重庆装修网站建设,怎么在各个网站免费推广信息,城乡建设部网站首页,宝安做棋牌网站建设哪家好文章目录 前期工作1. 设置GPU#xff08;如果使用的是CPU可以忽略这步#xff09;我的环境#xff1a; 2. 导入数据3. 查看数据 二、数据预处理1. 加载数据2. 可视化数据3. 再次检查数据4. 配置数据集5. 归一化 三、构建VGG-19网络1. 官方模型#xff08;已打包好#xff… 文章目录 前期工作1. 设置GPU如果使用的是CPU可以忽略这步我的环境 2. 导入数据3. 查看数据 二、数据预处理1. 加载数据2. 可视化数据3. 再次检查数据4. 配置数据集5. 归一化 三、构建VGG-19网络1. 官方模型已打包好2. 自建模型3. 网络结构图 四、编译五、训练模型六、模型评估七、保存and加载模型八、预测 前期工作 1. 设置GPU如果使用的是CPU可以忽略这步 我的环境 语言环境Python3.6.5编译器jupyter notebook深度学习环境TensorFlow2.4.1 import tensorflow as tfgpus tf.config.list_physical_devices(GPU)if gpus:tf.config.experimental.set_memory_growth(gpus[0], True) #设置GPU显存用量按需使用tf.config.set_visible_devices([gpus[0]],GPU)2. 导入数据 import matplotlib.pyplot as plt # 支持中文 plt.rcParams[font.sans-serif] [SimHei] # 用来正常显示中文标签 plt.rcParams[axes.unicode_minus] False # 用来正常显示负号import os,PIL# 设置随机种子尽可能使结果可以重现 import numpy as np np.random.seed(1)# 设置随机种子尽可能使结果可以重现 import tensorflow as tf tf.random.set_seed(1)from tensorflow import keras from tensorflow.keras import layers,modelsimport pathlibdata_dir weather_photos/ data_dir pathlib.Path(data_dir)3. 查看数据 数据集中一共有白月魁、查尔斯、红蔻、马克、摩根、冉冰等6个人物角色。 文件夹含义数量baiyuekui白月魁40 张chaersi查尔斯76 张hongkou红蔻36 张make马克38张mogen摩根30 张ranbing冉冰60张 image_count len(list(data_dir.glob(*/*)))print(图片总数为,image_count)二、数据预处理 1. 加载数据 使用image_dataset_from_directory方法将磁盘中的数据加载到tf.data.Dataset中 batch_size 32 img_height 224 img_width 224train_ds tf.keras.preprocessing.image_dataset_from_directory(data_dir,validation_split0.1,subsettraining,seed123,image_size(img_height, img_width),batch_sizebatch_size)Found 280 files belonging to 6 classes. Using 252 files for training.val_ds tf.keras.preprocessing.image_dataset_from_directory(data_dir,validation_split0.1,subsetvalidation,seed123,image_size(img_height, img_width),batch_sizebatch_size)Found 280 files belonging to 6 classes. Using 28 files for validation.我们可以通过class_names输出数据集的标签。标签将按字母顺序对应于目录名称。 class_names train_ds.class_names print(class_names)[baiyuekui, chaersi, hongkou, make, mogen, ranbing]2. 可视化数据 plt.figure(figsize(10, 5)) # 图形的宽为10高为5for images, labels in train_ds.take(1):for i in range(8):ax plt.subplot(2, 4, i 1) plt.imshow(images[i].numpy().astype(uint8))plt.title(class_names[labels[i]])plt.axis(off)plt.imshow(images[1].numpy().astype(uint8))3. 再次检查数据 for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break(16, 224, 224, 3) (16,)Image_batch是形状的张量16,180,180,3。这是一批形状180x180x3的16张图片最后一维指的是彩色通道RGB。Label_batch是形状16的张量这些标签对应16张图片 4. 配置数据集 AUTOTUNE tf.data.AUTOTUNEtrain_ds train_ds.cache().shuffle(1000).prefetch(buffer_sizeAUTOTUNE) val_ds val_ds.cache().prefetch(buffer_sizeAUTOTUNE)5. 归一化 normalization_layer layers.experimental.preprocessing.Rescaling(1./255) normalization_train_ds train_ds.map(lambda x, y: (normalization_layer(x), y)) val_ds val_ds.map(lambda x, y: (normalization_layer(x), y)) image_batch, labels_batch next(iter(val_ds)) first_image image_batch[0] # 查看归一化后的数据 print(np.min(first_image), np.max(first_image))0.0 0.9928046三、构建VGG-19网络 VGG优缺点分析 VGG优点 VGG的结构非常简洁整个网络都使用了同样大小的卷积核尺寸3x3和最大池化尺寸2x2。 