【Python】keras使用Lenet5識別mnist

Dsp Tian發表於2018-09-14
PythonKeras

 原始論文中的網路結構如下圖：

 

keras生成的網路結構如下圖：

程式碼如下：

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
from keras.utils.vis_utils import plot_model
from keras.utils import np_utils


# 從資料夾影象與標籤檔案載入資料
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():
        train_y.append(int(line))
    file.close()
    train_y = np.array(train_y).astype('float32')
    train_y = np_utils.to_categorical(train_y, classes)
    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')

# 從網路下載的資料集直接解析資料
'''
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST/", 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')
print(X_train.shape, X_test.shape)
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
print(X_train[0])
'''
model = Sequential()
model.add(Conv2D(filters=6, kernel_size=(5, 5), padding='valid', input_shape=(28, 28, 1), activation='tanh')) #C1
model.add(MaxPooling2D(pool_size=(2, 2)))　　　　#S2
model.add(Conv2D(filters=16, kernel_size=(5, 5), padding='valid', activation='tanh'))　　#C3
model.add(MaxPooling2D(pool_size=(2, 2)))　　　　#S4
model.add(Flatten())
model.add(Dense(120, activation='tanh'))　　　　#C5
model.add(Dense(84, activation='tanh'))　　　　#F6
model.add(Dense(10, activation='softmax'))　　#output
model.summary()

model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
history = model.fit(X_train, Y_train, batch_size=500, epochs=50, 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])

plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=False)

50次迭代，識別率在97%左右：

 相關測試資料可以在這裡下載到。

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