{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python solving with LeNet\n", "\n", "\n", "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/tzx/dev/caffe-rc3\n" ] } ], "source": [ "# 路径都是从 ipynb 文件开始的,而不是 ipython notebook 运行的目录。\n", "import os\n", "os.chdir('..')\n", "print os.path.realpath(os.getcwd())\n", "# print os.path.abspath(__file__)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['./python', '', '/usr/local/lib/python2.7/dist-packages/percol-0.2.1-py2.7.egg', '/usr/local/lib/python2.7/dist-packages/cmigemo-0.1.6-py2.7.egg', '/home/tzx/dev/caffe-rc3/python', '/home/tzx/dev/caffe-rc3/examples', '/usr/lib/python2.7', '/usr/lib/python2.7/plat-x86_64-linux-gnu', '/usr/lib/python2.7/lib-tk', '/usr/lib/python2.7/lib-old', '/usr/lib/python2.7/lib-dynload', '/usr/local/lib/python2.7/dist-packages', '/usr/lib/python2.7/dist-packages', '/usr/lib/python2.7/dist-packages/PILcompat', '/usr/lib/python2.7/dist-packages/gtk-2.0', '/usr/lib/pymodules/python2.7', '/usr/lib/python2.7/dist-packages/ubuntu-sso-client', '/usr/local/lib/python2.7/dist-packages/IPython/extensions', '/home/tzx/.ipython']\n" ] } ], "source": [ "import sys\n", "sys.path.insert(0, './python')\n", "print sys.path\n", "# 其实可以在自己的 .bashrc 里加上 export PYTHONPATH=~/dev/caffe-rc/python:$PYTHONPATH,两种方法是一样的\n", "import caffe\n", "\n", "from pylab import *\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Copying from /home/tzx/datasets/datasets...\n", "Unzipping...\n", "Done.\n", "Creating lmdb...\n", "Done.\n" ] } ], "source": [ "# Download and prepare data\n", "\n", "# 不使用原来的脚本来获取数据,因为下载太慢了!我更喜欢用【迅雷】来下载\n", "# !data/mnist/get_mnist.sh\n", "# Instead,直接从 $DATASETS_DIR 拷贝过来就可以,用我们修改过的脚本:\n", "!data/mnist/get_mnist_cvrs.sh \n", "!examples/mnist/create_mnist.sh" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2429 /usr/bin/python /usr/local/bin/ipython notebook --no-browser --port=9876\r\n" ] } ], "source": [ "!pgrep ipython -a" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#!/usr/bin/env sh\n", "# This scripts downloads the mnist data and unzips it.\n", "\n", "DIR=\"$( cd \"$(dirname \"$0\")\" ; pwd -P )\"\n", "cd $DIR\n", "\n", "echo \"Downloading...\"\n", "\n", "wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", "wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", "wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "\n", "echo \"Unzipping...\"\n", "\n", "gunzip train-images-idx3-ubyte.gz\n", "gunzip train-labels-idx1-ubyte.gz\n", "gunzip t10k-images-idx3-ubyte.gz\n", "gunzip t10k-labels-idx1-ubyte.gz\n", "\n", "# Creation is split out because leveldb sometimes causes segfault\n", "# and needs to be re-created.\n", "\n", "echo \"Done.\"\n", "\n", "\n", "\n", "#!/usr/bin/env sh\n", "# This scripts downloads the mnist data and unzips it.\n", "\n", "DIR=\"$( cd \"$(dirname \"$0\")\" ; pwd -P )\"\n", "cd $DIR\n", "\n", "echo \"Copying from $DATASETS_DIR...\"\n", "\n", "cp $DATASETS_DIR/MNIST/train-images-idx3-ubyte.gz ./\n", "cp $DATASETS_DIR/MNIST/train-labels-idx1-ubyte.gz ./\n", "cp $DATASETS_DIR/MNIST/t10k-images-idx3-ubyte.gz ./\n", "cp $DATASETS_DIR/MNIST/t10k-labels-idx1-ubyte.gz ./\n", "\n", "rm train-images-idx3-ubyte 2>/dev/null\n", "rm train-labels-idx1-ubyte 2>/dev/null\n", "rm t10k-images-idx3-ubyte 2>/dev/null\n", "rm t10k-labels-idx1-ubyte 2>/dev/null\n", "\n", "echo \"Unzipping...\"\n", "\n", "gunzip train-images-idx3-ubyte.gz\n", "gunzip train-labels-idx1-ubyte.gz\n", "gunzip t10k-images-idx3-ubyte.gz\n", "gunzip t10k-labels-idx1-ubyte.gz\n", "\n", "# Creation is split out because leveldb sometimes causes segfault\n", "# and needs to be re-created.\n", "\n", "echo \"Done.\"\n" ] } ], "source": [ "!cat data/mnist/get_mnist.sh\n", "print \"\\n\\n\"\n", "!cat data/mnist/get_mnist_cvrs.sh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need two external files to help out:\n", "* the net prototxt, defining the architecture and pointing to the train/test data\n", "* the solver prototxt, defining the learning parameters\n", "\n", "- 一个 net prototxt 文件,定义网络结构,指定训练和测试的数据;\n", "- 一个 solver prototxt 文件,定义学习策略(loss 和如何优化 loss)\n", "- caffe 就是 solver,net,layer 分离的。net 统领下面的 layers。\n", "\n", "We start with the net. We'll write the net in a succinct `[sək'sɪŋkt]` 简明 and natural way\n", "as Python code that serializes to Caffe's protobuf model format.\n", "\n", "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-rw-r-- 1 tzx tzx 1.4K 6月 21 12:54 examples/mnist/lenet_auto_test.prototxt\r\n", "-rw-rw-r-- 1 tzx tzx 1.4K 6月 21 12:54 examples/mnist/lenet_auto_train.prototxt\r\n" ] } ], "source": [ "from caffe import layers as L\n", "from caffe import params as P\n", "\n", "def lenet(lmdb, batch_size):\n", " # our version of LeNet: a series of linear and simple nonlinear transformations\n", " # net spec 就是在定义网络,可以给 n 随意指定 .data, .label, .shit 什么的。