VGG缺点 1)训练时间过长调参难度大。2)需要的存储容量大不利于部署。例如存储VGG-16权重值文件的大小为500多MB不利于安装到嵌入式系统中。 1. 官方模型已打包好 官网模型调用这块我放到后面几篇文章中下面主要讲一下VGG-19 # model keras.applications.VGG19(weightsimagenet) # model.summary()2. 自建模型 from tensorflow.keras import layers, models, Input from tensorflow.keras.models import Model from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropoutdef VGG19(nb_classes, input_shape):input_tensor Input(shapeinput_shape)# 1st blockx Conv2D(64, (3,3), activationrelu, paddingsame,nameblock1_conv1)(input_tensor)x Conv2D(64, (3,3), activationrelu, paddingsame,nameblock1_conv2)(x)x MaxPooling2D((2,2), strides(2,2), name block1_pool)(x)# 2nd blockx Conv2D(128, (3,3), activationrelu, paddingsame,nameblock2_conv1)(x)x Conv2D(128, (3,3), activationrelu, paddingsame,nameblock2_conv2)(x)x MaxPooling2D((2,2), strides(2,2), name block2_pool)(x)# 3rd blockx Conv2D(256, (3,3), activationrelu, paddingsame,nameblock3_conv1)(x)x Conv2D(256, (3,3), activationrelu, paddingsame,nameblock3_conv2)(x)x Conv2D(256, (3,3), activationrelu, paddingsame,nameblock3_conv3)(x)x Conv2D(256, (3,3), activationrelu, paddingsame,nameblock3_conv4)(x)x MaxPooling2D((2,2), strides(2,2), name block3_pool)(x)# 4th blockx Conv2D(512, (3,3), activationrelu, paddingsame,nameblock4_conv1)(x)x Conv2D(512, (3,3), activationrelu, paddingsame,nameblock4_conv2)(x)x Conv2D(512, (3,3), activationrelu, paddingsame,nameblock4_conv3)(x)x Conv2D(512, (3,3), activationrelu, paddingsame,nameblock4_conv4)(x)x MaxPooling2D((2,2), strides(2,2), name block4_pool)(x)# 5th blockx Conv2D(512, (3,3), activationrelu, paddingsame,nameblock5_conv1)(x)x Conv2D(512, (3,3), activationrelu, paddingsame,nameblock5_conv2)(x)x Conv2D(512, (3,3), activationrelu, paddingsame,nameblock5_conv3)(x)x Conv2D(512, (3,3), activationrelu, paddingsame,nameblock5_conv4)(x)x MaxPooling2D((2,2), strides(2,2), name block5_pool)(x)# full connectionx Flatten()(x)x Dense(4096, activationrelu, namefc1)(x)x Dense(4096, activationrelu, namefc2)(x)output_tensor Dense(nb_classes, activationsoftmax, namepredictions)(x)model Model(input_tensor, output_tensor)return modelmodelVGG19(1000, (img_width, img_height, 3)) model.summary()Model: model _________________________________________________________________ Layer (type) Output Shape Param # input_1 (InputLayer) [(None, 224, 224, 3)] 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_conv4 (Conv2D) (None, 56, 56, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_conv4 (Conv2D) (None, 28, 28, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_conv4 (Conv2D) (None, 14, 14, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 25088) 0 _________________________________________________________________ fc1 (Dense) (None, 4096) 102764544 _________________________________________________________________ fc2 (Dense) (None, 4096) 16781312 _________________________________________________________________ predictions (Dense) (None, 1000) 4097000 Total params: 143,667,240 Trainable params: 143,667,240 Non-trainable params: 0 _________________________________________________________________3. 