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slim.arg_scope()的使用

return (a+b) with slim.arg_scope([fun1],a=10): x=fun1(b=30) print(x) 运行结果为:40 平常所用到的slim.conv2d 例如在下面的代码中,不做单独声明的情况下,slim.conv2d, slim.max_pool2d, slim.avg_pool2d三个函数默认的步长都设为1,padding模式都是'VALID'的。 with slim.arg_scope( [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],stride = 1, padding = 'VALID'): net = slim.conv2d(inputs, 32, [3, 3], stride = 2, scope = 'Conv2d_1a_3x3' ) net = slim.conv2d(net, 32, [3, 3], scope = 'Conv2d_2a_3x3') net = slim.conv2d

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深度学习卷积神经网络——经典网络GoogLeNet(Inception V3)网络的搭建与实现

(inputs, 64, [11, 11], scope='conv1') net = slim.conv2d(net, 128, [11, 11], padding='VALID', scope ], stride=1, padding='SAME'): net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope=' (net, 256, [3, 3], scope='conv3_1') net = slim.conv2d(net, 256, [3, 3], scope='conv3_2') net = slim.conv2d (x, 32, [3, 3], scope='core/core_1') x = slim.conv2d(x, 32, [1, 1], scope='core/core_2') x = slim.conv2d (inputs,num_outputs=32,kernel_size=[3,3],stride=2,scope="Conv2d_1a_3x3") net = slim.conv2d

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    网络结构之 Inception V3

    (net, depth(48), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(64), (net, depth(64), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope=' (net, depth(48), [1, 1], scope='Conv2d_0b_1x1') branch_1 = slim.conv2d(branch_1, depth(64), (net, depth(64), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(96),

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    网络结构之 GoogleNet(Inception V1)

    ='Conv2d_0b_3x3') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net , 16, [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope=' ='Conv2d_0b_3x3') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net , 32, [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope=' , 16, [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='

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    网络结构之 Inception V2

    (branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth (branch_2, depth(96), [3, 3], scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth (branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3') branch_1 = slim.conv2d(branch_1, depth (branch_2, depth(128), [3, 3], scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth (branch_2, depth(128), [3, 3], scope='Conv2d_0b_3x3') branch_2 = slim.conv2d(branch_2, depth

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    网络结构之 Inception V4

    (inputs, 64, [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope ='Conv2d_0b_3x3') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(inputs, 64 , [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b ='Conv2d_0b_3x3') branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2, padding _2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7') branch_2 = slim.conv2d(branch_

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    TensorFlow-Slim 简介【转载】

    例如,考虑下面的代码段(来自 VGG 网络,它的 layers 在两个 pooling 层之间进行了很多 conv): 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 下面是用基本代码写的4层卷积层, net = slim.conv2d(net, 32, [3, 3], scope='core/core_1') net = slim.conv2d(net, 32, [ 1, 1], scope='core/core_2') net = slim.conv2d(net, 64, [3, 3], scope='core/core_3') net = slim.conv2d ='conv2') net = slim.conv2d(net, 256, [11, 11], scope='conv3') 使用 arg_scope 使代码更清晰、简单并且容易去维护。

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    TF.Slim的repeat和stack操作

    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 )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 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 具体查看文档例子,定义vggdef vgg16(inputs): with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn

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    风格迁移背后原理及tensorflow实现

    slim = tf.contrib.slim # 定义卷积,在slim中传入参数 def arg_scope(weight_decay=0.0005): with slim.arg_scope([slim.conv2d ), biases_initializer=tf.zeros_initializer()): with slim.arg_scope([slim.conv2d (imgs, 32, [9, 9], scope='conv1') out1 = relu(instance_norm(out1)) out2 = slim.conv2d out = tf.image.resize_images(x, size=[weight*scale, height*scale]) return out # net = slim.conv2d (x, outchannel, [3, 3], stride=1, scope='conv1') out1 = relu(out1) out2 = slim.conv2d

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    TensorFlow - TF-Slim 使用总览

    比如,将上面的代码和TF-Slim响应的代码调用进行比较:input = ...net = slim.conv2d(input, 128, [3, 3], scope='conv1_1')TF-Slim = slim.conv2d(net, 256, [3, 3], scope='conv3_2')net = slim.conv2d(net, 256, [3, 3], scope='conv3_3') 同样,我们可以用stack简化一个多卷积层塔:# Verbose way: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 ')net = slim.conv2d(net, 256, [11, 11], scope='conv3')如例所示,arg_scope使代码更简洁且易于维护。

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    资源 | GitHub新项目:轻松使用多种预训练卷积网络抽取图像特征

    一般而言,slim.conv2d 有三个参数必填,第一个参数是输入矩阵、第二个是当前卷积层的卷积核数量,最后就是卷积核尺寸。 (net, depth(48), [1, 1], scope='Conv2d_0a_1x1') branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], scope='Conv2d_0b_5x5') with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], scope='Conv2d_0b ('Branch_3'): branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') branch_3 = slim.conv2d

