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@kyamagu
Created December 11, 2017 06:12
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Caffe2 Linear regression tutorial
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Raw
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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gjqQjzFtKQICiTrCaxW0OZIBfW/33BFLh1vGruG3/fy16/QGOmJCx5gBWDP0m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\nmVlBXPTNzAryBEKodtlR2KD7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f34a1072f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 今回は人工的にデータを生成し、雑音を加える\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"train_label = np.linspace(\n",
" -10, 10, 1000, dtype=np.float32).reshape(\n",
" (1000, 1))\n",
"train_data = np.concatenate((\n",
" -train_label + 4 * np.random.random((1000, 1)),\n",
" train_label + 3 * np.random.random((1000, 1))),\n",
" axis=1).astype(np.float32)\n",
"\n",
"# プロット、色の濃さはyの値\n",
"plt.scatter(train_data[:,0], train_data[:,1], c=train_label)\n",
"plt.grid(\"on\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from caffe2.python import brew, model_helper, optimizer, workspace\n",
"\n",
"# デプロイ用モデル、入力からfc線形和で出力\n",
"def build_deploy_model(model, data):\n",
" return brew.fc(model, data, \"pred\", 2, 1)\n",
"\n",
"# 学習用モデル、予測モデルに正解値からのバックプロパゲーションを加える\n",
"def build_train_model(model, data, label):\n",
" data = model.StopGradient(data)\n",
" label = model.StopGradient(label)\n",
" pred = build_deploy_model(model, data)\n",
" loss = model.net.SquaredL2Distance([pred, label], \"loss\")\n",
"\n",
" model.AddGradientOperators([loss])\n",
" opt = optimizer.build_sgd(model, base_learning_rate=1e-5)\n",
" for param in model.GetOptimizationParamInfo():\n",
" opt(model.net, model.param_init_net, param)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name: \"deploy_model\"\n",
"op {\n",
" input: \"data\"\n",
" input: \"pred_w\"\n",
" input: \"pred_b\"\n",
" output: \"pred\"\n",
" name: \"\"\n",
" type: \"FC\"\n",
" arg {\n",
" name: \"use_cudnn\"\n",
" i: 1\n",
" }\n",
" arg {\n",
" name: \"order\"\n",
" s: \"NCHW\"\n",
" }\n",
" arg {\n",
" name: \"cudnn_exhaustive_search\"\n",
" i: 0\n",
" }\n",
"}\n",
"external_input: \"data\"\n",
"external_input: \"pred_w\"\n",
"external_input: \"pred_b\"\n",
"\n",
"name: \"deploy_model_init\"\n",
"op {\n",
" output: \"pred_w\"\n",
" name: \"\"\n",
" type: \"XavierFill\"\n",
" arg {\n",
" name: \"shape\"\n",
" ints: 1\n",
" ints: 2\n",
" }\n",
"}\n",
"op {\n",
" output: \"pred_b\"\n",
" name: \"\"\n",
" type: \"ConstantFill\"\n",
" arg {\n",
" name: \"shape\"\n",
" ints: 1\n",
" }\n",
"}\n",
"\n"
]
}
],
"source": [
"deploy_model = model_helper.ModelHelper(\"deploy_model\")\n",
"build_deploy_model(deploy_model, \"data\")\n",
"print(str(deploy_model.net.Proto()))\n",
"print(str(deploy_model.param_init_net.Proto()))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch= 0, loss=0.316065\n",
"epoch= 1, loss=0.249106\n",
"epoch= 2, loss=0.246439\n",
"epoch= 3, loss=0.245851\n",
"epoch= 4, loss=0.245357\n",
"epoch= 5, loss=0.244891\n",
"epoch= 6, loss=0.244451\n",
"epoch= 7, loss=0.244036\n",
"epoch= 8, loss=0.243643\n",
"epoch= 9, loss=0.243271\n"
]
}
],
"source": [
"# workspaceとモデルの初期化\n",
"workspace.ResetWorkspace()\n",
"train_model = model_helper.ModelHelper(\"train_model\")\n",
"train_model = build_train_model(train_model, \"data\", \"label\")\n",
"workspace.FeedBlob(\"data\", train_data)\n",
"workspace.FeedBlob(\"label\", train_label)\n",
"workspace.RunNetOnce(train_model.param_init_net) # パラメータブロブの初期化\n",
"workspace.CreateNet(train_model.net) # モデルの内部ブロブの初期化\n",
"losses = []\n",
"for epoch in range(0, 10):\n",
" workspace.FeedBlob(\"data\", train_data) # ここでは1バッチ=1エポック\n",
" workspace.FeedBlob(\"label\", train_label) # 一般的にはここでミニバッチループ\n",
" workspace.RunNet(train_model.net, 10) # 10バッチ一度に実行\n",
" losses.append(workspace.FetchBlob(\"loss\").sum() / train_label.size)\n",
" print(\"epoch={:2g}, loss={:g}\".format(epoch, losses[-1]))\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from caffe2.python.predictor import predictor_exporter\n",
"\n",
"deploy_model = model_helper.ModelHelper(\"deploy_model\")\n",
"build_deploy_model(deploy_model, \"data\")\n",
"exporter = predictor_exporter.PredictorExportMeta(\n",
" predict_net=deploy_model.net.Proto(),\n",
" parameters=[str(param) for param in deploy_model.params],\n",
" inputs=[\"data\"],\n",
" outputs=[\"pred\"])\n",
"predictor_exporter.save_to_db(\"minidb\", \"/tmp/deploy.minidb\", exporter)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# テストデータを生成\n",
"test_label = np.linspace(\n",
" -10, 10, 100, dtype=np.float32).reshape(\n",
" (100, 1))\n",
"test_data = np.concatenate((\n",
" -test_label + 4 * np.random.random((100, 1)),\n",
" test_label + 3 * np.random.random((100, 1))),\n",
" axis=1).astype(np.float32)\n",
"\n",
"# 保存したモデルの読み込み\n",
"predict_net = predictor_exporter.prepare_prediction_net(\n",
" \"/tmp/deploy.minidb\", \"minidb\")\n",
"\n",
"# あとはデータを入力してRunNetOnce()\n",
"workspace.FeedBlob(\"data\", test_data)\n",
"workspace.RunNetOnce(predict_net)\n",
"prediction = workspace.FetchBlob(\"pred\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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U5/OXELEbllBwtGZXTRnryguYkBqbzXJxzlFsqtzF27tW4zZc2I7DhNTB/Gxy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\nDhL5tY9qAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f348a58d590>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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foxoorsycWfvz+OR0/jbjombdy6EopfCbjX/rEKKn6p5DEUSb85teXFFW4XIp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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f348a549f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# プロット、色の濃さはyの値\n",
"plt.scatter(test_data[:,0], test_data[:,1], c=test_label)\n",
"plt.title(\"Ground truth\")\n",
"plt.grid(\"on\")\n",
"plt.show()\n",
"\n",
"plt.figure()\n",
"plt.scatter(test_data[:,0], test_data[:,1], c=prediction)\n",
"plt.title(\"Prediction\")\n",
"plt.grid(\"on\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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