str(i) + ".bmp", target_size=(28, 28))

        img = img.convert('L')

        x = image.img_to_array(img)

        train_x.append(x)

    train_x = np.array(train_x)

    train_x = train_x.astype('float32')

    train_x /= 255

    return train_x

def create_y(classes, filename):

    train_y = []

    file = open(filename, "r")

    for line in file.readlines():

        tmp = []

        for j in range(classes):

            if j == int(line):

                tmp.append(1)

            else:

                tmp.append(0)

        train_y.append(tmp)

    file.close()

    train_y = np.array(train_y).astype('float32')

    return train_y

classes = 10

X_train = create_x(55000, './train/')

X_test = create_x(10000, './test/')

Y_train = create_y(classes, 'train.txt')

Y_test = create_y(classes, 'test.txt')

# 从网络下载的数据集直接解析数据

'''

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

X_train, Y_train = mnist.train.images, mnist.train.labels

X_test, Y_test = mnist.test.images, mnist.test.labels

X_train = X_train.astype('float32')

X_test = X_test.astype('float32')

'''

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))

model.add(Conv2D(64, (3, 3), activation='relu'))

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

model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(81, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(10))

model.add(Activation('softmax'))

model.summary()

model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

history = model.fit(X_train, Y_train, batch_size=500, epochs=10, verbose=1, validation_data=(X_test, Y_test))

score = model.evaluate(X_test, Y_test, verbose=0)

test_result = model.predict(X_test)

result = np.argmax(test_result, axis=1)

print(result)

print('Test score:', score[0])

print('Test accuracy:', score[1])

最终在测试集上识别率在99%左右。

【Python】keras卷积神经网络识别mnist

相关测试数据可以在这里下载到。

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