走了几个坑把VGG16导入到模型中，在此记录下代码，以备不时之需。

代码运行所需文件：

vgg_16.ckpt

1.jpg (224 x 224 x 3)

import tensorflow as tf

from tensorflow.python import pywrap_tensorflow

vgg16_ckpt_path = 'vgg_16.ckpt'

def load_model(input,model_path=vgg16_ckpt_path):

net = {}

reader = pywrap_tensorflow.NewCheckpointReader(model_path)

vgg_variable = reader.get_variable_to_shape_map()

keys = sorted(vgg_variable)

# Print tensor name and values

# for key in keys:

# if key > 'vgg_16/conv6':

# print("tensor_name: ", key,reader.get_tensor(key).shape)

# conv1_1

net['conv1_1'] = tf.nn.conv2d(input,reader.get_tensor('vgg_16/conv1/conv1_1/weights'),[1,1,1,1],padding='SAME')

net['conv1_1'] = tf.nn.bias_add(net['conv1_1'],reader.get_tensor('vgg_16/conv1/conv1_1/biases'))

net['conv1_1'] = tf.nn.relu(net['conv1_1'])

# conv1_2

net['conv1_2'] = tf.nn.conv2d(net['conv1_1'],reader.get_tensor('vgg_16/conv1/conv1_2/weights'),[1,1,1,1],padding='SAME')

net['conv1_2'] = tf.nn.bias_add(net['conv1_2'],reader.get_tensor('vgg_16/conv1/conv1_2/biases'))

net['conv1_2'] = tf.nn.relu(net['conv1_2'])

#pool1

net['pool1'] = tf.nn.max_pool(net['conv1_2'],ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

# conv2_1

net['conv2_1'] = tf.nn.conv2d(net['pool1'],reader.get_tensor('vgg_16/conv2/conv2_1/weights'),[1,1,1,1],padding='SAME')

net['conv2_1'] = tf.nn.bias_add(net['conv2_1'],reader.get_tensor('vgg_16/conv2/conv2_1/biases'))

net['conv2_1'] = tf.nn.relu(net['conv2_1'])

# conv2_2

net['conv2_2'] = tf.nn.conv2d(net['conv2_1'],reader.get_tensor('vgg_16/conv2/conv2_2/weights'),[1,1,1,1],padding='SAME')

net['conv2_2'] = tf.nn.bias_add(net['conv2_2'],reader.get_tensor('vgg_16/conv2/conv2_2/biases'))

net['conv2_2'] = tf.nn.relu(net['conv2_2'])

#pool2

net['pool2'] = tf.nn.max_pool(net['conv2_2'],ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

# conv3_1

net['conv3_1'] = tf.nn.conv2d(net['pool2'],reader.get_tensor('vgg_16/conv3/conv3_1/weights'),[1,1,1,1],padding='SAME')

net['conv3_1'] = tf.nn.bias_add(net['conv3_1'],reader.get_tensor('vgg_16/conv3/conv3_1/biases'))

net['conv3_1'] = tf.nn.relu(net['conv3_1'])

# conv3_2

net['conv3_2'] = tf.nn.conv2d(net['conv3_1'],reader.get_tensor('vgg_16/conv3/conv3_2/weights'),[1,1,1,1],padding='SAME')

net['conv3_2'] = tf.nn.bias_add(net['conv3_2'],reader.get_tensor('vgg_16/conv3/conv3_2/biases'))

net['conv3_2'] = tf.nn.relu(net['conv3_2'])

# conv3_3

net['conv3_3'] = tf.nn.conv2d(net['conv3_2'],reader.get_tensor('vgg_16/conv3/conv3_3/weights'),[1,1,1,1],padding='SAME')

net['conv3_3'] = tf.nn.bias_add(net['conv3_3'],reader.get_tensor('vgg_16/conv3/conv3_3/biases'))

net['conv3_3'] = tf.nn.relu(net['conv3_3'])

#pool3

net['pool3'] = tf.nn.max_pool(net['conv3_3'],ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

# conv4_1

net['conv4_1'] = tf.nn.conv2d(net['pool3'],reader.get_tensor('vgg_16/conv4/conv4_1/weights'),[1,1,1,1],padding='SAME')

net['conv4_1'] = tf.nn.bias_add(net['conv4_1'],reader.get_tensor('vgg_16/conv4/conv4_1/biases'))

net['conv4_1'] = tf.nn.relu(net['conv4_1'])

