vgg16 包括网络参数的导入

1. vgg16 含有导入网络参数

2.值得学习的部分为:

2.1 参数移植, 但不太了解assign的用法

sess.run(self.parameters[i].assign(weights[k]))

2.2   两个矩阵拼接

self.parameters += [fc2w, fc2b]

2.3  内容排序,下面是以keys中名称字母和汉字顺序排序

keys = sorted(weights.keys())

2.4  遍历的读取keys中的内容

 for i, k in enumerate(keys):
            print (i, k, np.shape(weights[k]))
            sess.run(self.parameters[i].assign(weights[k]))

3. 整体代码为

######################################################################################### Davi Frossard, 2016                                                                  ## VGG16 implementation in TensorFlow                                                   ## Details:                                                                             ## http://www.cs.toronto.edu/~frossard/post/vgg16/                                      ##                                                                                      ## Model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md     ## Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow      #########################################################################################import tensorflow as tfimport numpy as npfrom scipy.misc import imread, imresizefrom imagenet_classes import class_namesclass vgg16:    def __init__(self, imgs, weights=None, sess=None):        self.imgs = imgs        self.convlayers()        self.fc_layers()        self.probs = tf.nn.softmax(self.fc3l)        if weights is not None and sess is not None:            self.load_weights(weights, sess)    def convlayers(self):        self.parameters = []        # zero-mean input        with tf.name_scope('preprocess') as scope:            mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')            images = self.imgs-mean        # conv1_1        with tf.name_scope('conv1_1') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv1_1 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv1_2        with tf.name_scope('conv1_2') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv1_2 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # pool1        self.pool1 = tf.nn.max_pool(self.conv1_2,                               ksize=[1, 2, 2, 1],                               strides=[1, 2, 2, 1],                               padding='SAME',                               name='pool1')        # conv2_1        with tf.name_scope('conv2_1') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv2_1 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv2_2        with tf.name_scope('conv2_2') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv2_2 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # pool2        self.pool2 = tf.nn.max_pool(self.conv2_2,                               ksize=[1, 2, 2, 1],                               strides=[1, 2, 2, 1],                               padding='SAME',                               name='pool2')        # conv3_1        with tf.name_scope('conv3_1') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv3_1 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv3_2        with tf.name_scope('conv3_2') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv3_2 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv3_3        with tf.name_scope('conv3_3') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv3_3 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # pool3        self.pool3 = tf.nn.max_pool(self.conv3_3,                               ksize=[1, 2, 2, 1],                               strides=[1, 2, 2, 1],                               padding='SAME',                               name='pool3')        # conv4_1        with tf.name_scope('conv4_1') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv4_1 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv4_2        with tf.name_scope('conv4_2') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv4_2 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv4_3        with tf.name_scope('conv4_3') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv4_3 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # pool4        self.pool4 = tf.nn.max_pool(self.conv4_3,                               ksize=[1, 2, 2, 1],                               strides=[1, 2, 2, 1],                               padding='SAME',                               name='pool4')        # conv5_1        with tf.name_scope('conv5_1') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv5_1 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv5_2        with tf.name_scope('conv5_2') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv5_2 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # conv5_3        with tf.name_scope('conv5_3') as scope:            kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,                                                     stddev=1e-1), name='weights')            conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME')            biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),                                 trainable=True, name='biases')            out = tf.nn.bias_add(conv, biases)            self.conv5_3 = tf.nn.relu(out, name=scope)            self.parameters += [kernel, biases]        # pool5        self.pool5 = tf.nn.max_pool(self.conv5_3,                               ksize=[1, 2, 2, 1],                               strides=[1, 2, 2, 1],                               padding='SAME',                               name='pool4')    def fc_layers(self):        # fc1        with tf.name_scope('fc1') as scope:            shape = int(np.prod(self.pool5.get_shape()[1:]))            fc1w = tf.Variable(tf.truncated_normal([shape, 4096],                                                         dtype=tf.float32,                                                         stddev=1e-1), name='weights')            fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),                                 trainable=True, name='biases')            pool5_flat = tf.reshape(self.pool5, [-1, shape])            fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)            self.fc1 = tf.nn.relu(fc1l)            self.parameters += [fc1w, fc1b]        # fc2        with tf.name_scope('fc2') as scope:            fc2w = tf.Variable(tf.truncated_normal([4096, 4096],                                                         dtype=tf.float32,                                                         stddev=1e-1), name='weights')            fc2b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),                                 trainable=True, name='biases')            fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b)            self.fc2 = tf.nn.relu(fc2l)            self.parameters += [fc2w, fc2b]        # fc3        with tf.name_scope('fc3') as scope:            fc3w = tf.Variable(tf.truncated_normal([4096, 1000],                                                         dtype=tf.float32,                                                         stddev=1e-1), name='weights')            fc3b = tf.Variable(tf.constant(1.0, shape=[1000], dtype=tf.float32),                                 trainable=True, name='biases')            self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b)            self.parameters += [fc3w, fc3b]    def load_weights(self, weight_file, sess):        weights = np.load(weight_file)        keys = sorted(weights.keys())        for i, k in enumerate(keys):            print (i, k, np.shape(weights[k]))            sess.run(self.parameters[i].assign(weights[k]))if __name__ == '__main__':    sess = tf.Session()    imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])    vgg = vgg16(imgs, 'vgg16_weights.npz', sess)    img1 = imread('laska.png', mode='RGB')    img1 = imresize(img1, (224, 224))    prob = sess.run(vgg.probs, feed_dict={vgg.imgs: [img1]})[0]    preds = (np.argsort(prob)[::-1])[0:5]    for p in preds:        print (class_names[p], prob[p])#down load websiet: https://www.cs.toronto.edu/~frossard/