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外贸那些网站好用吗,客户管理系统排行榜,展厅设计说明200字,厦门建设局电话一、前置知识1、CNN知识扫盲1.1. 卷积#xff08;Convolution#xff09;怎么做#xff1f;用一个叫 卷积核#xff08;Kernel#xff09; 或 滤波器#xff08;Filter#xff09; 的小矩阵#xff08;如 33#xff09;在图像上滑动。每滑一步#xff0c;和底下像素做…一、前置知识1、CNN知识扫盲1.1.卷积Convolution怎么做用一个叫卷积核Kernel或滤波器Filter的小矩阵如 3×3在图像上滑动。每滑一步和底下像素做“点乘 求和”得到一个新值 → 构成特征图Feature Map。不同卷积核 不同“滤镜”一个核专门检测竖直边缘一个核专门检测45度斜线一个核专门检测亮斑关键优势局部连接每个神经元只看图像一小块感受野符合生物视觉参数共享同一个卷积核在整个图像上复用 → 参数量暴减普通神经网络需百万参数CNN 只需几千1.2.池化Pooling作用降低特征图尺寸减少计算量同时让网络对微小位移“不敏感”鲁棒性。最常用最大池化Max Pooling把特征图切成 2×2 小块每块只保留最大值结果尺寸减半但最显著的特征如最强边缘被保留举例即使猫头稍微偏左/偏右最大池化后“耳朵区域”依然高亮 → 不影响识别1.3.堆叠与全连接Stacking FC浅层卷积检测简单模式边缘、角点中层卷积组合简单模式 → 复杂模式眼睛、耳朵深层卷积组合部件 → 完整物体猫、狗、车最后接全连接层FC把高级特征“拍平”送入分类器输出概率整个过程原始图像 → 卷积 → 激活ReLU→ 池化 → 卷积 → ... → 全连接 → 分类结果2、图片分类流程二、代码实现1、准备工作1.1.设置GPUimport tensorflow as tf gpus tf.config.list_physical_devices(GPU) if gpus: gpu0 gpus[0] #如果有多个GPU仅使用第0个GPU tf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用 tf.config.set_visible_devices([gpu0],GPU) print(gpus)2026-01-14 13:03:21.672196: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS0. [PhysicalDevice(name/physical_device:GPU:0, device_typeGPU)]1.2.导入数据import tensorflow as tf from tensorflow.keras import datasets, layers, models import matplotlib.pyplot as plt (train_images, train_labels), (test_images, test_labels) datasets.cifar10.load_data()Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz 170498071/170498071 [] - 947s 6us/step1.3.归一化# 将像素的值标准化至0到1的区间内。(对于灰度图片来说每个像素最大值是255每个像素最小值是0也就是直接除以255就可以完成归一化。) train_images, test_images train_images / 255.0, test_images / 255.0 # 查看数据维数信息 train_images.shape,test_images.shape,train_labels.shape,test_labels.shape((50000, 32, 32, 3), (10000, 32, 32, 3), (50000, 1), (10000, 1))1.4.可视化图片class_names [airplane, automobile, bird, cat, deer,dog, frog, horse, ship, truck] plt.figure(figsize(20,10)) for i in range(20): plt.subplot(5,10,i1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i], cmapplt.cm.binary) plt.xlabel(class_names[train_labels[i][0]]) plt.show()2、训练模型2.1.构建CNN网络model models.Sequential([ layers.Conv2D(32, (3, 3), activationrelu, input_shape(32, 32, 3)), #卷积层1卷积核3*3 layers.MaxPooling2D((2, 2)), #池化层12*2采样 layers.Conv2D(64, (3, 3), activationrelu), #卷积层2卷积核3*3 layers.MaxPooling2D((2, 2)), #池化层22*2采样 layers.Conv2D(64, (3, 3), activationrelu), #卷积层3卷积核3*3 layers.Flatten(), #Flatten层连接卷积层与全连接层 layers.Dense(64, activationrelu), #全连接层特征进一步提取 layers.Dense(10) #输出层输出预期结果 ]) model.summary() # 打印网络结构2026-01-14 13:25:51.622783: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2026-01-14 13:25:52.805568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10099 MB memory: - device: 0, name: NVIDIA GeForce RTX 3080 Ti, pci bus id: 0000:5e:00.0, compute