如果抛开Keras，TensorLayer，tfLearn，tensroflow 能否写出简介的代码？ 可以！slim这个模块是在16年新推出的，其主要目的是来做所谓的“代码瘦身”
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
众所周知 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。
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.
slim管理变量的机制
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 variablesmy_var = slim.variable(‘my_var‘, shape=[20, 1], initializer=tf.zeros_initializer())regular_variables_and_model_variables = slim.get_variables()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)input = ...net = slim.conv2d(input, 128, [3, 3], scope=‘conv1_1‘)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‘)net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope=‘conv3‘)
net = slim.max_pool2d(net, [2, 2], scope=‘pool2‘)
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‘)32, 64, 128], scope=‘fc‘)# 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‘)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‘)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‘)
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)): 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
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)
