一.简介
slim被放在tensorflow.contrib这个库下面，导入的方法如下：
- import tensorflow.contrib.slim as slim
这样我们就可以使用slim了，既然说到了，先来扒一扒tensorflow.contrib这个库，tensorflow官方对它的描述是：此目录中的任何代码未经官方支持，可能会随时更改或删除。每个目录下都有指定的所有者。它旨在包含额外功能和贡献，最终会合并到核心TensorFlow中，但其接口可能仍然会发生变化，或者需要进行一些测试，看是否可以获得更广泛的接受。所以slim依然不属于原生tensorflow。
那么什么是slim？slim到底有什么用？
slim是一个使构建，训练，评估神经网络变得简单的库。它可以消除原生tensorflow里面很多重复的模板性的代码，让代码更紧凑，更具备可读性。另外slim提供了很多计算机视觉方面的著名模型（VGG, AlexNet等），我们不仅可以直接使用，甚至能以各种方式进行扩展。
slim的子模块及功能介绍：
arg_scope: provides a new scope named arg_scope that allows a user to define default arguments for specific operations within that scope.
除了基本的namescope，variabelscope外，又加了argscope，它是用来控制每一层的默认超参数的。（后面会详细说）
data: contains TF-slim's dataset definition, data providers, parallel_reader, and decoding utilities.
貌似slim里面还有一套自己的数据定义，这个跳过，我们用的不多。
evaluation: contains routines for evaluating models.
评估模型的一些方法，用的也不多
layers: contains high level layers for building models using tensorflow.
这个比较重要，slim的核心和精髓，一些复杂层的定义
learning: contains routines for training models.
一些训练规则
losses: contains commonly used loss functions.
一些loss
metrics: contains popular evaluation metrics.
评估模型的度量标准
nets: contains popular network definitions such as VGG and AlexNet models.
包含一些经典网络，VGG等，用的也比较多
queues: provides a context manager for easily and safely starting and closing QueueRunners.
文本队列管理，比较有用。
regularizers: contains weight regularizers.
包含一些正则规则
variables: provides convenience wrappers for variable creation and manipulation.
这个比较有用，我很喜欢slim管理变量的机制
具体子库就这么多拉，接下来干货时间！
二.slim定义模型
slim中定义一个变量的示例：
- # Model Variables
- weights = slim.model_variable('weights',
- shape=[10, 10, 3 , 3],
- initializer=tf.truncated_normal_initializer(stddev=0.1),
- regularizer=slim.l2_regularizer(0.05),
- device='/CPU:0')
- model_variables = slim.get_model_variables()
- # Regular variables
- my_var = slim.variable('my_var',
- shape=[20, 1],
- initializer=tf.zeros_initializer())
- regular_variables_and_model_variables = slim.get_variables()
如上，变量分为两类：模型变量和局部变量。局部变量是不作为模型参数保存的，而模型变量会再save的时候保存下来。这个玩过tensorflow的人都会明白，诸如global_step之类的就是局部变量。slim中可以写明变量存放的设备，正则和初始化规则。还有获取变量的函数也需要注意一下，get_variables是返回所有的变量。
slim中实现一个层：
首先让我们看看tensorflow怎么实现一个层，例如卷积层：
- input = ...
- with tf.name_scope('conv1_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(input, kernel, [1, 1, 1, 1], padding='SAME')
- biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
- trainable=True, name='biases')
- bias = tf.nn.bias_add(conv, biases)
- conv1 = tf.nn.relu(bias, name=scope)
然后slim的实现：
- input = ...
