文件名称：基于numpy的VGG网络实现
文件大小：12.65MB
文件格式：ZIP
更新时间：2022-12-17 15:11:42
VGG Numpy 动态绑定
基于numpy的VGG网络前向，后向实现，不使用第三方深度学习计算库，类的实现使用了python动态绑定。
【文件预览】：
checkpoint_(loss_-1.23)_(epoch_4)__[(lr reg)_(-3.0 -4.0)]_ adam L2 ELU.png
optimizer_interface.py
vgg_grad_check.py
vgg_net.py
MNIST
----t10k-images-idx3-ubyte.gz(1.57MB)
----t10k-labels-idx1-ubyte.gz(4KB)
----train-images-idx3-ubyte.gz(9.45MB)
----train-labels-idx1-ubyte.gz(28KB)
__pycache__
----regulation_interface.cpython-35.pyc(1KB)
----cnn_train_interface.cpython-36.pyc(6KB)
----cnn_block_interface.cpython-35.pyc(9KB)
----cnn_train_interface.cpython-35.pyc(6KB)
----cnn_block_interface.cpython-36.pyc(9KB)
----activation_interface.cpython-35.pyc(1KB)
----read_mnist.cpython-36.pyc(1KB)
----activation_interface.cpython-36.pyc(1KB)
----vgg_net.cpython-35.pyc(10KB)
----MNIST_interface.cpython-35.pyc(3KB)
----MNIST_interface.cpython-36.pyc(3KB)
----optimizer_interface.cpython-36.pyc(1KB)
----cnn_layer_interface.cpython-36.pyc(9KB)
----results()
----optimizer_interface.cpython-35.pyc(1KB)
----regulation_interface.cpython-36.pyc(1KB)
----vgg_net.cpython-36.pyc(9KB)
cnn_train_interface.py
checkpoint_(loss_-1.23)_(epoch_4)__[(lr reg)_(-3.0 -4.0)]_ adam L2 ELU.npy
MNIST_interface.py
vgg_test.py
cnn_block_interface.py
activation_interface.py
.spyproject
----codestyle.ini(56B)
----encoding.ini(58B)
----vcs.ini(85B)
----workspace.ini(362B)
.DS_Store
regulation_interface.py