东莞网站推广,北京公司注册地址要求,迈肯奇迹做网站,中信建设有限责任公司电子商务平台文章目录 一、前言二、前期工作1. 设置GPU#xff08;如果使用的是CPU可以忽略这步#xff09;2. 导入数据3. 查看数据 二、构建一个tf.data.Dataset1.加载数据2. 配置数据集 三、构建Inception-ResNet-v2网络1.自己搭建2.官方模型 五、设置动态学习率六、训练模型七、模型评… 文章目录 一、前言二、前期工作1. 设置GPU如果使用的是CPU可以忽略这步2. 导入数据3. 查看数据 二、构建一个tf.data.Dataset1.加载数据2. 配置数据集 三、构建Inception-ResNet-v2网络1.自己搭建2.官方模型 五、设置动态学习率六、训练模型七、模型评估八、模型的保存与加载九、预测 一、前言
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
往期精彩内容
卷积神经网络CNN实现mnist手写数字识别 卷积神经网络CNN多种图片分类的实现卷积神经网络CNN衣服图像分类的实现卷积神经网络CNN鲜花识别卷积神经网络CNN天气识别 卷积神经网络VGG-16识别海贼王草帽一伙卷积神经网络ResNet-50鸟类识别 卷积神经网络AlexNet鸟类识别卷积神经网络(CNN)识别验证码
来自专栏机器学习与深度学习算法推荐
二、前期工作
1. 设置GPU如果使用的是CPU可以忽略这步
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,pathlib# 设置随机种子尽可能使结果可以重现
import pandas as pd
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,models# 导入图片数据
pictures_dir images
pictures_dir pathlib.Path(pictures_dir)# 导入训练数据的图片路径名及标签
train pd.read_csv(annotations.csv)3. 查看数据
image_count len(list(pictures_dir.glob(*.png)))
print(图片总数为,image_count)图片总数为 5998train.head()file_namecategory0000_0001.png01000_0002.png02000_0003.png03000_0010.png04000_0011.png0
二、构建一个tf.data.Dataset
1.加载数据
数据集中已经划分好了测试集与训练集这次只需要进行分别加载就好了。
def preprocess_image(image):image tf.image.decode_jpeg(image, channels3) # 编码解码处理image tf.image.resize(image, [299,299]) # 图片调整return image/255.0 # 归一化处理def load_and_preprocess_image(path):image tf.io.read_file(path)return preprocess_image(image)AUTOTUNE tf.data.experimental.AUTOTUNE
common_paths images/# 训练数据的标签
train_image_label [i for i in train[category]]
train_label_ds tf.data.Dataset.from_tensor_slices(train_image_label)# 训练数据的路径
train_image_paths [ common_pathsi for i in train[file_name]]
# 加载图片路径
train_path_ds tf.data.Dataset.from_tensor_slices(train_image_paths)
# 加载图片数据
train_image_ds train_path_ds.map(load_and_preprocess_image, num_parallel_callsAUTOTUNE)# 将图片与标签进行对应打包
image_label_ds tf.data.Dataset.zip((train_image_ds, train_label_ds))
image_label_dsplt.figure(figsize(20,4))for i in range(20):plt.subplot(2,10,i1)plt.xticks([])plt.yticks([])plt.grid(False)# 显示图片images plt.imread(train_image_paths[i])plt.imshow(images)# 显示标签plt.xlabel(train_image_label[i])plt.show()2. 配置数据集
BATCH_SIZE 6# 将训练数据集拆分成训练集与验证集
train_ds image_label_ds.take(5000).shuffle(1000) # 前1500个batch
val_ds image_label_ds.skip(5000).shuffle(1000) # 跳过前1500选取后面的train_ds train_ds.batch(BATCH_SIZE)
train_ds train_ds.prefetch(buffer_sizeAUTOTUNE)val_ds val_ds.batch(BATCH_SIZE)
val_ds val_ds.prefetch(buffer_sizeAUTOTUNE)
val_ds# 查看数据 shape 进行检查
for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break(6, 299, 299, 3)
(6,)# 再次查看数据确认是否被打乱
plt.figure(figsize(8,8))for images, labels in train_ds.take(1):for i in range(6):ax plt.subplot(4, 3, i 1) plt.imshow(images[i])plt.title(labels[i].numpy()) #使用.numpy()将张量转换为 NumPy 数组plt.axis(off)三、构建Inception-ResNet-v2网络
1.自己搭建
下面是本文的重点 InceptionResNetV2 网络模型的构建可以试着按照上面的图自己构建一下 InceptionResNetV2这部分我主要是参考官网的构建过程将其单独拎了出来。
from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Dense, Flatten, Dropout,BatchNormalization,Activation
from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D, Concatenate, Lambda,GlobalAveragePooling2D