- net = slim.conv2d(input, 128, [3, 3], scope='conv1_1')
但这个不是重要的，因为tenorflow目前也有大部分层的简单实现，这里比较吸引人的是slim中的repeat和stack操作：
假设定义三个相同的卷积层：
- 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')
在slim中的repeat操作可以减少代码量：
- net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
- net = slim.max_pool2d(net, [2, 2], scope='pool2')
而stack是处理卷积核或者输出不一样的情况：
假设定义三层FC：
- # Verbose way:
- 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')
使用stack操作：
- slim.stack(x, slim.fully_connected, [32, 64, 128], scope='fc')
同理卷积层也一样：
- # 普通方法:
- x = slim.conv2d(x, 32, [3, 3], scope='core/core_1')
- x = slim.conv2d(x, 32, [1, 1], scope='core/core_2')
- x = slim.conv2d(x, 64, [3, 3], scope='core/core_3')
- x = slim.conv2d(x, 64, [1, 1], scope='core/core_4')
- # 简便方法:
- slim.stack(x, slim.conv2d, [(32, [3, 3]), (32, [1, 1]), (64, [3, 3]), (64, [1, 1])], scope='core')
slim中的argscope：
如果你的网络有大量相同的参数，如下：
- net = slim.conv2d(inputs, 64, [11, 11], 4, padding='SAME',
- weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
- weights_regularizer=slim.l2_regularizer(0.0005), scope='conv1')
- net = slim.conv2d(net, 128, [11, 11], padding='VALID',
- weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
- weights_regularizer=slim.l2_regularizer(0.0005), scope='conv2')
- net = slim.conv2d(net, 256, [11, 11], padding='SAME',
- weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
- weights_regularizer=slim.l2_regularizer(0.0005), scope='conv3')
然后我们用arg_scope处理一下：
- with slim.arg_scope([slim.conv2d], padding='SAME',
- weights_initializer=tf.truncated_normal_initializer(stddev=0.01)
- weights_regularizer=slim.l2_regularizer(0.0005)):
- net = slim.conv2d(inputs, 64, [11, 11], scope='conv1')
- net = slim.conv2d(net, 128, [11, 11], padding='VALID', scope='conv2')
- net = slim.conv2d(net, 256, [11, 11], scope='conv3')
是不是一下子就变简洁了？这里额外说明一点，arg_scope的作用范围内，是定义了指定层的默认参数，若想特别指定某些层的参数，可以重新赋值（相当于重写），如上倒数第二行代码。那如果除了卷积层还有其他层呢？那就要如下定义：
- with slim.arg_scope([slim.conv2d, slim.fully_connected],
- activation_fn=tf.nn.relu,
- weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
- weights_regularizer=slim.l2_regularizer(0.0005)):
- with slim.arg_scope([slim.conv2d], stride=1, padding='SAME'):
- net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
- net = slim.conv2d(net, 256, [5, 5],
- weights_initializer=tf.truncated_normal_initializer(stddev=0.03),
- scope='conv2')
- net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc')
写两个arg_scope就行了。
采用如上方法，定义一个VGG也就十几行代码的事了。
- 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)):
- 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
三.训练模型
这个没什么好说的，说一下直接拿经典网络来训练吧。
- import tensorflow as tf
- vgg = tf.contrib.slim.nets.vgg
- # Load the images and labels.
- images, labels = ...
- # Create the model.
- predictions, _ = vgg.vgg_16(images)
- # Define the loss functions and get the total loss.
- loss = slim.losses.softmax_cross_entropy(predictions, labels)
是不是超级简单？
关于loss,要说一下定义自己的loss的方法，以及注意不要忘记加入到slim中让slim看到你的loss。
还有正则项也是需要手动添加进loss当中的，不然最后计算的时候就不优化正则目标了。
- # Load the images and labels.
- images, scene_labels, depth_labels, pose_labels = ...
- # Create the model.
- scene_predictions, depth_predictions, pose_predictions = CreateMultiTaskModel(images)
- # Define the loss functions and get the total loss.
- classification_loss = slim.losses.softmax_cross_entropy(scene_predictions, scene_labels)
- sum_of_squares_loss = slim.losses.sum_of_squares(depth_predictions, depth_labels)
- pose_loss = MyCustomLossFunction(pose_predictions, pose_labels)
- slim.losses.add_loss(pose_loss) # Letting TF-Slim know about the additional loss.
- # The following two ways to compute the total loss are equivalent:
- regularization_loss = tf.add_n(slim.losses.get_regularization_losses())
- total_loss1 = classification_loss + sum_of_squares_loss + pose_loss + regularization_loss
- # (Regularization Loss is included in the total loss by default).
- total_loss2 = slim.losses.get_total_loss()
四.读取保存模型变量
通过以下功能我们可以载入模型的部分变量：
- # Create some variables.
- v1 = slim.variable(name="v1", ...)
- v2 = slim.variable(name="nested/v2", ...)
- ...
- # Get list of variables to restore (which contains only 'v2').
- variables_to_restore = slim.get_variables_by_name("v2")
- # Create the saver which will be used to restore the variables.
- restorer = tf.train.Saver(variables_to_restore)
- with tf.Session() as sess:
- # Restore variables from disk.
- restorer.restore(sess, "/tmp/model.ckpt")
- print("Model restored.")
除了这种部分变量加载的方法外，我们甚至还能加载到不同名字的变量中。
假设我们定义的网络变量是conv1/weights，而从VGG加载的变量名为vgg16/conv1/weights，正常load肯定会报错（找不到变量名），但是可以这样：
- def name_in_checkpoint(var):
- return 'vgg16/' + var.op.name
- variables_to_restore = slim.get_model_variables()
- variables_to_restore = {name_in_checkpoint(var):var for var in variables_to_restore}
- restorer = tf.train.Saver(variables_to_restore)
- with tf.Session() as sess:
- # Restore variables from disk.
- restorer.restore(sess, "/tmp/model.ckpt")
通过这种方式我们可以加载不同变量名的变量！！
转载自https://blog.csdn.net/mao_xiao_feng/article/details/73409975