TensorFlow2.0 实现MNIST

参考了这篇：https://geektutu.com/post/tensorflow2-mnist-cnn.html

但是其中有个小问题，这里做出纠正；

MNIST采用LeNet5的网络模型，输入一个矩阵或图像，大小为$32*32$。
不计输入层，有3个卷积层，2个下采样（pooling）层，1个全连接层和1个输出层
LeNet论文：论文地址

输入包

import os
import tensorflow as tf
import numpy as np
from tensorflow.keras import datasets, layers, models
from PIL import Image
import matplotlib.pyplot as plt
import scipy.misc
np.set_printoptions(suppress=True)  # 不用科学计数法输出

定义模型

class LeNet(object):def __init__(self):model = models.Sequential()# 第1层卷积，卷积核大小为3*3，32个，28*28为待训练图片的大小model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))model.add(layers.MaxPooling2D((2, 2)))# 第2层卷积，卷积核大小为3*3，64个model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))# 第3层卷积，卷积核大小为3*3，64个model.add(layers.Conv2D(64, (3, 3), activation='relu'))# Flatten层用来将输入“压平”，即把多维的输入一维化，常用在从卷积层到全连接层的过渡。Flatten不影响batch的大小。model.add(layers.Flatten())# 全连接层model.add(layers.Dense(64, activation='relu'))model.add(layers.Dense(10, activation='softmax'))# model.summary()输出模型各层的参数状况，打印模型的结构model.summary()self.model = model

数据准备

class DataSource(object):def __init__(self):# minisi存储位置，不存在讲自动下载data_path = os.path.abspath(os.path.dirname('__file__')) + '\\dataset_tf2\\minist.npz'(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data(path=data_path)# 6万张训练，1万张测试train_images = train_images.reshape((60000, 28, 28, 1))test_images = test_images.reshape((10000, 28, 28, 1))# 像素值映射到0 - 1之间train_images, test_images = train_images/255.0, test_images/255.0# print(train_images[0], train_labels[0])self.train_images, self.train_labels = train_images, train_labelsself.test_images, self.test_labels = test_images, test_labels

训练

class Train:def __init__(self):self.cnn = LeNet()self.data = DataSource()def train(self):check_path = './ckpt/cp-{epoch:04d}.ckpt'# period 每隔5epoch保存一次save_model_cb = tf.keras.callbacks.ModelCheckpoint(check_path, save_weights_only=True, verbose=1, period=5)self.cnn.model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])self.cnn.model.fit(self.data.train_images, self.data.train_labels, epochs=5, callbacks=[save_model_cb])test_loss, test_acc = self.cnn.model.evaluate(self.data.test_images, self.data.test_labels)print("准确率: %.4f，共测试了%d张图片 " % (test_acc, len(self.data.test_labels)))print(self.data.test_images[0],self.data.test_labels[0])

app = Train()
app.train()

预测

原文中语句为：x = np.array([1 - flatten_img]) ，但训练了几遍预测结果都不对，最后打印数据发现flatten_img的数据没有归一化，改成x = np.array([1 - (flatten_img / 255.0)])即可。

class Predict(object):def __init__(self):latest = tf.train.latest_checkpoint('./ckpt')self.cnn = LeNet()# 恢复网络权重self.cnn.model.load_weights(latest)def predict(self, image_path):# 以黑白方式读取图片img = Image.open(image_path).convert('L')print(img.size)img = img.resize((28, 28))img_array = img.load()print(img.size)plt.imshow(img)flatten_img = np.reshape(img, (28, 28, 1))x = np.array([1 - (flatten_img / 255.0)])# API refer: https://keras.io/models/model/y = self.cnn.model.predict(x)# 因为x只传入了一张图片，取y[0]即可# np.argmax()取得最大值的下标，即代表的数字print(image_path)print(y[0])print('        -> Predict digit', np.argmax(y[0]))

app = Predict()
app.predict('./test_images/test.png')