\n", " n = caffe.NetSpec()\n", " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", " transform_param=dict(scale=1./255), ntop=2)\n", " # 上面就是数据和标记\n", " # 下面的卷积层,输入是 n.data,kernel_size,etc\n", " n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", " # MAX 池化\n", " n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", " # 卷积的 num_output 是啥意思?\n", " n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", " n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", " # ip 层(内积)是啥意思?\n", " n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", " # relu 层很好理解,就是 max(0, x),只要把小于 0 的置零就可以了,所以直接 in_place 就好\n", " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", " # \n", " n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n", " # 定义 loss 函数,softmax\n", " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", " # 上面定义的所有键值对的 value 要有 _to_proto 属性,不然 to_proto() 函数会失败。\n", " return n.to_proto()\n", "\n", "# 写出 train.prototxt,batchsize 是 64\n", "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n", " f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n", "\n", "# 写出 test.prototxt,batchsize 是 100\n", "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n", " f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))\n", "\n", "!ls -alh examples/mnist/lenet_auto_train.prototxt examples/mnist/lenet_auto_test.prototxt\n", "# print \"\\n\\n\"\n", "# !ls -alh examples/mnist/mnist_train_lmdb examples/mnist/mnist_test_lmdb\n", "# print \"\\n\\n!cat examples/mnist/lenet_auto_train.prototxt\"\n", "# !cat examples/mnist/lenet_auto_train.prototxt\n", "# print \"\\n\\n!cat examples/mnist/lenet_auto_test.prototxt\"\n", "# !cat examples/mnist/lenet_auto_test.prototxt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "5\n", "\n", "\n" ] }, { "data": { "text/plain": [ "{'shit': 5}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# caffe.NetSpec?\n", "from caffe import layers as L\n", "from caffe import params as P\n", "print P.Data.LMDB\n", "\n", "n = caffe.NetSpec()\n", "n.shit = 5\n", "print n.shit\n", "print type(n)\n", "# n.to_proto() # 'int' object has no attribute '_to_proto'\n", "print type(n.to_proto)\n", "\n", "dict(shit=5) # 这是 python 的数据格式。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**caffe.NetSpec?**\n", "\n", "```\n", "Init signature: caffe.NetSpec(self)\n", "Docstring:\n", "A NetSpec contains a set of Tops (assigned directly as attributes).\n", "Calling NetSpec.to_proto generates a NetParameter containing all of the\n", "layers needed to produce all of the assigned Tops, using the assigned\n", "names.\n", "File: ~/dev/caffe-rc3/python/caffe/net_spec.py\n", "Type: type\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net.\n", "\n", "protobuf 的定义,从 python 程序中写出的!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "layer {\r\n", " name: \"data\"\r\n", " type: \"Data\"\r\n", " top: \"data\"\r\n", " top: \"label\"\r\n", " transform_param {\r\n", " scale: 0.00392156862745\r\n", " }\r\n", " data_param {\r\n", " source: \"examples/mnist/mnist_train_lmdb\"\r\n", " batch_size: 64\r\n", " backend: LMDB\r\n", " }\r\n", "}\r\n", "layer {\r\n", " name: \"conv1\"\r\n", " type: \"Convolution\"\r\n", " bottom: \"data\"\r\n", " top: \"conv1\"\r\n", " convolution_param {\r\n", " num_output: 20\r\n", " kernel_size: 5\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", " }\r\n", "}\r\n", "layer {\r\n", " name: \"pool1\"\r\n", " type: \"Pooling\"\r\n", " bottom: \"conv1\"\r\n", " top: \"pool1\"\r\n", " pooling_param {\r\n", " pool: MAX\r\n", " kernel_size: 2\r\n", " stride: 2\r\n", " }\r\n", "}\r\n", "layer {\r\n", " name: \"conv2\"\r\n", " type: \"Convolution\"\r\n", " bottom: \"pool1\"\r\n", " top: \"conv2\"\r\n", " convolution_param {\r\n", " num_output: 50\r\n", " kernel_size: 5\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", " }\r\n", "}\r\n", "layer {\r\n", " name: \"pool2\"\r\n", " type: \"Pooling\"\r\n", " bottom: \"conv2\"\r\n", " top: \"pool2\"\r\n", " pooling_param {\r\n", " pool: MAX\r\n", " kernel_size: 2\r\n", " stride: 2\r\n", " }\r\n", "}\r\n", "layer {\r\n", " name: \"ip1\"\r\n", " type: \"InnerProduct\"\r\n", " bottom: \"pool2\"\r\n", " top: \"ip1\"\r\n", " inner_product_param {\r\n", " num_output: 500\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", " }\r\n", "}\r\n", "layer {\r\n", " name: \"relu1\"\r\n", " type: \"ReLU\"\r\n", " bottom: \"ip1\"\r\n", " top: \"ip1\"\r\n", "}\r\n", "layer {\r\n", " name: \"ip2\"\r\n", " type: \"InnerProduct\"\r\n", " bottom: \"ip1\"\r\n", " top: \"ip2\"\r\n", " inner_product_param {\r\n", " num_output: 10\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", " }\r\n", "}\r\n", "layer {\r\n", " name: \"loss\"\r\n", " type: \"SoftmaxWithLoss\"\r\n", " bottom: \"ip2\"\r\n", " bottom: \"label\"\r\n", " top: \"loss\"\r\n", "}\r\n" ] } ], "source": [ "!cat examples/mnist/lenet_auto_train.prototxt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# The train/test net protocol buffer definition\r\n", "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n", "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n", "# test_iter specifies how many forward passes the test should carry out.\r\n", "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n", "# covering the full 10,000 testing images.\r\n", "test_iter: 100\r\n", "# Carry out testing every 500 training iterations.\r\n", "test_interval: 500\r\n", "# The base learning rate, momentum and the weight decay of the network.\r\n", "base_lr: 0.01\r\n", "momentum: 0.9\r\n", "weight_decay: 0.0005\r\n", "# The learning rate policy\r\n", "lr_policy: \"inv\"\r\n", "gamma: 0.0001\r\n", "power: 0.75\r\n", "# Display every 100 iterations\r\n", "display: 100\r\n", "# The maximum number of iterations\r\n", "max_iter: 10000\r\n", "# snapshot intermediate results\r\n", "snapshot: 5000\r\n", "snapshot_prefix: \"examples/mnist/lenet\"\r\n" ] } ], "source": [ "!cat examples/mnist/lenet_auto_solver.prototxt\n", "# 这些有点意思:\n", "# snapshot_prefix: \"examples/mnist/lenet\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "caffe.set_device(0)\n", "# print \"caffe.device(): \", caffe.device\n", "caffe.set_mode_gpu()\n", "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "caffe.SGDSolver?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[('data', (64, 1, 28, 28)),\n", " ('label', (64,)),\n", " ('conv1', (64, 20, 24, 24)),\n", " ('pool1', (64, 20, 12, 12)),\n", " ('conv2', (64, 50, 8, 8)),\n", " ('pool2', (64, 50, 4, 4)),\n", " ('ip1', (64, 500)),\n", " ('ip2', (64, 10)),\n", " ('loss', ())]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# each output is (batch size, feature dim, spatial dim)\n", "[(k, v.data.shape) for k, v in solver.net.blobs.items()]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(64, 1, 28, 28)\n", "(64,)\n", "\n" ] } ], "source": [ "print solver.net.blobs['data'].data.shape\n", "print solver.net.blobs['label'].data.shape\n", "print type(solver.net.blobs['label'].data.shape)\n", "# solver.net.blobs['data'].data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('conv1', (20, 1, 5, 5)), ('conv2', (50, 20, 5, 5)), ('ip1', (500, 800)), ('ip2', (10, 500))]\n", "[('conv1', (20,)), ('conv2', (50,)), ('ip1', (500,)), ('ip2', (10,))]\n" ] } ], "source": [ "# blobs 里面是数据(包括卷积核什么的),blob = {data, diff}\n", "# params 立面是 weights 和 bias,param = [weights, biases]\n", "# just print the weight sizes (not biases)\n", "print [(k, v[0].data.shape) for k, v in solver.net.params.items()] # weights\n", "print [(k, v[1].data.shape) for k, v in solver.net.params.items()] # biases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'loss': array(2.4466583728790283, dtype=float32)}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.net.forward() # train net\n", "solver.test_nets[0].forward() # test net (there can be more than one)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5. 0. 4. 1. 9. 2. 1. 3.]\n", "data[:8, 0].shape: (8, 28, 28)\n", "transpose(1, 0, 2): (28, 8, 28)\n", "transpose(1, 0, 2): (28, 224)\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# we use a little trick to tile the first eight images\n", "print solver.net.blobs['label'].data[:8]\n", "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n", "\n", "# 上面的,解释一下,data 层原来结构是:('data', (64, 1, 28, 28))\n", "# solver.net.blobs['data'].data[:8, 0]:选定前 8 张图的第一个 channel(其实也只有一个)\n", "# 然后就维度就变成了 [n = 8, h, w],输出一下\n", "print \"data[:8, 0].shape: \", solver.net.blobs['data'].data[:8, 0].shape\n", "\n", "# 变换一下维度,原来是 [n, h, w] 现在是 [h, n, w],也就是最大的 span 的那个维度是 h,一次可以把\n", "# 8 张图片的行取到。\n", "print \"transpose(1, 0, 2): \", solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).shape\n", "# 然后 reshape 一下,变成 [h, n*w],就把图片横着放了。\n", "print \"transpose(1, 0, 2): \", solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28).shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "关于维度,我想了一个比喻。对于数字 123,其实就是 ['1','2','3'],三个维度是 [百,十,个],“百”的 span 是 100,“十”的 span 是 10,“个”的 span 是 1。跟这里的 [n,k,h,w] 可以类比。