神经网络之Inception模型的实现（Python+TensorFlow）
来源：互联网 发布：java图形界面模板 编辑：程序博客网 时间：2024/06/05 09:25
下面代码的网络模型是 Inception_v3：
下图是inception_v3的网络结构图，和原文章里的有点细节不太一样，但重要的Inception部分原理相同。
# -*- coding:utf-8 -*-## inception_v3 net# default_image_size = 299import tensorflow as tfslim = tf.contrib.slimdef inception_v3_base(inputs, num_classes, scope=None): end_points = {} with tf.variable_scope(scope, 'inception_v3', [inputs]): with scopes.arg_scope([slim.conv2d, slim.fc, slim.batch_norm, slim.dropout], is_training=is_training): # First part:5 conv layer with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='VALID'): # 299 x 299 x 3 end_points['Conv2d_1a'] = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a') # 149 x 149 x 32 end_points['Conv2d_2a'] = slim.conv2d(end_points['Conv2d_1a'], 32, [3, 3], scope='Conv2d_2a') # 147 x 147 x 32 end_points['Conv2d_2b'] = slim.conv2d(end_points['Conv2d_2a'], 64, [3, 3], padding='SAME', scope='Conv2d_2b') # 147 x 147 x 64 end_points['MaxPool_3a'] = slim.max_pool2d(end_points['Conv2d_2b'], [3, 3], stride=2, scope='MaxPool_3a') # 73 x 73 x 64 end_points['Conv2d_3b'] = slim.conv2d(end_points['MaxPool_3a'], 80, [1, 1], scope='Conv2d_3b') # 73 x 73 x 80 end_points['Conv2d_4a'] = slim.conv2d(end_points['Conv2d_3b'], 192, [3, 3], scope='Conv2d_4a') # 71 x 71 x 192 end_points['MaxPool_5a'] = slim.max_pool2d(end_points['Conv2d_4a'], [3, 3], stride=2, scope='MaxPool_5a') # 35 x 35 x 192 net = end_points['MaxPool_5a'] # Inception blocks with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'): # mixed_0: 35 x 35 x 256 with tf.variable_scope('Mixed_5b'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_5x5') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_0') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_1') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv_1x1') # 256 = 64 + 64 + 96 + 32 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_5b'] = net # mixed_1: 35 x 35 x 288 with tf.variable_scope('Mixed_5c'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_5x5') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_0') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_1') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv_1x1') # 288 = 64 + 64 + 96 + 64 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_5c'] = net # mixed_2: 35 x 35 x 288 with tf.variable_scope('Mixed_5d'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_5x5') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_0') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_1') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv_1x1') net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_5d'] = net # mixed_3: 17 x 17 x 768 with tf.variable_scope('Mixed_6a'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 384, [3, 3], stride=2, padding='VALID', scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv_3x3') branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2, padding='VALID', scope='Conv_1x1') with tf.variable_scope('Branch_2'): branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_3x3') # 768 = 384 + 96 + 288 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) end_points['Mixed_6a'] = net # mixed_4: 17 x 17 x 768 with tf.variable_scope('Mixed_6b'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv_1x7') branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv_1x1_a') branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv_7x1_b') branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv_1x7_c') branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv_7x1_d') branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1') # 768 = 192 + 192 + 192 + 192 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_6b'] = net # mixed_5: 17 x 17 x 768 with tf.variable_scope('Mixed_6c'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv_1x7') branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1_a') branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_b') branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv_1x7_c') branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_d') branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1') # 768 = 192 + 192 + 192 + 192 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_6c'] = net # mixed_6: 17 x 17 x 768 with tf.variable_scope('Mixed_6d'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv_1x7') branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1_a') branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_b') branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv_1x7_c') branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_d') branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1') # 768 = 192 + 192 + 192 + 192 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_6d'] = net # mixed_7: 17 x 17 x 768 with tf.variable_scope('Mixed_6e'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv_1x7') branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1_a') branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv_7x1_b') branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_c') branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv_7x1_d') branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e') with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1') # 