根据Kevin 训练VGG教程而来
假设我们这里已经有了一个VGG16层的参数原始文件,想要打开来看一下参数设置,使用以下函数就行了,涉及到的库自行补充
测试加载进来的参数
def test_load():
data_path = './/VGG-pretrain//vgg16.npy' # 文件保存路径
# 注意这个文件要到网上自行下载
data_dict = np.load(data_path, encoding='latin1').item()
keys = sorted(data_dict.keys())
for key in keys:
weights = data_dict[key][0]
biases = data_dict[key][1]
print('\n')
print(key)
print('weights shape: ', weights.shape)
print('biases shape: ', biases.shape)
结果展示:
conv1_1
weights shape: (3, 3, 3, 64)
biases shape: (64,)
conv1_2
weights shape: (3, 3, 64, 64)
biases shape: (64,)
conv2_1
weights shape: (3, 3, 64, 128)
biases shape: (128,)
conv2_2
weights shape: (3, 3, 128, 128)
biases shape: (128,)
conv3_1
weights shape: (3, 3, 128, 256)
biases shape: (256,)
conv3_2
weights shape: (3, 3, 256, 256)
biases shape: (256,)
conv3_3
weights shape: (3, 3, 256, 256)
biases shape: (256,)
conv4_1
weights shape: (3, 3, 256, 512)
biases shape: (512,)
conv4_2
weights shape: (3, 3, 512, 512)
biases shape: (512,)
conv4_3
weights shape: (3, 3, 512, 512)
biases shape: (512,)
conv5_1
weights shape: (3, 3, 512, 512)
biases shape: (512,)
conv5_2
weights shape: (3, 3, 512, 512)
biases shape: (512,)
conv5_3
weights shape: (3, 3, 512, 512)
biases shape: (512,)
fc6
weights shape: (25088, 4096)
biases shape: (4096,)
fc7
weights shape: (4096, 4096)
biases shape: (4096,)
fc8
weights shape: (4096, 1000)
biases shape: (1000,)