至于 transpose(1, 0, 2),其实就是把\n", "最大 span 的维度从“百”变成“十”,也就是具有同样十位数的数字放在一起(比如 120,28 原来就相隔很远,但\n", "transpose 后距离就很近)。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 7. 2. 1. 0. 4. 1. 4. 9.]\n" ] }, { "data": { "image/png": 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NlVqtJisri6ysLBITE1laWsLlckVtPmi1WoqKikhNTQXCJ0WbzSYqzkih0+moqKigsrKS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\nQkJCQkJCQkJCQkJCQkJCQkJCQmJn8P8Brf3zkiH+EakAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n", "print solver.test_nets[0].blobs['label'].data[:8]\n", "\n", "# 和上面一样,不过这次是测试网络。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both train and test nets seem to be loading data, and to have correct labels.\n", "\n", "Let's take one step of (minibatch) SGD and see what happens." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "solver.step(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 5, 5)\n", "(4, 5, 5, 5)\n", "(4, 5, 5, 5)\n", "(20, 25)\n" ] }, { "data": { "image/png": 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WqdpuYONwBzLlH12e8Do1QGB4VdXmhoM75sOdm9hL/4HH/NVMNm5I9zWVqoCrMJvu2Xny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\nVqeLFdAyrU6kKLtXoFUfdeCwi8P5f3aArACsNK4EJWErWbs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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# params = [weights, diff]\n", "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n", " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')\n", "\n", "# 最开始得维度是 [20, 1, 5, 5],用了 [:, 0] 后变成 [20, 5, 5], 格式是 [n, h, w]\n", "print solver.net.params['conv1'][0].diff[:, 0].shape\n", "# 每 5 张图片放在一起, 共四组,一组五张图,所以维度变成了 [4, 5, 5, 5],格式是:[n1, n2, h, w]\n", "# 这里我把组数用 n1 (=4) 表示,每组图片数目用 n2 (=5) 表示。\n", "print solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5).shape\n", "# 然后 transpose(0, 2, 1, 3),于是 [n1, n2, h, w] 变成了 [n1, h, n2, w]\n", "print solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5).transpose(0, 2, 1, 3).shape\n", "# 最后,用于显示,reshape(n1*h, n2*w) 这样就把 index 为 (i,j) 的图片(大小都是 w*h=5x5)放在了\n", "# 正确的位置(i 是 0..n1-1, j 是 0..n2-1,(i,j)表示第 i 组,第 j 张图片。\n", "# 因为组是更大的 span,所以一行是一组。\n", "# 所以这里的索引其实是\n", "# (0,0)~ 0, (0, 1)~ 1, (0,2)~ 2, (0,3)~ 3, (0,4)~ 4\n", "# (1,0)~ 5, (1, 1)~ 6, (1,2)~ 7, (1,3)~ 8, (1,4)~ 9\n", "# (2,0)~10, (2, 1)~11, (2,2)~12, (2,3)~13, (2,4)~14\n", "# (3,0)~15, (3, 1)~16, (3,2)~17, (3,3)~18, (3,4)~19\n", "\n", "print solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5).transpose(0, 2, 1, 3).reshape(4*5, 5*5).shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "那我们把上面这 4x5 个 filters 图片右下角的 2x3 显示出来。索引是 12, 13, 14, 17, 18, 19。" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3/AEnHfb6DvURqhOyoXZHwM5grh8aaJ+5+rpcZR1UnYvMuUIPMjbeXq+YG1E/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\nM2Zfh2MPLfQkXAXoDpCRqpMBLTwdzq5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 右下角的 2x3 个 filters。\n", "imshow(solver.net.params['conv1'][0].diff[[12,13,14,17,18,19], 0].reshape(2, 3, 5, 5) # 两组 * 三张/每组\n", " .transpose(0, 2, 1, 3).reshape(2*5, 3*5), cmap='gray')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 第一行\n", "imshow(solver.net.params['conv1'][0].diff[:5, 0].reshape(1, 5, 5, 5)\n", " .transpose(0, 2, 1, 3).reshape(1*5, 5*5), cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n", "Note that this process will be the same as if training through the `caffe` binary. In particular:\n", "* logging will continue to happen as normal\n", "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n", "* testing will happen at the interval specified (here, every 500 iterations)\n", "\n", "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n", "* write a custom stopping criterion\n", "* change the solving process by updating the net in the loop" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "niter = 200\n", "test_interval = 25\n", "# losses will also be stored in the log\n", "train_loss = zeros(niter)\n", "test_acc = zeros(int(np.ceil(niter / test_interval)))\n", "output = zeros((niter, 8, 10))\n", "\n", "# the main solver loop\n", "for it in range(niter):\n", " solver.step(1) # SGD by Caffe\n", " \n", " # store the train loss\n", " train_loss[it] = solver.net.blobs['loss'].data\n", " \n", " # store the output on the first test batch\n", " # (start the forward pass at conv1 to avoid loading new data)\n", " solver.test_nets[0].forward(start='conv1')\n", " output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n", " \n", " # run a full test every so often\n", " # (Caffe can also do this for us and write to a log, but we show here\n", " # how to do it directly in Python, where more complicated things are easier.)