768 = 192 + 192 + 192 + 192 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_6e'] = net # Auxiliary Head logits aux_logits = tf.identity(end_points['Mixed_6e']) with tf.variable_scope('AuxLogits'): aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, padding='VALID', scope='AvgPool_5x5') aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='Conv_1x1') # Shape of feature map before the final layer. shape = aux_logits.get_shape().as_list() aux_logits = slim.conv2d(aux_logits, 768, shape[1:3], weights_initializer=trunc_normal(0.01), padding='VALID', scope='Conv2d_2a_{}x{}'.format(shape[1],shape[2])) aux_logits = slim.conv2d(aux_logits, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, weights_initializer=trunc_normal(0.001), scope='Conv_1x1') aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze') end_points['AuxLogits'] = aux_logits # mixed_8: 8 x 8 x 1280 with tf.variable_scope('Mixed_7a'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1') branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2, padding='VALID', scope='Conv_3x3') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1') branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv_1x7') branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1') branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2, padding='VALID', scope='Conv_3x3') with tf.variable_scope('Branch_2'): branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_3x3') # 1280 = 320 + 192 + 768 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) end_points['Mixed_7a'] = net # mixed_9: 8 x 8 x 2048 with tf.variable_scope('Mixed_7b'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv_1x1') branch_1 = tf.concat(axis=3, values=[ slim.conv2d(branch_1, 384, [1, 3], scope='Conv_1x3'), slim.conv2d(branch_1, 384, [3, 1], scope='Conv_3x1')]) with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv_1x1') branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv_3x3') branch_2 = tf.concat(axis=3, values=[ slim.conv2d(branch_2, 384, [1, 3], scope='Conv_1x3'), slim.conv2d(branch_2, 384, [3, 1], scope='Conv_3x1')]) with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1') # 2048 = 320 + 384*2 + 384*2 + 192 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_7b'] = net # mixed_10: 8 x 8 x 2048 with tf.variable_scope('Mixed_7c'): with tf.variable_scope('Branch_0'): branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv_1x1') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv_1x1') branch_1 = tf.concat(axis=3, values=[ slim.conv2d(branch_1, 384, [1, 3], scope='Conv_1x3'), slim.conv2d(branch_1, 384, [3, 1], scope='Conv_3x1')]) with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv_1x1') branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv_3x3') branch_2 = tf.concat(axis=3, values=[ slim.conv2d(branch_2, 384, [1, 3], scope='Conv_1x3'), slim.conv2d(branch_2, 384, [3, 1], scope='Conv_3x1')]) with tf.variable_scope('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3') branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1') # 2048 = 320 + 384*2 + 384*2 + 192 net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) end_points['Mixed_7c'] = net # Final pooling and prediction with tf.variable_scope('Logits'): shape = net.get_shape().as_list() net = slim.avg_pool2d(net, shape[1:3], padding='VALID', scope='AvgPool_{}x{}'.format(shape[1],shape[2])) # 1 x 1 x 2048 net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout') end_points['PreLogits'] = net # 2048 logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='Conv_1x1') logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') # 1000 end_points['Logits'] = logits end_points['Predictions'] = prediction_fn(logits, scope='Predictions') return logits, end_pointsdef inception_v3(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.8, prediction_fn=slim.softmax, reuse=None, scope='inception_v3'): with tf.variable_scope(scope, 'inception_v3', [inputs, num_classes], reuse=reuse) as scope: with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training): net, end_points = inception_v3_base(inputs, scope=scope) return logits, end_points阅读全文
0 0
- 神经网络之Inception模型的实现（Python+TensorFlow）
- 神经网络之AlexNet模型的实现（Python+TensorFlow）
- 神经网络之VGGNet模型的实现（Python+TensorFlow）
- 神经网络之ResNet模型的实现（Python+TensorFlow）
- Inception V3 的 tensorflow 实现
- tensorflow 卷积神经网络 Inception-v3模型 迁移学习
- 机器学习算法练习之（二）：Python和Tensorflow分别实现简单的神经网络
- Tensorflow实现卷积神经网络模型
- tensorflow实现 Inception V3
- TensorFlow使用inception模型进行flower识别训练+修改原始inception实现predict
- Tensorflow实例：Inception-v3模型
- tensorflow入门之实现单隐层的神经网络
- [Tensorflow]基于slim框架下inception模型的植物识别
- AlexNet模型实现（4. 基于Tensorflow的python实现）
- tensorflow官方文档学习 Image Recognition（Inception v3模型）
- 基于回归神经网络的中文语句模型实践（Python+Tensorflow+阿里云）
- 神经网络之BP神经网络（Python实现）
- tensorflow代码实现：Inception模块
- 类间散度矩阵和类内散度矩阵
- 常用的验证数字字母组合的正则表达式
- [Poi2003 ][bzoj 2601]MAL猴子捞月
- Faster R-CNN
- mysqldump -h192.168.2.252 -uroot -p -R -E ipvacloud > d:2.sql注意这里不要加分号(密码随后输入即可)
- 神经网络之Inception模型的实现（Python+TensorFlow）
- win10上安装nodejs+npm+express+coffeescript
- 【技术】Android Studio更新后的SDK Manager
- 2017暑期ACM俱乐部个人训练赛第2场
- Servlet 单例多线程
- HashMap中(tab.length
- 使用Angular CLI快速搭建Angular2工程（一）
- 买饼干
- POJ