网络结构图 结构说明 16个卷积层Convolutional Layer分别用blockX_convX表示3个全连接层Fully connected Layer分别用fcX与predictions表示5个池化层Pool layer分别用blockX_pool表示 VGG-19包含了19个隐藏层16个卷积层和3个全连接层故称为VGG-19 ** ** 四、编译 在准备对模型进行训练之前还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的 损失函数loss用于衡量模型在训练期间的准确率。优化器optimizer决定模型如何根据其看到的数据和自身的损失函数进行更新。指标metrics用于监控训练和测试步骤。以下示例使用了准确率即被正确分类的图像的比率。 # 设置优化器 opt tf.keras.optimizers.Adam(learning_rate1e-4)model.compile(optimizeropt,losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsTrue),metrics[accuracy])五、训练模型 epochs 10history model.fit(train_ds,validation_dataval_ds,epochsepochs )Epoch 1/10 16/16 [] - 21s 274ms/step - loss: 5.4494 - accuracy: 0.1508 - val_loss: 6.8600 - val_accuracy: 0.0714 Epoch 2/10 16/16 [] - 2s 130ms/step - loss: 1.7976 - accuracy: 0.3174 - val_loss: 6.8402 - val_accuracy: 0.3929 Epoch 3/10 16/16 [] - 2s 139ms/step - loss: 1.4882 - accuracy: 0.4201 - val_loss: 6.8453 - val_accuracy: 0.5357 Epoch 4/10 16/16 [] - 2s 135ms/step - loss: 1.1548 - accuracy: 0.5917 - val_loss: 6.8551 - val_accuracy: 0.3571 Epoch 5/10 16/16 [] - 2s 139ms/step - loss: 1.0376 - accuracy: 0.6267 - val_loss: 6.8421 - val_accuracy: 0.4286 Epoch 6/10 16/16 [] - 2s 136ms/step - loss: 1.0189 - accuracy: 0.5942 - val_loss: 6.8277 - val_accuracy: 0.5714 Epoch 7/10 16/16 [] - 2s 133ms/step - loss: 0.6873 - accuracy: 0.7761 - val_loss: 6.8382 - val_accuracy: 0.6429 Epoch 8/10 16/16 [] - 2s 128ms/step - loss: 0.3739 - accuracy: 0.9019 - val_loss: 6.8109 - val_accuracy: 0.5357 Epoch 9/10 16/16 [] - 2s 128ms/step - loss: 0.3761 - accuracy: 0.8547 - val_loss: 6.8101 - val_accuracy: 0.6429 Epoch 10/10 16/16 [] - 2s 129ms/step - loss: 0.1258 - accuracy: 0.9713 - val_loss: 6.7796 - val_accuracy: 0.8929六、模型评估 acc history.history[accuracy] val_acc history.history[val_accuracy]loss history.history[loss] val_loss history.history[val_loss]epochs_range range(epochs)plt.figure(figsize(12, 4)) plt.subplot(1, 2, 1) plt.plot(epochs_range, acc, labelTraining Accuracy) plt.plot(epochs_range, val_acc, labelValidation Accuracy) plt.legend(loclower right) plt.title(Training and Validation Accuracy)plt.subplot(1, 2, 2) plt.plot(epochs_range, loss, labelTraining Loss) plt.plot(epochs_range, val_loss, labelValidation Loss) plt.legend(locupper right) plt.title(Training and Validation Loss) plt.show()七、保存and加载模型 # 保存模型 model.save(model/my_model.h5) # 加载模型 new_model keras.models.load_model(model/my_model.h5)八、预测 # 采用加载的模型new_model来看预测结果plt.figure(figsize(10, 5)) # 图形的宽为10高为5for images, labels in val_ds.take(1):for i in range(8):ax plt.subplot(2, 4, i 1) # 显示图片plt.imshow(images[i])# 需要给图片增加一个维度img_array tf.expand_dims(images[i], 0) # 使用模型预测图片中的人物predictions new_model.predict(img_array)plt.title(class_names[np.argmax(predictions)])plt.axis(off)