From 45d8f39e0b06d394785f468cb48bfe778b4b9645 Mon Sep 17 00:00:00 2001 From: bushuhui Date: Fri, 28 Oct 2022 12:21:13 +0800 Subject: [PATCH] Improve some contents --- .../2-Logistic_regression.ipynb | 14 ++++++--- 7_deep_learning/1_CNN/03-AlexNet.ipynb | 14 ++++++--- 7_deep_learning/1_CNN/05-googlenet.ipynb | 17 ++++++---- 7_deep_learning/1_CNN/07-densenet.ipynb | 31 ++++++++++++------- .../1_CNN/08-batch-normalization.ipynb | 11 +++++-- 7_deep_learning/1_CNN/09-lr-decay.ipynb | 2 +- 7_deep_learning/1_CNN/10-regularization.ipynb | 2 +- .../1_CNN/11-data-augumentation.ipynb | 2 +- 8 files changed, 62 insertions(+), 31 deletions(-) diff --git a/4_logistic_regression/2-Logistic_regression.ipynb b/4_logistic_regression/2-Logistic_regression.ipynb index 3e338a4..98aae8b 100644 --- a/4_logistic_regression/2-Logistic_regression.ipynb +++ b/4_logistic_regression/2-Logistic_regression.ipynb @@ -181,7 +181,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -234,7 +236,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def plot_decision_boundary(predict_func, data, label, figName=None):\n", @@ -263,7 +267,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "\n", @@ -780,7 +786,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/7_deep_learning/1_CNN/03-AlexNet.ipynb b/7_deep_learning/1_CNN/03-AlexNet.ipynb index 20ff56c..bfd8d91 100644 --- a/7_deep_learning/1_CNN/03-AlexNet.ipynb +++ b/7_deep_learning/1_CNN/03-AlexNet.ipynb @@ -13,7 +13,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import torch.nn as nn\n", @@ -97,7 +99,9 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from torchvision.datasets import CIFAR10\n", @@ -216,7 +220,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# save raw data\n", @@ -241,7 +247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/7_deep_learning/1_CNN/05-googlenet.ipynb b/7_deep_learning/1_CNN/05-googlenet.ipynb index 607e4c1..224b7e2 100644 --- a/7_deep_learning/1_CNN/05-googlenet.ipynb +++ b/7_deep_learning/1_CNN/05-googlenet.ipynb @@ -42,7 +42,8 @@ "ExecuteTime": { "end_time": "2017-12-22T12:51:05.427292Z", "start_time": "2017-12-22T12:51:04.924747Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -61,7 +62,8 @@ "ExecuteTime": { "end_time": "2017-12-22T12:51:08.890890Z", "start_time": "2017-12-22T12:51:08.876313Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -82,7 +84,8 @@ "ExecuteTime": { "end_time": "2017-12-22T12:51:09.671474Z", "start_time": "2017-12-22T12:51:09.587337Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -167,7 +170,8 @@ "ExecuteTime": { "end_time": "2017-12-22T12:51:13.149380Z", "start_time": "2017-12-22T12:51:12.934110Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -280,7 +284,8 @@ "ExecuteTime": { "end_time": "2017-12-22T12:51:16.387778Z", "start_time": "2017-12-22T12:51:15.121350Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -447,7 +452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/7_deep_learning/1_CNN/07-densenet.ipynb b/7_deep_learning/1_CNN/07-densenet.ipynb index 95e13a6..1b738c9 100644 --- a/7_deep_learning/1_CNN/07-densenet.ipynb +++ b/7_deep_learning/1_CNN/07-densenet.ipynb @@ -45,7 +45,8 @@ "ExecuteTime": { "end_time": "2017-12-22T15:38:31.113030Z", "start_time": "2017-12-22T15:38:30.612922Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -71,7 +72,8 @@ "ExecuteTime": { "end_time": "2017-12-22T15:38:31.121249Z", "start_time": "2017-12-22T15:38:31.115369Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -98,7 +100,8 @@ "ExecuteTime": { "end_time": "2017-12-22T15:38:31.145274Z", "start_time": "2017-12-22T15:38:31.123363Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -168,11 +171,12 @@ "ExecuteTime": { "end_time": "2017-12-22T15:38:31.222120Z", "start_time": "2017-12-22T15:38:31.215770Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ - "def transition(in_channel, out_channel):\n", + "def Transition_Block(in_channel, out_channel):\n", " trans_layer = nn.Sequential(\n", " nn.BatchNorm2d(in_channel),\n", " nn.ReLU(True),\n", @@ -209,7 +213,7 @@ } ], "source": [ - "test_net = transition(3, 12)\n", + "test_net = Transition_Block(3, 12)\n", "test_x = Variable(torch.zeros(1, 3, 96, 96))\n", "print('input shape: {} x {} x {}'.format(test_x.shape[1], test_x.shape[2], test_x.shape[3]))\n", "test_y = test_net(test_x)\n", @@ -232,7 +236,8 @@ "ExecuteTime": { "end_time": "2017-12-22T15:38:31.318822Z", "start_time": "2017-12-22T15:38:31.236857Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -240,10 +245,11 @@ " def __init__(self, in_channel, num_classes, growth_rate=32, block_layers=[6, 12, 24, 16]):\n", " super(DenseNet, self).__init__()\n", " self.block1 = nn.Sequential(\n", - " nn.Conv2d(in_channel, 64, 7, 2, 3),\n", + " nn.Conv2d(in_channels=in_channel, out_channels=64, \n", + " kernel_size=7, stride=2, padding=3),\n", " nn.BatchNorm2d(64),\n", " nn.ReLU(True),\n", - " nn.MaxPool2d(3, 2, padding=1)\n", + " nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n", " )\n", " \n", " channels = 64\n", @@ -252,7 +258,7 @@ " block.append(Dense_Block(channels, growth_rate, layers))\n", " channels += layers * growth_rate\n", " if i != len(block_layers) - 1:\n", - " block.append(transition(channels, channels // 2)) # 通过 transition 层将大小减半，通道数减半\n", + " block.append(Transition_Block(channels, channels // 2)) # 通过 transition 层将大小减半，通道数减半\n", " channels = channels // 2\n", " \n", " self.block2 = nn.Sequential(*block)\n", @@ -303,7 +309,8 @@ "ExecuteTime": { "end_time": "2017-12-22T15:38:32.894729Z", "start_time": "2017-12-22T15:38:31.656356Z" - } + }, + "collapsed": true }, "outputs": [], "source": [ @@ -458,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/7_deep_learning/1_CNN/08-batch-normalization.ipynb b/7_deep_learning/1_CNN/08-batch-normalization.ipynb index a00de6e..e4397a1 100644 --- a/7_deep_learning/1_CNN/08-batch-normalization.ipynb +++ b/7_deep_learning/1_CNN/08-batch-normalization.ipynb @@ -59,7 +59,7 @@ "source": [ "* 第一和第二个公式计算出一个 batch 中数据的均值和方差\n", "* 第三个公式对 batch 中的每个数据点做标准化，$\\epsilon$ 是为了计算稳定引入的一个小的常数，通常取 $10^{-5}$\n", - "* 最后利用权重修正得到最后的输出结果，其中 $\\gamma$ $\\beta$是权值变换参数，也是网络参数在训练过程一起学习\n", + "* 最后利用权重修正得到最后的输出结果，其中 $\\gamma$，$\\beta$是权值变换参数，也是网络参数在训练过程一起学习\n", "\n", "下面演示一维的情况，也就是神经网络中的情况" ] @@ -427,6 +427,13 @@ "下面我们在卷积网络下试用一下批标准化看看效果" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "FIXME：设计一个更有说服力的例子" + ] + }, { "cell_type": "code", "execution_count": null, @@ -588,7 +595,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/7_deep_learning/1_CNN/09-lr-decay.ipynb b/7_deep_learning/1_CNN/09-lr-decay.ipynb index 9f0de3d..a894752 100644 --- a/7_deep_learning/1_CNN/09-lr-decay.ipynb +++ b/7_deep_learning/1_CNN/09-lr-decay.ipynb @@ -267,7 +267,7 @@ " if epoch == 20:\n", " set_learning_rate(optimizer, 0.01) # 20 次修改学习率为 0.01\n", " elif epoch == 60:\n", - " set_learning_rate(optimizer, 0.005) # 60 次修改学习率为 0.01\n", + " set_learning_rate(optimizer, 0.005) # 60 次修改学习率为 0.005\n", "\n", " train_loss = 0\n", " net = net.train()\n", diff --git a/7_deep_learning/1_CNN/10-regularization.ipynb b/7_deep_learning/1_CNN/10-regularization.ipynb index 806fa88..974a072 100644 --- a/7_deep_learning/1_CNN/10-regularization.ipynb +++ b/7_deep_learning/1_CNN/10-regularization.ipynb @@ -160,7 +160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/7_deep_learning/1_CNN/11-data-augumentation.ipynb b/7_deep_learning/1_CNN/11-data-augumentation.ipynb index 775b6d2..1b37391 100644 --- a/7_deep_learning/1_CNN/11-data-augumentation.ipynb +++ b/7_deep_learning/1_CNN/11-data-augumentation.ipynb @@ -612,7 +612,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.5.4" } }, "nbformat": 4,