Keras学习～第一个例子～跑MNIST

时间：2023-10-01 10:50:31
import numpy as npimport gzip

import struct

import keras as ks

import logging

from keras.layers import Dense, Activation, Flatten, Convolution2D

from keras.utils import np_utils

def read_data(label_url,image_url):

    with gzip.open(label_url) as flbl:

        magic, num = struct.unpack(">II",flbl.read(8))

        label = np.fromstring(flbl.read(),dtype=np.int8)

    with gzip.open(image_url,'rb') as fimg:

        magic, num, rows, cols = struct.unpack(">IIII",fimg.read(16))

        image = np.fromstring(fimg.read(),dtype=np.uint8).reshape(len(label),rows,cols)

    return (label, image)

(train_lbl, train_img) = read_data('mnist/train-labels-idx1-ubyte.gz','mnist/train-images-idx3-ubyte.gz')

(val_lbl, val_img) = read_data('mnist/t10k-labels-idx1-ubyte.gz','mnist/t10k-images-idx3-ubyte.gz')

def to4d(img):

    return img.reshape(img.shape[0],784).astype(np.float32)/255

train_img = to4d(train_img)

val_img = to4d(val_img)

#important

train_LBL = np_utils.to_categorical(train_lbl,nb_classes=10)

val_LBL = np_utils.to_categorical(val_lbl,nb_classes=10)

model = ks.models.Sequential()

model.add(Dense(128,input_dim=784))

model.add(Activation('relu'))

model.add(Dense(64))

model.add(Activation('relu'))

model.add(Dense(10))

model.add(Activation('softmax'))

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

model.fit(x=train_img,y=train_LBL,batch_size=100,nb_epoch=10,verbose=1,validation_data=(val_img,val_LBL))

源码如上↑

mnist的数据集下载好后保存在mnist的文件夹里，main.py里复制粘贴上面的源码应该就能跑了，main.py要放在mnist文件夹的外边，最重要的就是#important下面的两行，keras似乎必须要对标签分类之后才能使用标签。

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