直接使用训练好的VGG16测试
实验环境：
1、下载用VGG16在ImageNet数据集上训练好的权重数据 vgg16.npy 链接:https://pan.baidu.com/s/1gg9jLw3 密码:umce
2、下载imagenet_classes.py （1000个类别，tf.argmax 返回值就是imagenet_classes中行号对应的类别），下载地址：http://www.cs.toronto.edu/~frossard/post/vgg16/
3、创建 vgg16_v1.py
4、将一张猫图片（或其他图片），vgg16_v1.py，imagenet_classes.py，vgg16.npy放在同一个文件夹在
5、运行，ok....
vgg16_v1.py如下：
import tensorflow as tf
import numpy as np
import cv2
import imagenet_classes
class vgg16:
def __init__(self, imgs, weights=None, sess=None):
self.imgs = imgs
self.convlayers()
self.fc_layers()
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')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases')
conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases')
conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases')
conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME')
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')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),trainable=True, name='biases')
conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME')
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('fc6') 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('fc7') 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('fc8') 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):
data_dict = np.load(weight_file, encoding='latin1').item()
keys = sorted(data_dict.keys())
# print(len(keys),len(self.parameters))
for i,key in enumerate(keys):
weights = data_dict[key][0]
biases = data_dict[key][1]
# print(i,key,'w=',data_dict[key][0].shape,'b=',data_dict[key][1].shape)
sess.run(self.parameters[2*i].assign(data_dict[key][0]))
sess.run(self.parameters[2*i+1].assign(data_dict[key][1]))
def predict(self):
return tf.argmax(tf.nn.softmax(self.fc3l),1)
if __name__ == '__main__':
weigth='E:/deepLearningModel/vgg16.npy' #我把vgg16.npy放在E:/deepLearningModel/
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
vgg = vgg16(imgs, weigth, sess)
preData = cv2.imread('cat.1.jpg')
img1 =cv2.resize(preData,(224, 224))
prob = sess.run(vgg.predict(), feed_dict={vgg.imgs: [img1]})
print(imagenet_classes.class_names[prob[0]])
结果