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    一段Python代码,告诉你机器之心今天的秘密

    , 3:4]), scope='Ince_0') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d =tf.constant_initializer(W[3:4, 1:4]), scope='Ince_1_2a'), slim.conv2d scope='Ince_1_2b')], 3) with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d weights_initializer=tf.constant_initializer(W[4:5, 4:5]), scope='Ince_2_1') branch_2 = slim.conv2d =tf.constant_initializer(W[0:1, 0:3]), scope='Ince_2_3a'), slim.conv2d

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    面朝黄土背朝天,老汉今天不发文章……

    , 3:4]), scope='Ince_0') with tf.variable_scope('Branch_1'): branch_1 = slim.conv2d =tf.constant_initializer(W[3:4, 1:4]), scope='Ince_1_2a'), slim.conv2d scope='Ince_1_2b')], 3) with tf.variable_scope('Branch_2'): branch_2 = slim.conv2d weights_initializer=tf.constant_initializer(W[4:5, 4:5]), scope='Ince_2_1') branch_2 = slim.conv2d =tf.constant_initializer(W[0:1, 0:3]), scope='Ince_2_3a'), slim.conv2d

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    使用腾讯云 GPU 学习深度学习系列之六:物体的识别与定位

    net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') end_points['block2'] = net net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') end_points['block3'] = net net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4') end_points['block4'] = net net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5') end_points['block5'] = net net = slim.conv2d(net, 1024, [1, 1], scope='conv7') end_points['block7'] = net net =

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    SSD网络tensorflow版本源码深入分析

    net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') end_points['block2'] = net net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') end_points['block3'] = net net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4') end_points['block4'] = net net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5') end_points['block5'] = net net = slim.conv2d(net, 1024, [1, 1], scope='conv7') end_points['block7'] = net net =

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    EAST 自然场景文本检测实践(EAST: An Efficient and Accurate Scene Text Detector)

    5, 'scale': True, 'is_training': is_training } with slim.arg_scope([slim.conv2d (tf.concat([g[i-1], f[i]], axis=-1), num_outputs[i], 1) h[i] = slim.conv2d(c1_1, if i <= 2: g[i] = unpool(h[i]) else: g[i] = slim.conv2d None) # 4 channel of axis aligned bbox and 1 channel rotation angle geo_map = slim.conv2d 4, 1, activation_fn=tf.nn.sigmoid, normalizer_fn=None) * FLAGS.text_scale angle_map = (slim.conv2d

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    残差网络ResNet网络原理及实现

    下面是输入和输出形状相同的残差块,这里slim.conv2d函数的输入有三个,分别是输入数据、卷积核数量、卷积核的大小,默认的话padding为SAME,即卷积后形状不变,由于输入和输出形状相同,因此我们可以在计算 (input_tensor,conv_depth,kernel_shape)) outputs = tf.nn.relu(slim.conv2d(relu,conv_depth,kernel_shape (input_tensor,conv_depth,kernel_shape,stride=2)) input_tensor_reshape = slim.conv2d(input_tensor ,conv_depth,[1,1],stride=2) outputs = tf.nn.relu(slim.conv2d(relu,conv_depth,kernel_shape) + def inference(inputs): x = tf.reshape(inputs,[-1,28,28,1]) conv_1 = tf.nn.relu(slim.conv2d(x,

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    低光照图像增强算法汇总

    (pool2,128,[3,3], rate=1, activation_fn=lrelu,scope='de_conv3_1') conv3=slim.conv2d(conv3,128, (pool3,256,[3,3], rate=1, activation_fn=lrelu,scope='de_conv4_1') conv4=slim.conv2d(conv4,256, (pool4,512,[3,3], rate=1, activation_fn=lrelu,scope='de_conv5_1') conv5=slim.conv2d(conv5,512, (up6, 256,[3,3], rate=1, activation_fn=lrelu,scope='de_conv6_1') conv6=slim.conv2d(conv6,256, (up7, 128,[3,3], rate=1, activation_fn=lrelu,scope='de_conv7_1') conv7=slim.conv2d(conv7,128,

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    tensorflow pb to tflite 精度下降详解

    'TtNet'): end_points = {} with tf.variable_scope(scope, 'TtNet', [images, num_classes]): net = slim.conv2d (images, 32, [3, 3], scope='conv1') # net = slim.conv2d(images, 64, [3, 3], scope='conv1_2') net = (net, 128, [3, 3], scope='conv2_1') net = slim.conv2d(net, 64, [3, 3], scope='conv2') net = slim.max_pool2d (net, [2, 2], 2, scope='pool2') net = slim.conv2d(net, 128, [3, 3], scope='conv3') net = slim.max_pool2d (net, [2, 2], 2, scope='pool3') net = slim.conv2d(net, 256, [3, 3], scope='conv4') net = slim.max_pool2d

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    风格迁移原理及tensorflow实现-附代码

    slim = tf.contrib.slim # 定义卷积,在slim中传入参数 def arg_scope(weight_decay=0.0005): with slim.arg_scope([slim.conv2d ), biases_initializer=tf.zeros_initializer()): with slim.arg_scope([slim.conv2d (imgs, 32, [9, 9], scope='conv1') out1 = relu(instance_norm(out1)) out2 = slim.conv2d : out = tf.image.resize_images(x, size=[weight*scale, height*scale]) return out # net = slim.conv2d (x, outchannel, [3, 3], stride=1, scope='conv1') out1 = relu(out1) out2 = slim.conv2d

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