TensorFlow (五): Slim

谁践踏了优雅 2022-03-14 15:52 230阅读 0赞

### Slim ###

*  Slim 简介
 *  代码对比
 *   *  原生 TensorFlow
     *  Slim
 *  共享参数

# Slim 简介 #

Slim ( tf.contrib.slim ) 是一个轻量级的 TensorFlow 框架, 封装了 TensorFlow 的大量 API, 在之前学习过程中我自己明显可以感觉到 tf 的代码太啰嗦了, 想要写一个简单的 CNN 就需要写将近 50 行的辅助函数, 和 20 行的网络搭建, 之前使用 Keras 框架虽然同样可以简化代码, 但是 Keras 和 TensorFlow 的原生 API 彼此的兼容性较差, 而 Slim 则兼备了轻量开发和与 TensorFlow 兼容两大优点

# 代码对比 #

## 原生 TensorFlow ##

def new_weights(shape):
    	return tf.Variable(tf.truncated_normal(shape, stddev=0.05))
    
    def new_biases(length):
    	return tf.Variable(tf.truncated_normal([length], stddev=0.05))
    
    def new_conv_layer(input, nb_filter, nb_channel = 1, filter_size = 3):
    	shape = [filter_size, filter_size, nb_channel, nb_filter]
    
    	weights = new_weights(shape)
    	biases = new_biases(nb_filter)
    
    	layer = tf.nn.conv2d(input = input,
    		 				 filter = weights,
    		 				 strides = [1, 1, 1, 1],
    		 				 padding = "SAME")
    
    	layer += biases 
    
    	layer = tf.nn.max_pool(value = layer, 
    						   ksize = [1, 2, 2, 1], 
    						   strides = [1, 2, 2, 1], 
    						   padding = 'SAME')
    
    	layer = tf.nn.relu(layer)
    
    	return layer, weights
    def flatten_layer(layer):
    	layer_shape = layer.get_shape()
    
    	num_features = layer_shape[1:4].num_elements()
    
    	layer_flat = tf.reshape(layer, [-1, num_features])
    
    	return layer_flat, num_features
    
    def new_fc_layer(input, nb_input, nb_output, use_relu = True):
    	weights = new_weights(shape = [nb_input, nb_output])
    	biases = new_biases(nb_output)
    
    	layer = tf.matmul(input, weights) + biases
    
    	if use_relu:
    		layer =  tf.nn.relu(layer)
    
    	return layer
    x = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28, 1])
    		y_ = tf.placeholder(dtype = tf.float32, shape = [None, nb_class])
    
    
    layer_1, _ = new_conv_layer(input = x,
    							nb_filter = 16)
    
    layer_2, _ = new_conv_layer(input = layer_1,
    							nb_channel = 16,
    							nb_filter = 32)
    
    layer_flat, fatures = flatten_layer(layer_2)
    
    fc_1 = new_fc_layer(input = layer_flat,
    					nb_input = fatures,
    					nb_output = 64)
    
    fc_2 = new_fc_layer(input = fc_1,
    					nb_input = 64,
    					nb_output = nb_class,
    					use_relu = False)
    
    y = tf.nn.softmax(fc_2)

## Slim ##

x = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28, 1])
    y_ = tf.placeholder(dtype = tf.float32, shape = [None, nb_class])
    
    layer_1 = slim.conv2d(x, 16, [3, 3])
    layer_2 = slim.conv2d(layer1, 32, [3, 3])
    layer_flat = slim.flatten(layer_2)
    fc_1 = slim.fully_connected(layer_flat, 64)
    fc_2 = slim.fully_connected(fc_1, nb_class, activation_fn = None)
    y = tf.nn.softmax(fc_2)

由此可见, slim 框架相比于原生 TensorFlow 极大地减少了代码量, 同时 slim 还有以下功能

# 共享参数 #

有时候神经网络的层之间的某些参数是相同的, 如 VGG-16 中所有卷积层的卷积核都是 3 \* 3, 如果每个层都写一遍会很啰嗦, slim 引入了 arg\_scope 机制解决了这一问题

# 写法一
    
    net = slim.conv2d(net, 32, [3, 3], weights_initializer = tf.truncated_normal_initializer(stddev = 0.01), scope = 'conv_1')
    net = slim.conv2d(net, 64, [3, 3], weights_initializer = tf.truncated_normal_initializer(stddev = 0.01), scope = 'conv_2')
    net = slim.conv2d(net, 128, [3, 3], weights_initializer = tf.truncated_normal_initializer(stddev = 0.01), scope = 'conv_3')
    
    # 写法二
    
    with slim.arg_scope([slim. conv2d], kernel_size = [3, 3], weights_initializer = tf.truncated_normal_initializer(stddev = 0.01)):
    	net = slim.conv2d(net, 32, scope = 'conv_1')
    	net = slim.conv2d(net, 64, scope = 'conv_2')
    	net = slim.conv2d(net, 128, scope = 'conv_3')