研究相关的图片分类，偶然看到bvlc模型，但是没有tensorflow版本的，所以将caffe版本的改成了tensorflow的:
关于模型这个图：
下面贴出通用模板：
1 from __future__ import print_function
2 import tensorflow as tf
3 import numpy as np
4 from scipy.misc import imread, imresize
5
6
7 class BVLG:
8 def __init__(self, imgs, weights=None, sess=None):
9 self.imgs = imgs
10 self.convlayers()
11 self.fc_layers()
12
13 self.probs = tf.nn.softmax(self.fc3l)
14 if weights is not None and sess is not None:
15 self.load_weights(weights,sess)
16
17 def convlayers(self):
18 self.parameters = []
19
20 # zero-mean input
21 with tf.name_scope('preprocess') as scope:
22 mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
23 images = self.imgs - mean
24
25 # conv1
26 with tf.name_scope('conv1') as scope:
27 kernel = tf.Variable(tf.truncated_normal([7, 7, 3, 96], dtype=tf.float32,
28 stddev=1e-1), name='weights')
29 conv = tf.nn.conv2d(images, kernel, [3, 3, 1, 1], padding='SAME')
30 biases = tf.Variable(tf.constant(0.0, shape=[96], dtype=tf.float32),
31 trainable=True, name='biases')
32 out = tf.nn.bias_add(conv, biases)
33 self.conv1 = tf.nn.relu(out, name=scope)
34 self.parameters += [kernel, biases]
35
36 # pool1
37 self.pool1 = tf.nn.max_pool(self.conv1,
38 ksize=[1, 3, 3, 1],
39 strides=[1, 2, 2, 1],
40 padding='SAME',
41 name='pool1')
42
43 # conv2
44 with tf.name_scope('conv2') as scope:
45 kernel = tf.Variable(tf.truncated_normal([4, 4, 96, 256], dtype=tf.float32,
46 stddev=1e-1), name='weights')
47 conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')
48 biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
49 trainable=True, name='biases')
50 out = tf.nn.bias_add(conv, biases)
51 self.conv2_1 = tf.nn.relu(out, name=scope)
52 self.parameters += [kernel, biases]
53
54
55 # pool2
56 self.pool2 = tf.nn.max_pool(self.conv2,
57 ksize=[1, 3, 3, 1],
58 strides=[1, 2, 2, 1],
59 padding='SAME',
60 name='pool2')
61
62 # conv5
63 with tf.name_scope('conv5') as scope:
64 kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
65 stddev=1e-1), name='weights')
66 conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')
67 biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
68 trainable=True, name='biases')
69 out = tf.nn.bias_add(conv, biases)
70 self.conv5 = tf.nn.relu(out, name=scope)
71 self.parameters += [kernel, biases]
72
73 # pool5
74 self.pool5 = tf.nn.max_pool(self.conv5,
75 ksize=[1, 2, 2, 1],
76 strides=[1, 2, 2, 1],
77 padding='SAME',
78 name='pool4')
79
80 def fc_layers(self):
81 # fc1
82 with tf.name_scope('fc1') as scope:
83 shape = int(np.prod(self.pool5.get_shape()[1:]))
84 fc1w = tf.Variable(tf.truncated_normal([shape, 4096],
85 dtype=tf.float32,
86 stddev=1e-1), name='weights')
87 fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
88 trainable=True, name='biases')
89 pool5_flat = tf.reshape(self.pool5, [-1, shape])
90 fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)
91 self.fc1 = tf.nn.relu(fc1l)
92 self.parameters += [fc1w, fc1b]
93
94 # fc3
95 with tf.name_scope('fc3') as scope:
96 fc3w = tf.Variable(tf.truncated_normal([4096, 587],
97 dtype=tf.float32,
98 stddev=1e-1), name='weights')
99 fc3b = tf.Variable(tf.constant(1.0, shape=[587], dtype=tf.float32),
100 trainable=True, name='biases')
101 self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b)
102 self.parameters += [fc3w, fc3b]
caffe版本的ImageNet模型地址： https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet