TF.Slim的repeat和stack操作
- 2019 年 10 月 28 日
- 筆記
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本文链接：https://blog.csdn.net/weixin_36670529/article/details/102723445
一、常规做法
在搭建网络时，TF-Slim 提供 repeat 和 stack，允许用户重复执行相同的 操作，方便网络构建，例如：
net = ... net = slim.conv2d(net, 256, [3, 3], scope='conv3_1') net = slim.conv2d(net, 256, [3, 3], scope='conv3_2') net = slim.conv2d(net, 256, [3, 3], scope='conv3_3') net = slim.max_pool2d(net, [2, 2], scope='pool2')
常见的作法：可用循环减少工作
net = ... for i in range(3): net = slim.conv2d(net, 256, [3, 3], scope='conv3_%d' % (i+1)) net = slim.max_pool2d(net, [2, 2], scope='pool2')
二、TF-Slim 中的 repeat 操作
使用 TF-Slim 中的 repeat 操作替代上边的定义：
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') net = slim.max_pool2d(net, [2, 2], scope='pool2')
slim.repeat 会自动给每一个卷积层的scopes命名为’conv3/conv3_1’, ’conv3/conv3_2’ 和’conv3/conv3_3’
三、TF-Slim 中的 stack操作
TF-Slim 的 slim.stack 操作允许用户用不同的参数重复调用同一种操作 slim.stack 也为每一个被创建的操作创建一个新的 tf.variable_scope
# Verbose way:原始操作 x = slim.fully_connected(x, 32, scope='fc/fc_1') x = slim.fully_connected(x, 64, scope='fc/fc_2') x = slim.fully_connected(x, 128, scope='fc/fc_3') # Equivalent , TF-Slim way using slim.stack:等价操作 slim.stack(x, slim.fully_connected , [32, 64, 128], scope='fc') # slim.stack 调用了 slim.fully_connected 三次
看到repeat和stack在细节上有差异。具体查看文档
例子，定义vgg
def vgg16(inputs): with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn=tf.nn.relu, weights_initializer=tf.truncated_normal_initializer(0.0, 0.01), weights_regularizer=slim.l2_regularizer(0.0005)): # with之下 net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') net = slim.max_pool2d(net, [2, 2], scope='pool1') net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') net = slim.max_pool2d(net, [2, 2], scope='pool2') net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') net = slim.max_pool2d(net, [2, 2], scope='pool3') net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4') net = slim.max_pool2d(net, [2, 2], scope='pool4') net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5') net = slim.max_pool2d(net, [2, 2], scope='pool5') net = slim.fully_connected(net, 4096, scope='fc6') net = slim.dropout(net, 0.5, scope='dropout6') net = slim.fully_connected(net, 4096, scope='fc7') net = slim.dropout(net, 0.5, scope='dropout7') net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8') return net
基于slim的vgg框架：
import tensorflow as tf import tensorflow.contrib.slim.nets as nets slim = tf.contrib.slim vgg = nets.vgg ... train_log_dir = ... if not tf.gfile.Exists(train_log_dir): tf.gfile.MakeDirs(train_log_dir) with tf.Graph().as_default(): # Set up the data loading: images, labels = ... # Define the model: predictions = vgg.vgg_16(images, is_training=True) # Specify the loss function: slim.losses.softmax_cross_entropy(predictions , labels) total_loss = slim.losses.get_total_loss() tf.summary.scalar('losses/total_loss', total_loss) # Specify the optimization scheme: optimizer = tf.train.GradientDescentOptimizer(learning_rate=.001) # create_train_op that ensures that when we evaluate it to get the loss, # the update_ops are done and the gradient updates are computed. train_tensor = slim.learning.create_train_op(total_loss , optimizer) # Actually runs training. slim.learning.train(train_tensor , train_log_dir)
