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tensorflow预定义经典卷积神经网络和数据集tf.keras.applications_efficientnetv2b0 远程主机强迫关闭了一个现有的连接

efficientnetv2b0 远程主机强迫关闭了一个现有的连接

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1.1  tensorflow预定义经典卷积神经网络和数据集

1.1.1          预定义模型tf.keras.applications

tensorflow有很多已经定义好的模型,而且模型参数已经训练过,可以直接下载模型参数文件,载入参数,使用模型。预定义模型在tf.keras.applications。

  1. # This file is MACHINE GENERATED! Do not edit.
  2. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
  3. """Keras Applications are canned architectures with pre-trained weights.
  4. """
  5. from __future__ import print_function as _print_function
  6. import sys as _sys
  7. from . import densenet
  8. from . import efficientnet
  9. from . import imagenet_utils
  10. from . import inception_resnet_v2
  11. from . import inception_v3
  12. from . import mobilenet
  13. from . import mobilenet_v2
  14. from . import nasnet
  15. from . import resnet
  16. from . import resnet50
  17. from . import resnet_v2
  18. from . import vgg16
  19. from . import vgg19
  20. from . import xception
  21. from tensorflow.python.keras.applications.densenet import DenseNet121
  22. from tensorflow.python.keras.applications.densenet import DenseNet169
  23. from tensorflow.python.keras.applications.densenet import DenseNet201
  24. from tensorflow.python.keras.applications.efficientnet import EfficientNetB0
  25. from tensorflow.python.keras.applications.efficientnet import EfficientNetB1
  26. from tensorflow.python.keras.applications.efficientnet import EfficientNetB2
  27. from tensorflow.python.keras.applications.efficientnet import EfficientNetB3
  28. from tensorflow.python.keras.applications.efficientnet import EfficientNetB4
  29. from tensorflow.python.keras.applications.efficientnet import EfficientNetB5
  30. from tensorflow.python.keras.applications.efficientnet import EfficientNetB6
  31. from tensorflow.python.keras.applications.efficientnet import EfficientNetB7
  32. from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2
  33. from tensorflow.python.keras.applications.inception_v3 import InceptionV3
  34. from tensorflow.python.keras.applications.mobilenet import MobileNet
  35. from tensorflow.python.keras.applications.mobilenet_v2 import MobileNetV2
  36. from tensorflow.python.keras.applications.nasnet import NASNetLarge
  37. from tensorflow.python.keras.applications.nasnet import NASNetMobile
  38. from tensorflow.python.keras.applications.resnet import ResNet101
  39. from tensorflow.python.keras.applications.resnet import ResNet152
  40. from tensorflow.python.keras.applications.resnet import ResNet50
  41. from tensorflow.python.keras.applications.resnet_v2 import ResNet101V2
  42. from tensorflow.python.keras.applications.resnet_v2 import ResNet152V2
  43. from tensorflow.python.keras.applications.resnet_v2 import ResNet50V2
  44. from tensorflow.python.keras.applications.vgg16 import VGG16
  45. from tensorflow.python.keras.applications.vgg19 import VGG19
  46. from tensorflow.python.keras.applications.xception import Xception
  47. del _print_function

预定模型种类说明

可以在官网查看https://keras.io/zh/applications/

 

 

 

 

 

 

模型

大小

Top-1 准确率

Top-5 准确率

参数数量

深度

Xception

88 MB

0.790

0.945

22,910,480

126

VGG16

528 MB

0.713

0.901

138,357,544

23

VGG19

549 MB

0.713

0.900

143,667,240

26

ResNet50

98 MB

0.749

0.921

25,636,712

-

ResNet101

171 MB

0.764

0.928

44,707,176

-

ResNet152

232 MB

0.766

0.931

60,419,944

-

ResNet50V2

98 MB

0.760

0.930

25,613,800

-

ResNet101V2

171 MB

0.772

0.938

44,675,560

-

ResNet152V2

232 MB

0.780

0.942

60,380,648

-

ResNeXt50

96 MB

0.777

0.938

25,097,128

-

ResNeXt101

170 MB

0.787

0.943

44,315,560

-

InceptionV3

92 MB

0.779

0.937

23,851,784

159

InceptionResNetV2

215 MB

0.803

0.953

55,873,736

572

MobileNet

16 MB

0.704

0.895

4,253,864

88

MobileNetV2

14 MB

0.713

0.901

3,538,984

88

DenseNet121

33 MB

0.750

0.923

8,062,504

121

DenseNet169

57 MB

0.762

0.932

14,307,880

169

DenseNet201

80 MB

0.773

0.936

20,242,984

201

NASNetMobile

23 MB

0.744

0.919

5,326,716

-

NASNetLarge

343 MB

0.825

0.960

88,949,818

1.1.2          数据集tensorflow_datasets

(1)安装方式,可以打开anaconda界面,用命令行去添加,也可以直接在pycharm里面为环境添加。

 

