1.VGG-16介绍

vgg是在Very Deep Convolutional Networks for Large-Scale Image Recognition期刊上提出的。模型可以达到92.7%的测试准确度,在ImageNet的前5位。它的数据集包括1400万张图像，1000个类别。
vgg-16是一种深度卷积神经网络模型，16表示其深度，在图像分类等任务中取得了不错的效果。
vgg16 的宏观结构图如下。代码定义在tensorflow的vgg16.py文件。注意，包括一个预处理层，使用RGB图像在0-255范围内的像素值减去平均值（在整个ImageNet图像训练集计算）。

2. 文件组成

模型权重 - vgg16_weights.npz
TensorFlow模型- vgg16.py
类名（输出模型到类名的映射） - imagenet_classes.py
示例图片输入 - laska.png
我们使用特定的工具转换了原作者在GitHub profile上公开可用的Caffe权重，并做了一些后续处理，以确保模型符合TensorFlow标准。最终实现可用的权重文件vgg16_weights.npz
下载所有的文件到同一文件夹下，然后运行 python vgg16.py

- vgg16.py文件代码：

import tensorflow as tf
import numpy as np
from scipy.misc import imread, imresize
from imagenet_classes import class_namesclass vgg16:def __init__(self, imgs, weights=None, sess=None):self.imgs = imgsself.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 inputwith 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_1with 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_2with 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]# pool1self.pool1 = tf.nn.max_pool(self.conv1_2,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME',name='pool1')# conv2_1with 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_2with 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]# pool2self.pool2 = tf.nn.max_pool(self.conv2_2,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME',name='pool2')# conv3_1with 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_2with 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_3with 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]# pool3self.pool3 = tf.nn.max_pool(self.conv3_3,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME',name='pool3')# conv4_1with 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_2with 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_3with 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]# pool4self.pool4 = tf.nn.max_pool(self.conv4_3,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME',name='pool4')# conv5_1with 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_2with 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_3with 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]# pool5self.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):# fc1with 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]# fc2with 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]# fc3with 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]print("class_name {}: step {}".format(class_names[p], prob[p]))

运行，测试

测试1：

输入图片为laska.png

运行结果：

2018-03-23 11:04:38.311802: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-03-23 11:04:38.311873: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0 conv1_1_W (3, 3, 3, 64)
1 conv1_1_b (64,)
2 conv1_2_W (3, 3, 64, 64)
3 conv1_2_b (64,)
4 conv2_1_W (3, 3, 64, 128)
5 conv2_1_b (128,)
6 conv2_2_W (3, 3, 128, 128)
7 conv2_2_b (128,)
8 conv3_1_W (3, 3, 128, 256)
9 conv3_1_b (256,)
10 conv3_2_W (3, 3, 256, 256)
11 conv3_2_b (256,)
12 conv3_3_W (3, 3, 256, 256)
13 conv3_3_b (256,)
14 conv4_1_W (3, 3, 256, 512)
15 conv4_1_b (512,)
16 conv4_2_W (3, 3, 512, 512)
17 conv4_2_b (512,)
18 conv4_3_W (3, 3, 512, 512)
19 conv4_3_b (512,)
20 conv5_1_W (3, 3, 512, 512)
21 conv5_1_b (512,)
22 conv5_2_W (3, 3, 512, 512)
23 conv5_2_b (512,)
24 conv5_3_W (3, 3, 512, 512)
25 conv5_3_b (512,)
26 fc6_W (25088, 4096)
27 fc6_b (4096,)
28 fc7_W (4096, 4096)
29 fc7_b (4096,)
30 fc8_W (4096, 1000)
31 fc8_b (1000,)
class_name **weasel**: step 0.693385839462
class_name polecat, fitch, foulmart, foumart, Mustela putorius: step 0.175387635827
class_name mink: step 0.12208583951
class_name black-footed ferret, ferret, Mustela nigripes: step 0.00887066219002
class_name otter: step 0.000121083263366

分类结果为weasel

测试2：
输入图片为多场景

运行结果为：

2018-03-23 11:15:22.718228: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-03-23 11:15:22.718297: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0 conv1_1_W (3, 3, 3, 64)
1 conv1_1_b (64,)
2 conv1_2_W (3, 3, 64, 64)
3 conv1_2_b (64,)
4 conv2_1_W (3, 3, 64, 128)
5 conv2_1_b (128,)
6 conv2_2_W (3, 3, 128, 128)
7 conv2_2_b (128,)
8 conv3_1_W (3, 3, 128, 256)
9 conv3_1_b (256,)
10 conv3_2_W (3, 3, 256, 256)
11 conv3_2_b (256,)
12 conv3_3_W (3, 3, 256, 256)
13 conv3_3_b (256,)
14 conv4_1_W (3, 3, 256, 512)
15 conv4_1_b (512,)
16 conv4_2_W (3, 3, 512, 512)
17 conv4_2_b (512,)
18 conv4_3_W (3, 3, 512, 512)
19 conv4_3_b (512,)
20 conv5_1_W (3, 3, 512, 512)
21 conv5_1_b (512,)
22 conv5_2_W (3, 3, 512, 512)
23 conv5_2_b (512,)
24 conv5_3_W (3, 3, 512, 512)
25 conv5_3_b (512,)
26 fc6_W (25088, 4096)
27 fc6_b (4096,)
28 fc7_W (4096, 4096)
29 fc7_b (4096,)
30 fc8_W (4096, 1000)
31 fc8_b (1000,)
class_name alp: step 0.830908000469
class_name church, church building: step 0.0817768126726
class_name castle: step 0.024959910661
class_name valley, vale: step 0.0158758834004
class_name monastery: step 0.0100631769747

