文件名称：十种流行网络在cifar-10数据集上的应用
文件大小：1.4MB
文件格式：ZIP
更新时间：2021-05-03 13:09:48
cifar，tf
实验环境： - Python (3.5.2) - Keras (2.1.3) - tensorflow-gpu (1.4.1) 十种方法： - [LeNet-5 - Yann LeCun][2] - [Network In Network][3] - [Very Deep Convolutional Networks for Large-Scale Image Recognition][4] - [Deep Residual Learning for Image Recognition][5] - [Identity Mappings in Deep Residual Networks][6] - [Wide Residual Networks][7] - [Aggregated Residual Transformations for Deep Neural Networks][8] - [Densely Connected Convolutional Networks][9] - [Squeeze-and-Excitation Networks][10]
【文件预览】：
cifar-10-cnn-master
----9_Multi-GPU()
--------densenet_multi_gpu.py(6KB)
----3_Vgg19_Network()
--------Vgg19_keras.py(7KB)
--------Vgg_prediction.py(2KB)
--------test_pic()
----8_SENet()
--------SENet_Keras.py(6KB)
----images()
--------results.jpg(32KB)
--------cf10.png(232KB)
----7_DenseNet()
--------DenseNet_keras.py(5KB)
----doc()
--------PyTorch-install.md(6KB)
--------img()
--------Ubuntu-install-cuda-tensorflow.md(5KB)
--------OpenAI-gym-install.md(3KB)
----6_ResNeXt()
--------ResNeXt_keras.py(5KB)
----LICENSE(1KB)
----5_Wide_Residual_Network()
--------Wide_ResNet_keras.py(5KB)
----README.md(7KB)
----Tensorflow_version()
--------Network_in_Network_bn.py(9KB)
--------vgg_19_pretrain.py(12KB)
--------vgg_19.py(13KB)
--------Network_in_Network.py(9KB)
--------data_utility.py(6KB)
----4_Residual_Network()
--------ResNet_keras.py(7KB)
----2_Network_in_Network()
--------nin()
--------Network_in_Network_bn_keras.py(5KB)
--------Network_in_Network_keras.py(4KB)
--------nin_bn()
----1_Lecun_Network()
--------LeNet_dp_da_wd_keras.py(3KB)
--------LeNet_keras.py(2KB)
--------LeNet_dp_keras.py(2KB)
--------LeNet_dp_da_keras.py(3KB)
----_config.yml(29B)
----for_girl()
--------02_set_momory.py(2KB)
--------03_save_pic.py(2KB)
--------01_print_summary.py(2KB)
--------00_original.py(2KB)