输入activate tensoflow 激活环境

然后输入pip install tensorflow_datasets 安装数据集库

(2)或者在pycharm里面安装库

点击pycharm工程的file-》setting

 

在输入框内输入tensorflow_datasets,出现安装库,然后选中列表中的tensorflow_datasets,点击左下角的install Package

 

之后再py文件中输入import tensorflow_datasets as dataset就可以使用数据集了。

(3)运行出现HDF5库和h5py版本不匹配的问题

安装完成后开始编译下载数据,出现如下错误

h5py is running against HDF5 1.10.5 when it was built against 1.10.6, this may cause problems

因为下载数据用到的HDF5和h5py的版本冲突不匹配,可以在anaconda中先输入pip uninstall h5py 然后再输入pip install h5py。下载最新版本的h5py,问题解决。

(4)tensorflow_datasets 包含的数据集名称

可以用下面的语句打印显示

import tensorflow_datasets as data

print(data.list_builders())#打印显示所有的数据集名称,用load加载

['abstract_reasoning', 'accentdb', 'aeslc', 'aflw2k3d', 'ag_news_subset', 'ai2_arc', 'ai2_arc_with_ir', 'amazon_us_reviews', 'anli', 'arc', 'bair_robot_pushing_small', 'bccd', 'beans', 'big_patent', 'bigearthnet', 'billsum', 'binarized_mnist', 'binary_alpha_digits', 'blimp', 'bool_q', 'c4', 'caltech101', 'caltech_birds2010', 'caltech_birds2011', 'cars196', 'cassava', 'cats_vs_dogs', 'celeb_a', 'celeb_a_hq', 'cfq', 'cherry_blossoms', 'chexpert', 'cifar10', 'cifar100', 'cifar10_1', 'cifar10_corrupted', 'citrus_leaves', 'cityscapes', 'civil_comments', 'clevr', 'clic', 'clinc_oos', 'cmaterdb', 'cnn_dailymail', 'coco', 'coco_captions', 'coil100', 'colorectal_histology', 'colorectal_histology_large', 'common_voice', 'coqa', 'cos_e', 'cosmos_qa', 'covid19sum', 'crema_d', 'curated_breast_imaging_ddsm', 'cycle_gan', 'd4rl_mujoco_ant', 'd4rl_mujoco_halfcheetah', 'dart', 'davis', 'deep_weeds', 'definite_pronoun_resolution', 'dementiabank', 'diabetic_retinopathy_detection', 'div2k', 'dmlab', 'dolphin_number_word', 'downsampled_imagenet', 'drop', 'dsprites', 'dtd', 'duke_ultrasound', 'e2e_cleaned', 'efron_morris75', 'emnist', 'eraser_multi_rc', 'esnli', 'eurosat', 'fashion_mnist', 'flic', 'flores', 'food101', 'forest_fires', 'fuss', 'gap', 'geirhos_conflict_stimuli', 'gem', 'genomics_ood', 'german_credit_numeric', 'gigaword', 'glue', 'goemotions', 'gpt3', 'gref', 'groove', 'gtzan', 'gtzan_music_speech', 'hellaswag', 'higgs', 'horses_or_humans', 'howell', 'i_naturalist2017', 'imagenet2012', 'imagenet2012_corrupted', 'imagenet2012_real', 'imagenet2012_subset', 'imagenet_a', 'imagenet_r', 'imagenet_resized', 'imagenet_v2', 'imagenette', 'imagewang', 'imdb_reviews', 'irc_disentanglement', 'iris', 'kitti', 'kmnist', 'lambada', 'lfw', 'librispeech', 'librispeech_lm', 'libritts', 'ljspeech', 'lm1b', 'lost_and_found', 'lsun', 'lvis', 'malaria', 'math_dataset', 'mctaco', 'mlqa', 'mnist', 'mnist_corrupted', 'movie_lens', 'movie_rationales', 'movielens', 'moving_mnist', 'multi_news', 'multi_nli', 'multi_nli_mismatch', 'natural_questions', 'natural_questions_open', 'newsroom', 'nsynth', 'nyu_depth_v2', 'ogbg_molpcba', 'omniglot', 'open_images_challenge2019_detection', 'open_images_v4', 'openbookqa', 'opinion_abstracts', 'opinosis', 'opus', 'oxford_flowers102', 'oxford_iiit_pet', 'para_crawl', 'patch_camelyon', 'paws_wiki', 'paws_x_wiki', 'pet_finder', 'pg19', 'piqa', 'places365_small', 'plant_leaves', 'plant_village', 'plantae_k', 'qa4mre', 'qasc', 'quac', 'quickdraw_bitmap', 'race', 'radon', 'reddit', 'reddit_disentanglement', 'reddit_tifu', 'resisc45', 'robonet', 'rock_paper_scissors', 'rock_you', 's3o4d', 'salient_span_wikipedia', 'samsum', 'savee', 'scan', 'scene_parse150', 'schema_guided_dialogue', 'scicite', 'scientific_papers', 'sentiment140', 'shapes3d', 'siscore', 'smallnorb', 'snli', 'so2sat', 'speech_commands', 'spoken_digit', 'squad', 'stanford_dogs', 'stanford_online_products', 'star_cfq', 'starcraft_video', 'stl10', 'story_cloze', 'sun397', 'super_glue', 'svhn_cropped', 'tao', 'ted_hrlr_translate', 'ted_multi_translate', 'tedlium', 'tf_flowers', 'the300w_lp', 'tiny_shakespeare', 'titanic', 'trec', 'trivia_qa', 'tydi_qa', 'uc_merced', 'ucf101', 'vctk', 'vgg_face2', 'visual_domain_decathlon', 'voc', 'voxceleb', 'voxforge', 'waymo_open_dataset', 'web_nlg', 'web_questions', 'wider_face', 'wiki40b', 'wiki_bio', 'wiki_table_questions', 'wiki_table_text', 'wikiann', 'wikihow', 'wikipedia', 'wikipedia_toxicity_subtypes', 'wine_quality', 'winogrande', 'wmt13_translate', 'wmt14_translate', 'wmt15_translate', 'wmt16_translate', 'wmt17_translate', 'wmt18_translate', 'wmt19_translate', 'wmt_t2t_translate', 'wmt_translate', 'wordnet', 'wsc273', 'xnli', 'xquad', 'xsum', 'xtreme_pawsx', 'xtreme_xnli', 'yelp_polarity_reviews', 'yes_no', 'youtube_vis']

