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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 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 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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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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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 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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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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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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一段Python代码，告诉你机器之心今天的秘密

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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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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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使用腾讯云 GPU 学习深度学习系列之六：物体的识别与定位

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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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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) + input_tensor_reshape...def inference(inputs): x = tf.reshape(inputs,[-1,28,28,1]) conv_1 = tf.nn.relu(slim.conv2d(x,32,[3,3]

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低光照图像增强算法汇总

(conv10) l_conv2=slim.conv2d(conv1,32,[3,3], rate=1, activation_fn=lrelu,scope='l_conv1_2')...(pool1,64,[3,3], rate=1, activation_fn=lrelu,scope='de_conv2_1') conv2=slim.conv2d(conv2,64,[...(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

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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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点击加载更多

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

网络结构之 GoogleNet(Inception V1)

网络结构之 Inception V4

slim.arg_scope()的使用

网络结构之 Inception V3

网络结构之 Inception V2

TensorFlow-Slim 简介【转载】

TF.Slim的repeat和stack操作

风格迁移背后原理及tensorflow实现

TensorFlow - TF-Slim 使用总览

面朝黄土背朝天，老汉今天不发文章……

一段Python代码，告诉你机器之心今天的秘密

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

SSD网络tensorflow版本源码深入分析

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

使用腾讯云 GPU 学习深度学习系列之六：物体的识别与定位

残差网络ResNet网络原理及实现

残差网络ResNet网络原理及实现

低光照图像增强算法汇总

风格迁移原理及tensorflow实现-附代码

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