capability: 8.6 Model: sequential _________________________________________________________________ Layer (type) Output Shape Param # conv2d (Conv2D) (None, 30, 30, 32) 896 max_pooling2d (MaxPooling2D (None, 15, 15, 32) 0 ) conv2d_1 (Conv2D) (None, 13, 13, 64) 18496 max_pooling2d_1 (MaxPooling (None, 6, 6, 64) 0 2D) conv2d_2 (Conv2D) (None, 4, 4, 64) 36928 flatten (Flatten) (None, 1024) 0 dense (Dense) (None, 64) 65600 dense_1 (Dense) (None, 10) 650 Total params: 122,570 Trainable params: 122,570 Non-trainable params: 0 _________________________________________________________________2.2.编译模型model.compile(optimizeradam, losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsTrue), metrics[accuracy])2.3.训练模型history model.fit(train_images, train_labels, epochs10, validation_data(test_images, test_labels))Epoch 1/10 2026-01-14 13:26:28.595628: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101 2026-01-14 13:26:31.971672: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once. 1563/1563 [] - 15s 6ms/step - loss: 1.5071 - accuracy: 0.4519 - val_loss: 1.2221 - val_accuracy: 0.5586 Epoch 2/10 1563/1563 [] - 9s 6ms/step - loss: 1.1403 - accuracy: 0.5940 - val_loss: 1.0596 - val_accuracy: 0.6186 Epoch 3/10 1563/1563 [] - 9s 6ms/step - loss: 0.9880 - accuracy: 0.6537 - val_loss: 0.9577 - val_accuracy: 0.6648 Epoch 4/10 1563/1563 [] - 9s 6ms/step - loss: 0.8843 - accuracy: 0.6883 - val_loss: 0.9154 - val_accuracy: 0.6849 Epoch 5/10 1563/1563 [] - 9s 6ms/step - loss: 0.8077 - accuracy: 0.7143 - val_loss: 0.8642 - val_accuracy: 0.6990 Epoch 6/10 1563/1563 [] - 9s 6ms/step - loss: 0.7459 - accuracy: 0.7385 - val_loss: 0.9030 - val_accuracy: 0.6912 Epoch 7/10 1563/1563 [] - 9s 6ms/step - loss: 0.6906 - accuracy: 0.7584 - val_loss: 0.8798 - val_accuracy: 0.7117 Epoch 8/10 1563/1563 [] - 9s 6ms/step - loss: 0.6560 - accuracy: 0.7673 - val_loss: 0.8750 - val_accuracy: 0.7042 Epoch 9/10 1563/1563 [] - 9s 6ms/step - loss: 0.6054 - accuracy: 0.7874 - val_loss: 0.9065 - val_accuracy: 0.7002 Epoch 10/10 1563/1563 [] - 9s 6ms/step - loss: 0.5748 - accuracy: 0.7987 - val_loss: 0.9055 - val_accuracy: 0.70493、模型预测plt.imshow(test_images[1])matplotlib.image.AxesImage at 0x7f3e28442b80import numpy as np pre model.predict(test_images) print(class_names[np.argmax(pre[1])])313/313 [] - 1s 2ms/step ship4、模型评估import matplotlib.pyplot as plt plt.plot(history.history[accuracy], labelaccuracy) plt.plot(history.history[val_accuracy], label val_accuracy) plt.xlabel(Epoch) plt.ylabel(Accuracy) plt.ylim([0.5, 1]) plt.legend(loclower right) plt.show() test_loss, test_acc model.evaluate(test_images, test_labels, verbose2)313/313 - 1s - loss: 0.9055 - accuracy: 0.7049 - 874ms/epoch - 3ms/stepprint(test_acc)0.7049000263214111