分类结果把高山，教堂，城堡，山谷，修道院都识别出来了，效果非常不错，虽然各种精度不高，但是类别是齐全的。
测试3：
输入图片为

运行结果为

2018-03-23 11:34:50.490069: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-03-23 11:34:50.490137: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0 conv1_1_W (3, 3, 3, 64)
1 conv1_1_b (64,)
2 conv1_2_W (3, 3, 64, 64)
3 conv1_2_b (64,)
4 conv2_1_W (3, 3, 64, 128)
5 conv2_1_b (128,)
6 conv2_2_W (3, 3, 128, 128)
7 conv2_2_b (128,)
8 conv3_1_W (3, 3, 128, 256)
9 conv3_1_b (256,)
10 conv3_2_W (3, 3, 256, 256)
11 conv3_2_b (256,)
12 conv3_3_W (3, 3, 256, 256)
13 conv3_3_b (256,)
14 conv4_1_W (3, 3, 256, 512)
15 conv4_1_b (512,)
16 conv4_2_W (3, 3, 512, 512)
17 conv4_2_b (512,)
18 conv4_3_W (3, 3, 512, 512)
19 conv4_3_b (512,)
20 conv5_1_W (3, 3, 512, 512)
21 conv5_1_b (512,)
22 conv5_2_W (3, 3, 512, 512)
23 conv5_2_b (512,)
24 conv5_3_W (3, 3, 512, 512)
25 conv5_3_b (512,)
26 fc6_W (25088, 4096)
27 fc6_b (4096,)
28 fc7_W (4096, 4096)
29 fc7_b (4096,)
30 fc8_W (4096, 1000)
31 fc8_b (1000,)
class_name cup: step 0.543631911278
class_name coffee mug: step 0.364796578884
class_name pitcher, ewer: step 0.0259610358626
class_name eggnog: step 0.0117611540481
class_name water jug: step 0.00806392729282

分类结果为cup
测试4：
输入图片为

运行结果为

2018-03-23 11:37:23.573090: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-03-23 11:37:23.573159: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0 conv1_1_W (3, 3, 3, 64)
1 conv1_1_b (64,)
2 conv1_2_W (3, 3, 64, 64)
3 conv1_2_b (64,)
4 conv2_1_W (3, 3, 64, 128)
5 conv2_1_b (128,)
6 conv2_2_W (3, 3, 128, 128)
7 conv2_2_b (128,)
8 conv3_1_W (3, 3, 128, 256)
9 conv3_1_b (256,)
10 conv3_2_W (3, 3, 256, 256)
11 conv3_2_b (256,)
12 conv3_3_W (3, 3, 256, 256)
13 conv3_3_b (256,)
14 conv4_1_W (3, 3, 256, 512)
15 conv4_1_b (512,)
16 conv4_2_W (3, 3, 512, 512)
17 conv4_2_b (512,)
18 conv4_3_W (3, 3, 512, 512)
19 conv4_3_b (512,)
20 conv5_1_W (3, 3, 512, 512)
21 conv5_1_b (512,)
22 conv5_2_W (3, 3, 512, 512)
23 conv5_2_b (512,)
24 conv5_3_W (3, 3, 512, 512)
25 conv5_3_b (512,)
26 fc6_W (25088, 4096)
27 fc6_b (4096,)
28 fc7_W (4096, 4096)
29 fc7_b (4096,)
30 fc8_W (4096, 1000)
31 fc8_b (1000,)
class_name cellular telephone, cellular phone, cellphone, cell, mobile phone: step 0.465327292681
class_name iPod: step 0.10543012619
class_name radio, wireless: step 0.0810257941484
class_name hard disc, hard disk, fixed disk: step 0.0789099931717
class_name modem: step 0.0603163056076