# conv4_2

net['conv4_2'] = tf.nn.conv2d(net['conv4_1'],reader.get_tensor('vgg_16/conv4/conv4_2/weights'),[1,1,1,1],padding='SAME')

net['conv4_2'] = tf.nn.bias_add(net['conv4_2'],reader.get_tensor('vgg_16/conv4/conv4_2/biases'))

net['conv4_2'] = tf.nn.relu(net['conv4_2'])

# conv4_3

net['conv4_3'] = tf.nn.conv2d(net['conv4_2'],reader.get_tensor('vgg_16/conv4/conv4_3/weights'),[1,1,1,1],padding='SAME')

net['conv4_3'] = tf.nn.bias_add(net['conv4_3'],reader.get_tensor('vgg_16/conv4/conv4_3/biases'))

net['conv4_3'] = tf.nn.relu(net['conv4_3'])

#pool4

net['pool4'] = tf.nn.max_pool(net['conv4_3'],ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

# conv5_1

net['conv5_1'] = tf.nn.conv2d(net['pool4'],reader.get_tensor('vgg_16/conv5/conv5_1/weights'),[1,1,1,1],padding='SAME')

net['conv5_1'] = tf.nn.bias_add(net['conv5_1'],reader.get_tensor('vgg_16/conv5/conv5_1/biases'))

net['conv5_1'] = tf.nn.relu(net['conv5_1'])

# conv5_2

net['conv5_2'] = tf.nn.conv2d(net['conv5_1'],reader.get_tensor('vgg_16/conv5/conv5_2/weights'),[1,1,1,1],padding='SAME')

net['conv5_2'] = tf.nn.bias_add(net['conv5_2'],reader.get_tensor('vgg_16/conv5/conv5_2/biases'))

net['conv5_2'] = tf.nn.relu(net['conv5_2'])

# conv5_3

net['conv5_3'] = tf.nn.conv2d(net['conv5_2'],reader.get_tensor('vgg_16/conv5/conv5_3/weights'),[1,1,1,1],padding='SAME')

net['conv5_3'] = tf.nn.bias_add(net['conv5_3'],reader.get_tensor('vgg_16/conv5/conv5_3/biases'))

net['conv5_3'] = tf.nn.relu(net['conv5_3'])

# pool5

net['pool5'] = tf.nn.max_pool(net['conv5_3'],ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

p5_shape = net['pool5'].shape

#print(net['pool5'].shape)

net['reshape'] = tf.reshape(net['pool5'],shape=[-1,p5_shape[1] * p5_shape[2] * p5_shape[3]])

# fc6

net['fc6'] = tf.matmul(net['reshape'],tf.reshape(reader.get_tensor('vgg_16/fc6/weights'),

shape=[p5_shape[1] * p5_shape[2] * p5_shape[3],4096]))

net['fc6'] = tf.add(net['fc6'],reader.get_tensor('vgg_16/fc6/biases'))

net['fc6'] = tf.nn.relu(tf.nn.dropout(net['fc6'],keep_prob=0.5))

# fc7

net['fc7'] = tf.matmul(net['fc6'],tf.reshape(reader.get_tensor('vgg_16/fc7/weights'),

shape=[4096,4096]))

net['fc7'] = tf.add(net['fc7'],reader.get_tensor('vgg_16/fc7/biases'))

net['fc7'] = tf.nn.relu(tf.nn.dropout(net['fc7'],keep_prob=0.5))

# fc8

net['fc8'] = tf.matmul(net['fc7'],tf.reshape(reader.get_tensor('vgg_16/fc8/weights'),

shape=[4096,1000]))

net['fc8'] = tf.add(net['fc8'],reader.get_tensor('vgg_16/fc8/biases'))

net['fc8'] = tf.nn.relu(net['fc8'])

softmax = tf.nn.softmax(net['fc8'])

predictions = tf.argmax(softmax, 1)

return net,predictions

def main():

img_path = '1.jpg'

with tf.Graph().as_default():

with tf.Session() as sess:

img_bytes = tf.read_file(img_path)

png = img_path.lower().endswith('png')

image = tf.image.decode_png(img_bytes, channels=3) if png else tf.image.decode_jpeg(img_bytes, channels=3)

image = tf.to_float(sess.run(image))

image = tf.expand_dims(image, 0)

print(image.shape)

if image.shape != (1,224,224,3):

print("图像大小不符合224 x 224 x 3")

net,predictions = load_model(image)

sess.run(tf.global_variables_initializer())

print(sess.run(predictions))

if __name__ == '__main__':

main()

我使用测试的图片：

image

输出是[340]

在 imagenet 1000 class id 可以查到340对应的是 'zebra' 。

じゃね