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')
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’
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)