1.1.3          预定义模型和数据集使用实例

  1. import tensorflow as tf
  2. import tensorflow_datasets as data
  3. #(3)定义训练参数和模型对象,数据集对象
  4. num_epochs = 5
  5. batch_size = 19#一批数据的数量
  6. learning_rate = 0.001#学习率
  7. #根据第一个参数名称来下载数据集
  8. print(data.list_builders())#打印显示所有的数据集名称,用load加载
  9. dataset = data.load("tf_flowers",split=data.Split.TRAIN,as_supervised=True)#创建数据源对象,下载数据
  10. dataset=dataset.map(lambda img,label:(tf.image.resize(img,(224,224))/255.0,label)).shuffle(1024).batch(batch_size)
  11. model = tf.keras.applications.MobileNetV2(weights=None,classes=5)#创建模型
  12. optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)#创建优化器,用于参数学习优化
  13. #开始训练参数
  14. #arryindex=np.arange(num_batches)
  15. #arryloss=np.zeros(num_batches)
  16. #通过梯度下降法对模型参数进行训练,优化模型
  17. for e in range(num_epochs):
  18. for images,labels in dataset:
  19. with tf.GradientTape() as tape:
  20. label_pred=model(images,trainable=True)
  21. # 计算损失函数
  22. loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=labels, y_pred=label_pred)
  23. # 计算损失函数的均方根值,表示误差大小
  24. loss = tf.reduce_mean(loss)
  25. print("第%d次训练后:误差%f" % (batch_index, loss.numpy()))
  26. grads = tape.gradient(loss, model.variables)
  27. # 将梯度值调整模型参数
  28. print(label_pred)
  29. optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))

运行下载数据出现断开连接的错误

Connection broken: ConnectionResetError(10054, '远程主机强迫关闭了一个现有的连接。', None, 10054, None)"

网上因为下载数据太多,时间超时,远程主机以为是受到攻击,自动断开。没找到解决方法。

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