\n", " if it % test_interval == 0:\n", " print 'Iteration', it, 'testing...'\n", " correct = 0\n", " for test_it in range(100):\n", " solver.test_nets[0].forward()\n", " correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n", " == solver.test_nets[0].blobs['label'].data)\n", " test_acc[it // test_interval] = correct / 1e4" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100, 10)\n", "(100,)\n", "[71 89 82 68 85 23 91 86 84 99]\n" ] }, { "data": { "text/plain": [ "array([7, 2, 1, 0, 4, 1, 4, 9, 2, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6,\n", " 5, 4, 0, 7, 4, 0, 1, 3, 1, 3, 6, 7, 2, 7, 1, 2, 1, 1, 7, 4, 2, 3, 5,\n", " 1, 2, 4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 2, 9, 3, 7, 4, 6, 4, 3,\n", " 0, 7, 0, 2, 9, 1, 7, 3, 2, 9, 7, 7, 6, 2, 7, 8, 4, 7, 3, 6, 1, 3, 6,\n", " 9, 3, 1, 4, 1, 7, 6, 9])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print solver.test_nets[0].blobs['ip2'].data.shape\n", "print solver.test_nets[0].blobs['ip2'].data.argmax(1).shape\n", "print solver.test_nets[0].blobs['ip2'].data.argmax(0)\n", "solver.test_nets[0].blobs['ip2'].data.argmax(1)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Object `numpy.argmax` not found.\n" ] } ], "source": [ "print type(solver.test_nets[0].blobs['ip2'].data) # numpy.ndarray\n", "arr = np.array([])\n", "\n", "arr.argmax?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**numpy.ndarray.argmax**\n", "\n", "```\n", "Docstring:\n", "a.argmax(axis=None, out=None)\n", "\n", "Return indices of the maximum values along the given axis.\n", "\n", "Refer to `numpy.argmax` for full documentation.\n", "\n", "See Also\n", "--------\n", "numpy.argmax : equivalent function\n", "Type: builtin_function_or_method\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "solver.test_nets[0].blobs['ip2'].data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "solver.test_nets[0].blobs['ip2'].data.argmax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the train loss and test accuracy. 把这两百组 iter 的 loss 和 accuracy 显示一下。" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xwrPPypDVM88k75iKomQ+YcKwz8EKw/4LYrncFHxvCvA/SCKCWsQN9xPgszCH\neBCZJxptnStNzwIKlt+OVXxAPtKnD6xe7UO7IrBnD+yKlFFPURTFO+8gUWsDEPEBEZ4pwed/BEYA\nRyPBCOHEB8T15sRzqramJ0Bxut8Mw4fD0qUJbI8Hampg797kHlNRFCUCCUnV1vSi4KZPl/LbcTJi\nRPLHgQ4eVAFSFCWtSEiqtqYlQGHKb8fCiBHw0ksJbJMH1AJSFCXNqA4u327MTpqWC27mTJgwwbX8\ntldGjBAX3LZtUscuGdTUyDiQoihKNtG0LKDp0z3lf4vEgAGwcaNkyW7TBtasSVDbIqAuOEVRspGm\nYwEFAo0OQABo0QJuvBEefDB5kWnGBadpgBRFySaajgCtXSvZD8KU346Fxx+Hb39b5gMlQxRqaqTp\nBw/6fyxFUZRk0XQEaPr0qOW3Y6F5c2jdOjmusZoaeVQ3nKIo2UTTEaAEuN+cdOyYHDecsXw0EEFR\nlGyiaQiQKb+dYAHq1Ck5AqQWkKIo2UjTEKBFi6BbN8kBl0CSKUBt26oAKYqSXTQNAfLB/QbJE6CD\nB6GgQAVIUZTsomkIUALm/7iRTAuoa1cdA1IUJbvIfgE6dEgqoHosvx0LyRSgbt3UAlIUJbvIfgH6\n7DMYMsRz+e1YUAFSFEWJn+wXIJ/cb5DcMSAVIEVRso3sFyCfAhDAuwBVV8d/jNpayYKQn68CpChK\ndpHdArRnDyxZAiee6MvuvQjQ+vWSuDReamok40K7dhqEoChKdpHdAmTKb7du7cvuO3WC3bsjb7Nh\nA5SXx5/H7eBBS4DUAlIUJZvIbgHy0f0G3lLxbN4sj19+Gd8x7BaQCpCiKAngbGAlsAapZhqO44Fa\n4GK/GqIC1Ai8uOCMAJWXx3eMmhpo1Qrat1cBUhSl0TQD/oCI0DDgcmBomO0eBN7FKredcLJXgKqq\npHLcscf6dgjjgjMlGZYvh+OOC93GCND69dH3Z3K+OdfpGJCiKAliLFAGlANHgFeAC122+yHwGrDN\nz8ZkrwDNnCmTTxtRfjsaLVtKgbr9++X1v/8NCxaERr1t3gzDh0e3gLZuhT59JOrNjo4BKYqSQAqB\nCtvrjcF1zm0uBP4cfO1b1bPsLcnt4/wfO8YNl5cHb74pYrFoEUyYIO9v3ixBeNEsoIoK2LZNBOyE\nE6z10caADh8WEUxQmaO0Zds2mDhRghoVRXGntLSU0tLSSJt4EZPHgJ8Gt83BRxdcdgqQKb99xx2+\nH8oIUMsRxMFGAAAgAElEQVSWsHIlXHEFLFwYKkDXXw9//nPk/VRWyuPMmQ0FqFWr8AJ0xRVw0UVw\n1VWJ+T7pSmWliLSiKOEpKSmhxJZ27N5773Vusgkosr0uQqwgO8cirjmALsA5iLtuWgKbCmSrC27t\nWvFlJaD8djSMAL3zDpxxhojHwoXyXiDg3QKqqpKM1zNnhq43LrjWreHIEVnsbNwITz+duO+Truza\nBQcOpLoVTY8PPoCf/zzVrVASyHxgIFAMtAQuo6Gw9AP6BpfXgEku2ySE7BQg435Lgl/KCNC0aXDe\neTBmjCVA1dXiHuvfXwIIInWglZVwySWSuu7QIWu9ccHl5LhbQdu3y2fWrk38d0sndu1yF+B0p7TU\nClLJRFasgC++SHUrlARSC9wMvAcsB14FVgA3BZekkp0C5HP4tZ1OneBf/xIRuOACGDEC1qwRy2Xz\nZujZE3JzoXfvyIEIlZWSM3XIEJg711pvXHAQXoAuvxyeeQbuvhvuuy/hXzEtMOHubpGC6cyFF8K6\ndaluRfzs2CELSGHhO+7IbEFVAHgHGAwMAO4PrpsSXJxcC/zLr4ZknwDV18OHHyZVgF54AZ5/Xp63\nagUDB8LSpZYAAfTtG12AevSAU0+V5huMBQQNBejIEYnA+/GP4YEH4KWX4OOPE/4Vk04gAIsXh64z\nAmQiDjOBw4fF8jXje5nIzp1ykwPyG/zud+oKVRJH9gnQokVSvS3B5bfDcdxxYnWccYa1zrjh7AJU\nXBx5HKiqSgRo1ChYtcpab8aAoKEA7dgBnTtLrrl580QIk5Gd22/KyuDss0PXme+VSZ2fsRwyXYDM\n99i6VR6NIClKY/FbgKKlfCgBqoGFweUXjT5iEt1vAFdeCXfdFbpuwgR4441QAerXL/I4jbGAevYM\n7bDsFlBBAfzqVzLeBNIR5OfL8+OPhy5dpMPIdHbtku9md/VkogCZjjrTBWjPHrHmVICUROOnAHlN\n+TALGBNcftPooyZZgNy4/HKZz/PBB5YADR8ubjk3AgGxgLp3FxEy2RMgdAzomWckUOGSSyTIb/t2\nER1D587ZIUDV1fL97NaeClBqMNfTzp0yFwvSR4BeeSU0YEfJPPwUIK8pHxIXqnboEMye7Uv57Vho\n3Rp++EMJqe7RQ9aNHg3//a/79rt