import tensorflow as tf
 
import numpy as np
import scipy.io
 
data_path = 'model/vgg19.mat' # data_path指下载下来的Vgg19预训练模型的文件地址
 
# 读取Vgg19文件
data = scipy.io.loadmat(data_path)
# 打印Vgg19的数据类型及其组成
print("type: ", type(data))
print("data.keys: ", data.keys())
 
# 得到对应卷积核的矩阵
weights = data['layers'][0]
# 定义Vgg19的组成
layers = (
 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
 
 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
 
 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
 'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
 
 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
 
 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
 'relu5_3', 'conv5_4', 'relu5_4'
)
 
# 打印Vgg19不同卷积层所对应的维度
for i, name in enumerate(layers):
 kind = name[:4]
 if kind == 'conv':
  print("%s: %s" % (name, weights[i][0][0][2][0][0].shape))
 elif kind == 'relu':
  print(name)
 elif kind == 'pool':
  print(name)
 
 
代码输出结果如下：
type: <class 'dict'>
data.keys: dict_keys(['__header__', '__version__', '__globals__', 'layers', 'meta'])
 
conv1_1: (3, 3, 3, 64)
relu1_1
conv1_2: (3, 3, 64, 64)
relu1_2
pool1
conv2_1: (3, 3, 64, 128)
relu2_1
conv2_2: (3, 3, 128, 128)
relu2_2
pool2
conv3_1: (3, 3, 128, 256)
relu3_1
conv3_2: (3, 3, 256, 256)
relu3_2
conv3_3: (3, 3, 256, 256)
relu3_3
conv3_4: (3, 3, 256, 256)
relu3_4
pool3
conv4_1: (3, 3, 256, 512)
relu4_1
conv4_2: (3, 3, 512, 512)
relu4_2
conv4_3: (3, 3, 512, 512)
relu4_3
conv4_4: (3, 3, 512, 512)
relu4_4
pool4
conv5_1: (3, 3, 512, 512)
relu5_1
conv5_2: (3, 3, 512, 512)
relu5_2
conv5_3: (3, 3, 512, 512)
relu5_3
conv5_4: (3, 3, 512, 512)
relu5_4

def _conv_layer(input, weights, bias):
 conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),
   padding='SAME')
 return tf.nn.bias_add(conv, bias)
 
def _pool_layer(input):
 return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
   padding='SAME')
 
class VGG19:
 layers = (
  'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
 
  'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
 
  'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
  'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
 
  'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
  'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
 
  'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
  'relu5_3', 'conv5_4', 'relu5_4'
 )
 
 def __init__(self, data_path):
  data = scipy.io.loadmat(data_path)
 
  self.weights = data['layers'][0]
 
 def feed_forward(self, input_image, scope=None):
  # 定义net用来保存模型每一步输出的特征图
  net = {}
  current = input_image
 
  with tf.variable_scope(scope):
   for i, name in enumerate(self.layers):
    kind = name[:4]
    if kind == 'conv':
     kernels = self.weights[i][0][0][2][0][0]
     bias = self.weights[i][0][0][2][0][1]
 
     kernels = np.transpose(kernels, (1, 0, 2, 3))
     bias = bias.reshape(-1)
 
     current = _conv_layer(current, kernels, bias)
    elif kind == 'relu':
     current = tf.nn.relu(current)
    elif kind == 'pool':
     current = _pool_layer(current)
    # 在每一步都保存当前输出的特征图
    net[name] = current
 
  return net

image_path = "data/test.jpg" # 本地的测试图片
 
image_raw = tf.gfile.GFile(image_path, 'rb').read()
# 一定要tf.float()，否则会报错
image_decoded = tf.to_float(tf.image.decode_jpeg(image_raw))
 
# 扩展图片的维度，从三维变成四维，符合Vgg19的输入接口
image_expand_dim = tf.expand_dims(image_decoded, 0)
 
# 定义Vgg19模型
vgg19 = VGG19(data_path)
net = vgg19.feed_forward(image_expand_dim, 'vgg19')
print(net)
 
代码结果如下所示：
{'conv1_1': <tf.Tensor 'vgg19_1/BiasAdd:0' shape=(1, "_blank" href="https://www.jb51.net/article/187355.htm">Tensorflow加载Vgg预训练模型的几个注意事项。

到这里，如何使用tensorflow读取Vgg19模型结束了，若是大家有其他疑惑，可在评论区留言，会定时回答。
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