from tensorflow.keras import backend as Kdef conv2d_bn(x,filters,kernel_size,strides1,paddingsame,activationrelu,use_biasFalse,nameNone):x Conv2D(filters,kernel_size,stridesstrides,paddingpadding,use_biasuse_bias,namename)(x)if not use_bias:bn_axis 1 if K.image_data_format() channels_first else 3bn_name None if name is None else name _bnx BatchNormalization(axisbn_axis, scaleFalse, namebn_name)(x)if activation is not None:ac_name None if name is None else name _acx Activation(activation, nameac_name)(x)return xdef inception_resnet_block(x, scale, block_type, block_idx, activationrelu):if block_type block35:branch_0 conv2d_bn(x, 32, 1)branch_1 conv2d_bn(x, 32, 1)branch_1 conv2d_bn(branch_1, 32, 3)branch_2 conv2d_bn(x, 32, 1)branch_2 conv2d_bn(branch_2, 48, 3)branch_2 conv2d_bn(branch_2, 64, 3)branches [branch_0, branch_1, branch_2]elif block_type block17:branch_0 conv2d_bn(x, 192, 1)branch_1 conv2d_bn(x, 128, 1)branch_1 conv2d_bn(branch_1, 160, [1, 7])branch_1 conv2d_bn(branch_1, 192, [7, 1])branches [branch_0, branch_1]elif block_type block8:branch_0 conv2d_bn(x, 192, 1)branch_1 conv2d_bn(x, 192, 1)branch_1 conv2d_bn(branch_1, 224, [1, 3])branch_1 conv2d_bn(branch_1, 256, [3, 1])branches [branch_0, branch_1]else:raise ValueError(Unknown Inception-ResNet block type. Expects block35, block17 or block8, but got: str(block_type))block_name block_type _ str(block_idx)mixed Concatenate(nameblock_name _mixed)(branches)up conv2d_bn(mixed,K.int_shape(x)[3],1,activationNone,use_biasTrue,nameblock_name _conv)x Lambda(lambda inputs, scale: inputs[0] inputs[1] * scale,output_shapeK.int_shape(x)[1:],arguments{scale: scale},nameblock_name)([x, up])if activation is not None:x Activation(activation, nameblock_name _ac)(x)return xdef InceptionResNetV2(input_shape[299,299,3],classes1000):inputs Input(shapeinput_shape)# Stem blockx conv2d_bn(inputs, 32, 3, strides2, paddingvalid)x conv2d_bn(x, 32, 3, paddingvalid)x conv2d_bn(x, 64, 3)x MaxPooling2D(3, strides2)(x)x conv2d_bn(x, 80, 1, paddingvalid)x conv2d_bn(x, 192, 3, paddingvalid)x MaxPooling2D(3, strides2)(x)# Mixed 5b (Inception-A block)branch_0 conv2d_bn(x, 96, 1)branch_1 conv2d_bn(x, 48, 1)branch_1 conv2d_bn(branch_1, 64, 5)branch_2 conv2d_bn(x, 64, 1)branch_2 conv2d_bn(branch_2, 96, 3)branch_2 conv2d_bn(branch_2, 96, 3)branch_pool AveragePooling2D(3, strides1, paddingsame)(x)branch_pool conv2d_bn(branch_pool, 64, 1)branches [branch_0, branch_1, branch_2, branch_pool]x Concatenate(namemixed_5b)(branches)# 10次 Inception-ResNet-A blockfor block_idx in range(1, 11):x inception_resnet_block(x, scale0.17, block_typeblock35, block_idxblock_idx)# Reduction-A blockbranch_0 conv2d_bn(x, 384, 3, strides2, paddingvalid)branch_1 conv2d_bn(x, 256, 1)branch_1 conv2d_bn(branch_1, 256, 3)branch_1 conv2d_bn(branch_1, 384, 3, strides2, paddingvalid)branch_pool MaxPooling2D(3, strides2, paddingvalid)(x)branches [branch_0, branch_1, branch_pool]x Concatenate(namemixed_6a)(branches)# 20次 Inception-ResNet-B blockfor block_idx in range(1, 21):x inception_resnet_block(x, scale0.1, block_typeblock17, block_idxblock_idx)# Reduction-B blockbranch_0 conv2d_bn(x, 256, 1)branch_0 conv2d_bn(branch_0, 384, 3, strides2, paddingvalid)branch_1 conv2d_bn(x, 256, 1)branch_1 conv2d_bn(branch_1, 