2ici0bi2C5RQgYwF17Spzirp2lW2cAtSxo6QGqq/353slC5Nh\n3N7RZaIAmQ47kwVoxw6Z47Z9e/pZQLfeKlF6SubipwB5SfkQAE4EFgNvI5ZS/Jjy2506NWo3iWDS\nJMmOYCygwkJxY2zZ0nBbM/4DknS0rs66+7ePARmKimRS5o4doQLUvDm0bZv5OeNMKiN7R7dzpwhs\nJgnQ9u2SXildBKi6OvYItp07YcAAudbSTYCqq602NSWefDLzpiOEw08B8nKpf4HMxB0N/B54PdyG\nkydP/moJm2oiDdxvhk6d4JNPYNw4eZ2TE94KMuM/Zjv7OJDdAjIUFckEVKcFBOKGy/RABGMBmcFv\nkO9UWJh5AjRyZPoI0De/CZ9+6n37ujq5mRkwwLKACgrcBWjrVpnzlCxqayUi0u2GLtu59db4s+un\nG34KkJeUD3sB06W8A7QAOrvtzC5AJeFcbEnK/+aV0aPFKjGMGtUwvBikg+re3XrtFCAzBmQwFlA4\nAXIbB3r66dD5RQC33y7zVGbPttZt2ZL6u0o3CyhTBWjECLFw04Ht22PrsKurJfKyoMCygIYOtVyL\ndmbOhIceSlxbo2Gs/FRfq8nm8GH5D2x09qQZip8C5CXlQwHWGNDY4PP4htF9Lr+dCEaNim4BQWgg\nQiQXXCwC9OSTodF6dXUyd+mEE+DMM60/9E9/KpNmL78cLr4YfvnL2L9nY9m9W4TbWECBgKzLRAEa\nOtTK4rBnT2on0u7dG5t1bML88/MtC2jYMHcLaO/e5Lp+zU1KOgpQVZVMi/AD871VgKLjJeXDJcAS\nYBGSgfXbcR/N5/LbiWD0aLGAPvtMMjsbf7x9DAhCAxHCueDCCVCnTg0F6MgRGaxdtUqi8wA+/xy6\ndYOf/UyCGsxd7datIlYlJaLl/3LMgQ4EJMR83Dh49tm4T0VEqqtl7MR0dPv3S0qjTBwDKiiQ87tl\nC/zv/8q5TRX79kUvIW9n504RoC5dRIy2bct8Aaqt9Wf85Be/sP5bU6fCr3+d+GOA9fupAHkjWsqH\nPwIjgKORYITP4j5SGo3/hGPYMFi9Gi67DObMscp0Oy0gL2NARoDMPCCD2xjQihXSof/kJ/Dww7Lu\nP/+Br39dnufnW9bGjh0waBDcdBN897uhEXkg4jlpkkzAfT3siF3jqK6W/Hn2GfidOkGbNpklQNu2\nifj06CG/Z2lpagfwY7WAdu6Ua8OLBbRnT2YI0BNPyFy6RDNrluXiLi/3TyDUAkpXMkCA2rSRlDyX\nXCKdvxkQ/vLL8C64RIwBLVok2RJuuEHS/Lz9Nrz1liVA5g4X5NGIWn6+dPh2t9HatWId3X23pP2p\nq4v9PBw+LEs4du8WATJtSrQAHTyYnFB18/v06CF3x+vWxWaBJJLaWvkdYxUgYwFVVYnADBoUvwX0\n8MMNCy7GS3W1WJexClBFhT+dd3W1lemkvNy/0iFqAaUjSSi/nSjefx8efFDcW59+Kp3skiWhNYDs\nFpDbGFBBgXQOVVWxCVD79mK1fOc7sGGDNVzmtIDMPnNyGk6MNdkdCgrkPXtQxX33wV//Gv0cPPAA\nTJ4c/n2/LaArrxQL0E8CgVAB+sc/ZFzLrwjFurrQHIJO9u2Tx3hccPn5Uuuqc2ex6HbsaBjObQQo\nXJh3TY0EvSQqIKO6WnIuxipA27eHRlcmCrsAbdggx/DDWt+9W8ZoVYDSiSSU304UvXpJR3TSSSJA\n06ZJHrm2ba1tnBaQU4CaNZNtamtFVOxEEiCQsZsXX5RgAxOhZ1wstbXSiXTsaH3WOTF20yYrzV5J\nidXp1ddL0b1PPol+DsrKxJUXDmMB+SVA5eX+l684cEAEvE0b+a1mzYJTTvHPApo7VyzrcBgBiicI\noUsXsdK7dZNJqW3ahJadBxGgurrwQRbG3ZwoF6RdgGKZ27Rtmz8CtHt3qAXUrp38V/w4zvDhKkDp\nRQa435yMGSMd8bPPyvwMO84gBKcLDsQN16VLw5JHziCEQCBUgADOOiu0WKyxgHbtgg4dQnW8sDD0\nj2QXoFNPteZ+zJ4tnYE9kWo4vvxSXFLh3GDGAjIdxc6diRUgYzD7id092qOHfNfzzw8vQK+9JtZu\nvMydK+cpnACYic3xjgGBCBDI93IKiXG/hXPDmXkrbiHc8VBdLVMXmjd3rxQcDj8soPp6acP69XJ9\n7t0r/28/3HC7d4sbdNeu7EhDlPkCFAik3fwfL7RsKR7DefOssRhD+/bWRe3mggNLgJw4gxC+/FIE\nrKAgfFuMANnHfwxOC8ieYHXCBBkHOnJE8nJ973veBejgQXcrpL5eOrF+/ayEpLt2yfdKhADV1UlE\nWjIEqGtXed69u1i4p57qLgBHjsDVVzeMOIwFMwDuDBox7N0rbYjHBdexo9S0iiRARgTCCdCGDfKY\nKAHavVtulrp1i80N54cA7dkjWU+OHBGXdO/esvghQNXV8h91usYzlcwXoCSW3040J50kc3CcbjQz\n9lJZ6e6Cg8gCZLeAFi6U8O9ImCAENwGKZAF16yZuuNNPh3/+U8KMDx60Olm3O9P6etnHaadZYat2\n9u0ToWnbVkR63z53F9ycOeHFKNIff8cOESG/BWjbNuv3GTNGcgN27eouAEuWyN3siy/Gf7y5c+X3\ncH4vIwj79kmnGE8QQm6uPEYToGbNGmcBvfqq93pW1dXxCdC2bfK93KzvffviK7Zn2tK3r3gE+vQR\nV7sf19ju3XJD4Nf+k03mC5BxvyWh/HaiufVWGTdxo3dvGTc4dCi8C84pFtBQgD77zEoHFA6vFlB9\nfcOQ8alTRUTHjZOULYMGiRU0a5b8IZ3jUVu2yB/opJNCBeijj8SQra62xqBMR2cEqG1bqyDdLbe4\nBxJUVUkbwrknqqrkbjWZLrjiYrj/fvkObgI0bx5ceqmIajypZbZulf1OmBB6s7Bxo2RiABGIoiI5\nl85Ods+eUBetwYwBgXwXY9GFE6CePUMF6O234RvfkOcbNlgpfcLx1lv+CtCBA3Lz0a6d++9wzjny\nG8SKEYW+fWVMtLjYilRNNCpA6UYGut8M+flyIbnx6KPw85/DUUe5a+sllzSsQwQNBWjOHPja16K3\nw7gmIllA27eLtWYXxGbNpJ1vvCGvBw8WAXrnHTFM77kndH9ffil/zmOPDRWghx+WgnrGtWJvl5sF\ntH27WHdO1q4VKyxc5vHKSrFIKiv9DcV2C5Fv1UqO6RzrmTdP3HMXXAAvvxz7sebOlXpQRUWhArRu\nndw8BAIiEF26yJiJc5xo5UpxHTmtFzMGBPLZaGNAhYUNBai0VI5fXi5zxyJZQDt2eC8nYgQollBs\nE+Fpj/q0s369zNOLFbsF9Omn/gtQhw6NFqBoddouRBJELwQWABPjPlIUMluAklx+O5kcfbT8gc87\nz/39Hj3co85bt5Y/fE2N+KQXLAgN8XbDqwVkd7+FwwjQBx9ISParr4bWQaqoEOvOCFB9vVg1H3wg\nn3NaQCY4wilA27a5C5CJRAqXCqWqSlwkHTv6m8bFTYBycqySGXbmzYOxYyU8/B//iP1Yc+fKb1xY\nGNopVVRYCUX37RPLr2PHhm64NWvk0dmhGRccSFJV4+UOZwE5BejDD+W3LSvzJkCxjM/EYwEZt6ib\nANXVybURT5JPc8327SvXp58uOHOsRuzfS5226UiC6DHAd4Gn4m1vNDJbgBYvlisqSeW3k81xx0l0\nVKyYQITFi+VPYSyKcNjHgJydphGgQEAEyAQghGPwYImIW7MGzj0XfvCD0LlBX34pAtS1q/yRFi8W\n8TGuO7sFZMJ/ly6VP5wRoJoa6dgWLWp4/HXr5K7488/d22fSHjW2g7j77sgpXexjQHY6dQoVABM9\nNXIkHHMMLF8e+zjEvHmWANktIBP6vGOHHKddu4bHB+uu334+6utDbwb+9Cdx8UFDATLpbQoKLAGq\nqpLr5rzzxK22fbvcVJnP1dc3/J6xWkAdO8YmQCYwxE2Atm0TEYpHgMw126+fvE5zF5yXOm37bc/z\nAN/yd2S2AGWw+81PjBvOi/sNpGOvr5cL2mkB5eVJHrbdu6VD8WIBmTkvLVuKiDotoKJgjvTbb5fB\n+ddfh+uuk8HuNWusTi8/X5KhlpRIIlcjQNu3ixAePtxwYuP69eKeDCdAJvN4YwRo5074zW8idzCm\ndIETpwW0YIF0zC1ayPc1UX+x8N//yj569QoVINM+pwA5LbA1a8Q9aP/stm2yrT2bu6GgIDQCa+9e\nuU46dLAEqLRUBOtrX5Nxwp495bwbC+iHP4THHw/db6IsoPJyGD++4WeMVeomQOb7NNYCAhGg/Hzr\nRgnEAi0ri33fTowADR4sN29xBE14qdMGcBGSu/Md4JZ42uqFzBagDJz/kwyMAM2e7S05eE6O/GFW\nr3YPbDBWkBcX3MCB8njGGfI4cqREeRmMBQRiHQH87W9SFmLQILmbt1tAYHVUdgHq2lXGcpxuuHXr\nZCylvNw9Ci8RFpCpwmksDDe2brXGTOw4BWDOHHG/gfwO/fvHNkl21y45J4WF7hZQTo4lQOFccKtX\nw8knh54P++/kpF8/y9UJIjrt28tiBGjmTBnXGjtWLNziYvk9jQAtXizZ2A0mW3isY0DdujUM3Cgr\ng/nzG3bOJjdfOAEaMqRxFlDfvvJf6dFDzrv9Gps6VaJE7QQCkVNShTtWx47yvzp4ULwGgQDceafn\nKQpeJet1xDV3PvBCbK30TuYKUJqU305HCgok6mrmTG8WEMifctUqdwEyHZsXF1zbthJ5dc458rp3\nb7n7Mx2LvWPLzYW//EUSnxYXy13dvHmWBXTVVRLMYMYhWreWP93WrdKZhROgQYPEYvrii4btq6pq\nvAW0fLn1XcIRToDsAlBXJ9//4out92MVoBUrpOSDKWRYVWXl56uokHOxc6f8Bm4uuEBABOjUU70L\nUN++0lGbIA5jXTkFaOJEGeurq7Osgl275PWqVSJiK1fK9jt2yPUQToCefNIK3qitled5efJbOgv+\nVVXJ+05himYBnXCC7CvWfHXGAmrbVs5hbrBXtbvhNm9uOG/nzTel7IlX6urEosrLk9/7vPMkcnD2\nbKnFtHYtlJaWhtROc8FLnTY7HwPNAZeeofFkrgClUfntdOP3v5dO7Xvfkw7IC126WJPcnPTsKR2F\nFxcciEvIHDcnRwTJWEEmCMEweLAVij54sAiIsYD69RORMeTmSlRgRYW7BXTokHT8vXpJVJhbIIIJ\nI+/VK34f/YoV0tmYyZVubNkSXoCMBTR1qmxz8snW+/36yTmIpS1Dg0PILVvK38G4pL78Us5RJBfc\n1q3yuVGjvAtQ27byPUzH7xQgUzBt+HDpLIcNk4H55s3lt127Vn6ra6+1ov527BCR2rnTPUz8f/7H\niprcs0eOl5NjTfi0f8a0y26lQXQB6tNHBC3WGxP7uKU9YtUpQE6hXLbMCgDxgrE0jcCdf76I2J/+\nJMfdtAlKSkqiCZCXOm39seq0HRN89CGBUSYLkLrfwtK9u2S+/u1vvU+PsmfAdjJpkox5fPZZdAsI\nGh7TCJCZpBouK4OJsrLnonPSpo10jsYCsgcibNhg5do79VQJ4HB2ZomygCZODG8B1dRIB+sW/GEE\nIBCQpLQ//Wno+XKzgOzh4s5Q6eXLLQECy1rdu1faMHBgZBfc6tVys+CMoIskQBAqlEYQjABt2CCf\nNR3lN74hNwQgv9vs2XLMK66Al16yErf27GlNPrbzySdyDsxYor3Dz8uT8St7UIQZF3S606K54Hr2\nFBGM1Q1nD9awY7/GKivlpsSePX7t2tBr6P/+L3J2eeN+M0ycKDdgb78tbmePmRG81Gn7JlKnbSHw\nOI2p0xaFzBUgDUBIKJEE6Gtfg/fekztEM9AaC2YcqKxMOrrcMFedEaBIUXtt2kgH17WrdGKVlVan\nvH69FYl04YXSAdtrFh04ID73xs6jWL5c8umFE6Bt28SycRN/IwALFogoOMPsnQJkrMn9+6Wj69cv\ntLS63QICS0hMsIfpbMO54NasEZFyng8vAmTa6bSANmyQjtzwq19Z6aa6dhVBGTxYAlT27ZO2Rpqj\nU1oqNy3GijbjP4bevUN/CzPO5xQSYwHZy48YGitAbtes0wKqrw8NmCgrk8+a5Sc/EasoHE4Bat1a\nRiC++U2xNmNIzROtTttDSJ22McB4IExIT+PJTAHKgPLbmUZ+vnTublkXwHJ3xePxNAJ0332S8ywc\nAwZIpx1NgIwF1KyZWFemJMS6dZZA5uZK2Ye77rJ8+sb6ycmxLIVYJ6Pu3Sud1/jx4QUo3PgPWC64\n+fMlUtApxv37h7rg3n5bOvRHH5XJurt3h87WdwqQiYQzrk7ToYdzwRkLqEsXETkzkB2LBbR3b2gQ\nQnl5qADZ6dpVJmsOHiy/w8CBsh9TXNEtm3tpKdx4o2UBOTv8Pn1C3aGVlXLT5CZAxgJyzmOyC5DT\ndRcNpzAY7AJUWSlttotEWZn8JhUVlphHysRgt/wMTz4Jjzwibfcj+7bfZKYAffSRhNekcfntTCM/\n333eSiIYOVI63I8/Ds3C7eSoo0RAorngNmwIzbNmxoHsFhCIldKjh2RYAEuAzH46d47dClq5UjrP\n4mLppO0uvtdft+5ywwmQEYBFi9xz9PXqJZ83qYSmTxfheewx8fX/4hdWiPn+/eLWsVulvXpZrh27\nBRRuHtDq1SICdlGG2AXIaQH16eP+ua5d5RyaMUKzHzMJOj8/VID27BGL8/rr5SYmEIguQFVVIkBO\nIYk0EdUE2CTSAjJWZSAQmoEDxE27Y4d0YxUVIka5udEFyPnf6NlTjl1YmJnJSTNTgNT9lnDMH9MP\nOncWIbj//tC6R268+qq4ZsLRpo0VhAChAmS3gEA61XvuEcurrq5h5dlBgxoOAtvrGW3c6D7mMmyY\ndLYtW1od2YYNMtaxZk34AASwXHCLF7vnX2veXIRj/XrppObOlcH6SZPECrDPcVq1SqxG+1ydSy8V\nwV20yLKATBSc2xiQ3YIyHWZNjXR2kTKoRxoDimQBmRsH4241+zHusc6dQ8Xhk0+kkzZjSpWV3iyg\nceNChaS+PrS8hP0YR47Ie926WQK0ebNlJR0+LOIfjmgW0K5d4lno398SiXXr5Fh9+8p1WVYmORUj\n1ckKN9YEagElFw1ASDhmHolffPGFpJqJxnHHyaTMcLRpIy41uwW0aJF0ErNmWXNqDBMmSEf66KMy\n8fWqq6z3Bg4MFaBVq8S1ZuaqfP/7sj/7mMyKFSJAIB2fccOZFDorV4afhArSgZgquKNGuW9jxoE+\n/VS2ad9egkDuv18CPysrpdNzut9ARPXSS2HKlOgW0KFDInTGGjHuu40b5Xm4sTqIbAGVl0e2gMCa\nL+a0gJwuuBkz5DfMyRFLeunShh1x796WAB08KG7EMWPkt6mvl/Nxyy3WpOo2bcQqOXBArqWqKhGf\nZs3k3H/yiTxed53s8913ZeJsuDk74SygTp3kM6tXy42PvdJxWZkcw4xflZXBRReJQNm/v91VGE7o\nINQC2rbNn2qsfpB5ApRB5bcziYkT48tD5pX8/MQkLG/TRh5NRzZihAjHtGlyV+0MksjJEbfVHXdI\nJ2SfczNwYGjyyVdflUcz2L14sVg148dbCTwXL5YBXwgd/H7lFUmlYwQokgtu2TJ5P9xYV79+0vG+\n+25DQ795c7GcFiyQ7+w2z+uee+SO21hApkNq2zZUgFavlrvwo46S18YCiuZ+A+lQq6vFDWgE6Kij\npMNfsybyGFBhoYiB+a5GgIwVbjrgQAD+/W8JKAErmtLNAjK/Q1WViL8JFX/zTXH/5uZKBnawJl7f\neqtsO2eOFd3Zu7f8hlVV4jLeulV+2/p6d9fcoUNiXbuNBuTkyE3AvHnWBFUjEmVlYr3aBWjwYIkW\nNFbQjh3ym5hUVpEEqKDAqmr8y1/C00+7b5duZJ4AZVD57UwiJyczhtSMC8+4C1u3lk7s7rvDBzic\ndZZYR7fdFrre7oILBKSjGTNGIs927pSO7r77pONYtEi2WbDAuvcxncfq1dKx3HSTWCXRghCOHHF3\nvxkmTZLjPPKIlVHCzvHHw1NPSQd5ww0N3y8okE5s4kTpqGtqRLhzc2UMrLpaOrNlyywxBensysq8\nCVBurjVgb4IQcnLksbo61NVpp29fEWqD3QVnLCDjHlu0SPZpxspGjpS5U888Y5WZgFAXnH2cr7hY\nMrXfeCM88YRM2jR07Squ2+uvl9/NPr1g4EA5bxdcIBOF335b2uA2QdiIYbibKyNATgto7VpLgCoq\n5DocMEBch2Yc6NVX5be+/XZ5Pn9++JuW5s3l/G3Z4q0ES7qQeQKk7rcmTZs20onb3XRjxkgn9q1v\nuX8mJ0cizpydhN0Ft3SpjJPccIPcZS9ZIh1eTo644ebNE/eUGawH6TzKy6Vzu/RS6cxXrow8BmQ6\nkEgCNHKkCObatdZdu53jjhNr9ec/Dz+mNmKE3KPl5IjV066drG/RQr7Pp582FKCLLpKyGv/5T3gX\nmp3+/UV8zRgQiAD17h3+/vCkk8RyMxQUiBVVXt7QAnrtNQkxNr/b2LEijr//fWgGga5dxcLbvz+0\nXlVxsVjH117bsB3/+IdE191/v7g53cqiXHMN3HuvHHfcOHcBimSVgOzXbgE5XXBFRXLTUl0t25SU\niMgeOiRjeXfdJRbQo4/K+TEZRtwoLJTrefXqyNdXOuGSajCNMeW3nbeySpOhTZuG0Xpjx8ofNtYQ\n8f79peOrrZU7zMsukzvdZ58VK8iM0djzmR17rNUh9u4tImAEoU0bEaA+fcILUIsW4n6KVqUWQiP6\n7IwfLxNt3awfN/LzQ8PNJ0wQgSsrk+9sKCqSybHXXw9nnx19v+PHi0PCuOBABMikTvJCTo5YRcuW\nhVpAgYB0xH/7m7Xt6NHuUYvG1bVhQ6gF1K+fiKp5bceeIeTNN93Hd049VX7Hyy4TUYxkAYWjqEhE\nwVhATheciXocPlysytNPlxuja6+Vm6ozzpBrxsyjikTPniLuo0ZZbtV0J7MEyJTfHjIk1S1RUkSb\nNtb4j2HSJEk7FCutWknntG6dJMZ86y3pDJcvF/eMmb1/wgmSVWLAgNChxzPOEHfQVVdZd/wtW8pd\ndzgBArHYzL7joahIOn6vmMzMhlNOkQwMu3aFWkAgHd/8+d5cOOedJx1j166hAhRu/Ccc/frJOe/U\nybKAli0Tq8breTJuOJPtHOQ+1cs8r/bt3dfn5kpAQmGhWIamWuuWLfKdc3O9CRCIOBQUyHjcl1/K\nd+zTR66XggK5tkDE9KmnxIK94orIATlOCgtFtO3jnOlOZrngMrj8tpIY3Cyg5s3jH78aOFDcG0VF\ncofdvr2Ix5tvWhbQ4MFyl/r++6HjF/n58J3vhLqbhgwRayySAH30kbeURokiP98SCBBBXbpUOmxn\nrsCcHAk5to+xhGPYMNl+6dLGC1CnTnIeTRTcc8+JsHv9q5tABJMFAWRfjZ3bZtyJ9gwVZ54pVjJ4\nc8GBtKlFC9n2rrskwq5lS+sYRoBABGnGDLGuY6FnTzkHmTL+A5kmQNOn6/hPE8fNAmoMAwdKxNCk\nSda6UaNkUNx0ws2aiZtt7tzowZdDh0onYzqXdMApQK1bixXWr1/j2mkyMh88aFkRPXo0DA2PRr9+\nVlBJ585iYbzwghUG7YXx42Usbvlyd5dbYzElKDZuFPfsK6/I+hUrIk9fsFtA5vG110JLMxQXW2Hp\nhlGjIt/EuGHaoQLkB1lcflvxzvnnex/78MKgQXL3feml1rqRI6VTsLtWxo6Vu2nToYRjyJDYOw6/\nyZHHVfkAAAcKSURBVM+3wp4NEyY0dL/FgxmbMAL31FMyWTYWBgywbipMNd/hw0Otgmhcc42Ea3/6\nafgIvMbQrp0szz0n0XHz54sYPflkZPevuV5Mm3r0kAAK+3X0+OORU1R5xRT9ixbBmE5kzhiQKb/t\nFq6iNBnMDPpE8fWvyx/XngPPLeKppESii6K5hMJFVKWS/HwZq7Bzyy0NK6PGQ0mJLEbg4vGOn3mm\nNazbooV09PGM6f32t2K1egnwiIf+/UVw7rtPog+vvFKsvXATikFuYt56y4pWvPPOhsKaKME88UQp\nbZJJIxSZ0tRA4KGHxGn9hz+kui2KEpZAwCoali4sWiSD325zitIRE9gRLjFuqrj6anjxRQl0mDtX\nIuzeestbhFqqyBE1Stt+PnMsoBkzZMaYoqQxOTnpJT6QOXNCDPFYP8lgwAAZO+veXcLU77038rwc\nJTppq4x2WkLgULt2YgFpBVRFUVLAF19IyH6sY1ypJN0toLRtmJ1TIDArXI1lRVEUxZV0F6CMiII7\nDTT6TVEUJctQAVIURVFSgt8CdDawElgD3BlmmyeC7y9GapA3YBS4Z2VUFEVRYiVav3wl0h//F/iU\nYBfsB34KUDPgD8iXHQZcDjjnSJ8LDAAGAjcCf3bb0TzIjFoBGUJpaWmqm5A16LlMLHo+fcdLv7wO\nOAURnl8DT/nVGD8FaCxQBpQDR4BXgAsd21wAPB98PhfoCDSoJTnDtyY2TfRPnjj0XCYWPZ++46Vf\nngOYqctzAd+mVvspQIVAhe31xuC6aNs0+LL/TnjTFEVRmiRe+mU71wNv+9UYPyeiBjxu5wwRbPC5\nlY1vi6IoiuK9XwY4FbgOyMgB+HHAu7bXd9FwwOtJ4Nu21ytxccEhJmNAF1100UWXmJYyQvHSL4OM\n/5QhY/QZSXNgLVAMtAQW4R6EYMy7ccBnyWqcoihKE8RLv9wbEZ8MKuzgzjnAKuTL3BVcd1NwMfwh\n+P5i4BgURVEUP4nWLz8D7AAWBhdNQaMoiqIoycTLRFYlMuXIhDL7nUxn4ANgNfA+Ev6uuPMssAVY\nYlsX6fzdhVyvK4Ezk9TGTMHtXE5GIrHM3bY9v7Sey8gUAR8Cy4ClwC3B9Xp9JoBmiIlYDLTA3Vep\nRGc9ckHaeQi4I/j8TuCBpLYosxiPZOiwd5rhzt8w5DptgVy3ZWRIuqsk4XYu7wF+4rKtnsvodAdM\nsY08xK02FL0+E8LXCI3W+GlwUWJjPZDvWGePNuyORrpHo5jQTjPc+XNGFL1LFgzkJphiGgrQrS7b\n6bmMndeB08mg6zOd1S/WCVOKOwFgOjAfuCG4rgBxhRB8dAt9V8IT7vz1RK5Tg16z3vghEoT0Fyx3\nkZ7L2ChGrMu5ZND1mc4CFEh1A7KEk5AL8xzgfxA3iB0zX0CJj2jnT89tZP4M9EVcSZXAIxG21XPp\nTh4wFfgRsNfxXlpfn+ksQJuQQTZDEaHqrXijMvi4DclqNBa5K+oeXN8D2JqCdmUy4c6f85rtFVyn\nhGcrVif5DHJ9gp5Lr7RAxOcFxAUHGXR9prMAzUeyZBcjE6YuA6alskEZSBugXfB5WyTqZQlyHr8T\nXP8drAtX8Ua48zcNyezRErmrH4jOoYhGD9vzb2CND+m5jE4O4rZcDjxmW6/XZ4JwmzCleKcvEvWy\nCAnTNOewMzIupGHY0XkZ2AwcRsYkryXy+fsZcr2uBM5KakvTH+e5vA74GzJNYDHSUdrHI/VcRuZk\noB75f5sw9rPR61NRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRlOzg0+BjH+DyBO/7\nZ2GOpSiKoihfUQK8GeNnmkd535mHS1EURVG+Yl/w8TNgNzJ7/EdIWqrfIalJFgM3BrcrAT4G3sBK\na/86kipqKVaG8QeA2uD+XnAcKye47yXIrP9v2fZdCvwTWAG8mIDvpyiKoqQpxkqZQKgFdCPw8+Dz\no4DPkTyEJYiQ9LFt2yn42BoRFfPaaQGZ199EUqLkAN2ADUiyyBJEBHsG35uNZC9XlCZBOicjVRQ/\nyXG8PhO4BrFgPkPyaQ0IvjcPEQ3Dj5D8W3OQ7MIDoxzrZOAlJOPzVmAWcHzw9TwkP1oguM/ieL6M\nomQi0XzaitKUuBn4wLGuBNjveH0aUknyIPAh0CrKfgM0FDxTh+WQbV0d+p9UmhBqASlNlb1YpSoA\n3gN+gCUAg5ByFk7aA7sQ8RlCaEnjI7gLyMdIOZFcoCtwCmL5OEVJUZoUerelNDWM5bEYsTgWAc8B\nTyDury8QYdiK1KdxVpR8F/g+UoNlFeKGMzyFBBksAK62fe7fwNeCxwwAtwf3P5SGFSm16qeiKIqi\nKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKE2b/wcbKcc9sHsrmwAAAABJ\nRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_, ax1 = subplots()\n", "ax2 = ax1.twinx()\n", "ax1.plot(arange(niter), train_loss)\n", "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n", "ax1.set_xlabel('iteration')\n", "ax1.set_ylabel('train loss')\n", "ax2.set_ylabel('test accuracy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n", "\n", "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(8):\n", " figure(figsize=(2, 2))\n", " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n", " figure(figsize=(10, 2))\n", " imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n", " xlabel('iteration')\n", " ylabel('label')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n", "\n", "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(8):\n", " figure(figsize=(2, 2))\n", " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n", " figure(figsize=(10, 2))\n", " imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n", " xlabel('iteration')\n", " ylabel('label')" ] } ], "metadata": { "description": "Define, train, and test the classic LeNet with the Python interface.", "example_name": "Learning LeNet", "include_in_docs": true, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" }, "priority": 2 }, "nbformat": 4, "nbformat_minor": 0 }