分类结果为 cellular telephone
测试5：
输入图片为

运行结果为

2018-03-23 11:40:40.956946: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-03-23 11:40:40.957016: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0 conv1_1_W (3, 3, 3, 64)
1 conv1_1_b (64,)
2 conv1_2_W (3, 3, 64, 64)
3 conv1_2_b (64,)
4 conv2_1_W (3, 3, 64, 128)
5 conv2_1_b (128,)
6 conv2_2_W (3, 3, 128, 128)
7 conv2_2_b (128,)
8 conv3_1_W (3, 3, 128, 256)
9 conv3_1_b (256,)
10 conv3_2_W (3, 3, 256, 256)
11 conv3_2_b (256,)
12 conv3_3_W (3, 3, 256, 256)
13 conv3_3_b (256,)
14 conv4_1_W (3, 3, 256, 512)
15 conv4_1_b (512,)
16 conv4_2_W (3, 3, 512, 512)
17 conv4_2_b (512,)
18 conv4_3_W (3, 3, 512, 512)
19 conv4_3_b (512,)
20 conv5_1_W (3, 3, 512, 512)
21 conv5_1_b (512,)
22 conv5_2_W (3, 3, 512, 512)
23 conv5_2_b (512,)
24 conv5_3_W (3, 3, 512, 512)
25 conv5_3_b (512,)
26 fc6_W (25088, 4096)
27 fc6_b (4096,)
28 fc7_W (4096, 4096)
29 fc7_b (4096,)
30 fc8_W (4096, 1000)
31 fc8_b (1000,)
class_name water bottle: step 0.75726544857
class_name pop bottle, soda bottle: step 0.0976340323687
class_name nipple: step 0.0622750669718
class_name water jug: step 0.0233819428831
class_name soap dispenser: step 0.0179366543889

分类结果为 water bottle

参考

添加链接描述

Tensorflow【实战Google深度学习框架】基于tensorflow + Vgg16进行图像分类识别相关推荐

免费教材丨第55期：Python机器学习实践指南、Tensorflow 实战Google深度学习框架
小编说时间过的好快啊,小伙伴们是不是都快进入寒假啦?但是学习可不要落下哦! 本期教材本期为大家发放的教材为:<Python机器学习实践指南>.<Tensorflow 实战G ...
06.图像识别与卷积神经网络------《Tensorflow实战Google深度学习框架》笔记
一.图像识别问题简介及经典数据集图像识别问题希望借助计算机程序来处理.分析和理解图片中的内容,使得计算机可以从图片中自动识别各种不同模式的目标和对象.图像识别问题作为人工智能的一个重要领域,在最近几 ...
TensorFlow：实战Google深度学习框架（四）MNIST数据集识别问题
第5章 MNIST数字识别问题 5.1 MNIST数据处理 5.2 神经网络的训练以及不同模型结果的对比 5.2.1 TensorFlow训练神经网络 5.2.2 使用验证数据集判断模型的效果 5.2 ...
《Tensorflow 实战google深度学习框架》第二版源代码
<<Tensorflow 实战google深度学习框架–第二版>> 完整资料github地址: https://github.com/caicloud/tensorflow-t ...
学习《TensorFlow实战Google深度学习框架 (第2版) 》中文PDF和代码
TensorFlow是谷歌2015年开源的主流深度学习框架,目前已得到广泛应用.<TensorFlow:实战Google深度学习框架(第2版)>为TensorFlow入门参考书,帮助快速. ...
说说TensorFlow实战Google深度学习框架
说说TensorFlow实战Google深度学习框架事情是这样的,博主买了这本书,但是碍于想在电脑上边看边码,想找找PDF版本,然后各种百度,Google,百度网盘,最后找到的都是很多200M的,百 ...
（转）Tensorflow 实战Google深度学习框架读书笔记
本文大致脉络: 读书笔记的自我说明对读书笔记的摘要具体章节的摘要: 第一章深度学习简介第二章 TensorFlow环境搭建第三章 TensorFlow入门第四章深层神经网络第五章 MN ...
TensorFlow实战Google深度学习框架
TensorFlow是谷歌2015年开源的主流深度学习框架.科技届的聚光灯已经从"互联网+"转到了"AI+": 掌握深度学习需要较强的理论功底,用好Tensor ...
TensorFlow实战Google深度学习框架5-7章学习笔记
目录第5章 MNIST数字识别问题第6章图像识别与卷积神经网络第7章图像数据处理第5章 MNIST数字识别问题 MNIST是一个非常有名的手写体数字识别数据集,在很多资料中,这个数据集都会 ...
Tensorflow【实战Google深度学习框架】TensorFlow模型的保存与恢复加载
我们使用TensorFlow进行模型的训练,训练好的模型需要保存,预测阶段我们需要将模型进行加载还原使用,这就涉及TensorFlow模型的保存与恢复加载. 总结一下Tensorflow常用的模型保存 ...

Tensorflow【实战Google深度学习框架】基于tensorflow + Vgg16进行图像分类识别

文章目录

1.VGG-16介绍

2. 文件组成

- vgg16.py文件代码：

运行，测试

参考

Tensorflow【实战Google深度学习框架】基于tensorflow + Vgg16进行图像分类识别相关推荐

最新文章

热门文章