288, 3, strides2, paddingvalid)branch_2 conv2d_bn(x, 256, 1)branch_2 conv2d_bn(branch_2, 288, 3)branch_2 conv2d_bn(branch_2, 320, 3, strides2, paddingvalid)branch_pool MaxPooling2D(3, strides2, paddingvalid)(x)branches [branch_0, branch_1, branch_2, branch_pool]x Concatenate(namemixed_7a)(branches)# 10次 Inception-ResNet-C blockfor block_idx in range(1, 10):x inception_resnet_block(x, scale0.2, block_typeblock8, block_idxblock_idx)x inception_resnet_block(x, scale1., activationNone, block_typeblock8, block_idx10)x conv2d_bn(x, 1536, 1, nameconv_7b)x GlobalAveragePooling2D(nameavg_pool)(x)x Dense(classes, activationsoftmax, namepredictions)(x)# 创建模型model Model(inputs, x, nameinception_resnet_v2)return modelmodel InceptionResNetV2([299,299,3],58)
model.summary()2.官方模型
# import tensorflow as tf
# # 如果使用官方模型需要将图片shape调整为 [299,299,3]目前图片的shape是 [150,150,3]
# model tf.keras.applications.inception_resnet_v2.InceptionResNetV2()
# model.summary()五、设置动态学习率
这里先罗列一下学习率大与学习率小的优缺点。
学习率大 优点 1、加快学习速率。 2、有助于跳出局部最优值。缺点 1、导致模型训练不收敛。 2、单单使用大学习率容易导致模型不精确。 学习率小 优点 1、有助于模型收敛、模型细化。 2、提高模型精度。缺点 1、很难跳出局部最优值。 2、收敛缓慢。
注意这里设置的动态学习率为指数衰减型ExponentialDecay。在每一个epoch开始前学习率learning_rate都将会重置为初始学习率initial_learning_rate然后再重新开始衰减。计算公式如下
learning_rate initial_learning_rate * decay_rate ^ (step / decay_steps)
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。
model.compile(optimizeroptimizer,losssparse_categorical_crossentropy,metrics[accuracy])六、训练模型
Inception-ResNet-v2 模型相对之前的模型较为复杂故而运行耗时也更长我这边每一个epoch运行时间是130s左右。我的GPU配置是 NVIDIA GeForce RTX 3080。建议大家先将 epochs 调整为1跑通程序。
epochs 10history model.fit(train_ds,validation_dataval_ds,epochsepochs
)Epoch 1/10
834/834 [] - 154s 163ms/step - loss: 2.5214 - accuracy: 0.3563 - val_loss: 1.3834 - val_accuracy: 0.6168
Epoch 2/10
834/834 [] - 133s 159ms/step - loss: 0.9230 - accuracy: 0.7522 - val_loss: 0.5457 - val_accuracy: 0.8531
Epoch 3/10
834/834 [] - 133s 159ms/step - loss: 0.3952 - accuracy: 0.9105 - val_loss: 0.3391 - val_accuracy: 0.9064
Epoch 4/10
834/834 [] - 134s 160ms/step - loss: 0.1876 - accuracy: 0.9655 - val_loss: 0.2481 - val_accuracy: 0.9296
Epoch 5/10
834/834 [] - 131s 156ms/step - loss: 0.1071 - accuracy: 0.9862 - val_loss: 0.1265 - val_accuracy: 0.9716
Epoch 6/10
834/834 [] - 128s 153ms/step - loss: 0.0587 - accuracy: 0.9954 - val_loss: 0.0911 - val_accuracy: 0.9794
Epoch 7/10
834/834 [] - 132s 158ms/step - loss: 0.0429 - accuracy: 0.9976 - val_loss: 0.0941 - val_accuracy: 0.9777
Epoch 8/10
834/834 [] - 132s 158ms/step - loss: 0.0306 - accuracy: 0.9980 - val_loss: 0.0955 - val_accuracy: 0.9777
Epoch 9/10
834/834 [] - 133s 158ms/step - loss: 0.0248 - accuracy: 0.9997 - val_loss: 0.0864 - val_accuracy: 0.9794
Epoch 10/10
834/834 [] - 132s 158ms/step - loss: 0.0216 - accuracy: 0.9988 - val_loss: 0.0750 - val_accuracy: 0.9794七、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()八、模型的保存与加载
# 保存模型
model.save(model/14_model.h5)# 加载模型
new_model keras.models.load_model(model/14_model.h5)九、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(6):ax plt.subplot(2, 3, 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) # 使用模型预测路标predictions new_model.predict(img_array)plt.title(np.argmax(predictions))plt.axis(off)
