keras CNN

1、数据预处理

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from keras import backend as K

num_classes = 10
img_rows, img_cols = 28, 28
 
# 通过Keras封装好的API加载MNIST数据。其中trainX就是一个60000 * 28 * 28的数组，
# trainY是每一张图片对应的数字。
(trainX, trainY), (testX, testY) = mnist.load_data()

# 根据对图像编码的格式要求来设置输入层的格式。
if K.image_data_format() == 'channels_first':
    trainX = trainX.reshape(trainX.shape[0], 1, img_rows, img_cols)
    testX = testX.reshape(testX.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    trainX = trainX.reshape(trainX.shape[0], img_rows, img_cols, 1)
    testX = testX.reshape(testX.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)1、数据预处理

import keras
from keras.datas