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