keras构建LeNet的mnist数据集

最新推荐文章于 2023-09-11 00:01:13 发布

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博客主要围绕使用Keras构建LeNet模型来处理MNIST数据集展开，涉及利用Keras框架搭建特定模型以对MNIST数据进行分析处理等信息技术相关内容。

import keras
from keras.layers import Conv2D, MaxPool2D, Activation, Flatten, Dense
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import to_categorical
from keras.optimizers import SGD, RMSprop, Adam
import numpy as np
import matplotlib.pyplot as plt


class LeNet:
    @staticmethod
    def build(input_shape, classes):
        model = Sequential()
        model.add(Conv2D(20, (5, 5), padding='same', input_shape=input_shape))
        model.add(Activation('relu'))
        model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))

        model.add(Conv2D(50, (5, 5), padding='same'))
        model.add(Activation('relu'))
        model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))

        model.add(Flatten())
        model.add(Dense(100))
        model.add(Activation('relu'))
        model.add(Dense(classes))
        model.add(Activation('softmax'))
        return model


NB_EPOCH = 1
BATCH_SIZE = 128
VERBOSSE = 1
OPTIMIZER = Adam()
VALIDATION_SPLIT = 0.2
IMG_ROWS, IMG_COLS = 28, 28
NB_CLASSES = 10
INPUT_SHAPE = (IMG_ROWS, IMG_COLS, 1)

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

x_train = np.expand_dims(x_train, 3)
x_test = np.expand_dims(x_test, 3)

y_train = to_categorical(y_train, NB_CLASSES)
y_test = to_categorical(y_test, NB_CLASSES)

model1 = LeNet()
model = model1.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss='categorical_crossentropy', optimizer=OPTIMIZER, metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epoch=NB_EPOCH, verbose=VERBOSSE, validation_split=VALIDATION_SPLIT)
score = model.evaluate(x_test, y_test, verbose=VERBOSSE)
print('Test score:', score[0])
print('Test accuracy:', score[1])
print(history.history.keys())

plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'])
plt.show()


plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()