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"cells": [
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"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/bipinKrishnan/fastai_course/blob/master/cyclicLR_scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YYwXusZUtn3T"
},
"source": [
"import torch\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from random import random\n",
"from tqdm.notebook import tqdm"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "CDsEi5grAoUU"
},
"source": [
"## Cyclical lr from scratch"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-mkOEVvxy0KD"
},
"source": [
"def cyclical_lr_np(iter, step_size, base_lr, max_lr):\n",
" cycle = np.floor(1 + (iter / (2*step_size)))\n",
" x = np.abs((iter/step_size) - 2*cycle + 1)\n",
"\n",
" lr = base_lr + (max_lr-base_lr) * (np.max((0, (1-x))))\n",
"\n",
" return lr"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wm1fnqKXUOdK",
"outputId": "28615491-2d9b-4482-83ed-55a1cb325ed1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
}
},
"source": [
"lr = []\n",
"for i in range(1100):\n",
" lr.append(cyclical_lr_np(i, 200, 0.0001, 0.001))\n",
"\n",
"plt.plot(lr, linewidth=2.5)\n",
"plt.xlabel(\"Iterations\", fontsize=13)\n",
"plt.ylabel(\"Learning rate\", fontsize=13)\n",
"plt.yticks(rotation=60)\n",
"plt.savefig(\"foo1.jpg\")"
],
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nNW7Hg-4AzLI"
},
"source": [
"## Train a PyTorch model using above function"
]
},
{
"cell_type": "code",
"metadata": {
"id": "v2HZGkTdA3Ab"
},
"source": [
"from torch import nn, optim\n",
"from torchvision import datasets, models\n",
"from torch.utils.data import DataLoader, Dataset\n",
"from torchvision.transforms import transforms"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "joBTUfNRewFn"
},
"source": [
"transform = transforms.Compose([\n",
" transforms.ToPILImage(),\n",
" transforms.ToTensor() \n",
"])\n",
"\n",
"dataset = datasets.MNIST('./data', train=False, download=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SxEDUbT8QVp4"
},
"source": [
"class LoadData(Dataset):\n",
" def __init__(self, data, target, transform):\n",
" super().__init__()\n",
" self.data = data\n",
" self.target = target\n",
" self.transform = transform\n",
"\n",
" def __len__(self):\n",
" return len(self.data)\n",
"\n",
" def __getitem__(self, idx):\n",
" return (self.transform(self.data[idx]), self.target[idx])\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lGO8iJb3PYnT"
},
"source": [
"bs = 32\n",
"trainloader = DataLoader(LoadData(dataset.data[:8000], dataset.targets[:8000], transform), bs, shuffle=True)\n",
"valloader = DataLoader(LoadData(dataset.data[8000:], dataset.targets[8000:], transform), bs)"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7--IsYcTfb2o",
"outputId": "daf38c94-563c-4838-8c05-3d5d15e60b4c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"for data, label in trainloader:\n",
" print(data.shape, label.shape)\n",
" break\n",
"\n",
"for data, label in valloader:\n",
" print(data.shape, label.shape)\n",
" break"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"torch.Size([32, 1, 28, 28]) torch.Size([32])\n",
"torch.Size([32, 1, 28, 28]) torch.Size([32])\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gd-XjZgIEDQK"
},
"source": [
"def validate(dataloader):\n",
" correct = 0.0\n",
" for inp, output in dataloader:\n",
" out = model(inp.to(device))\n",
"\n",
" loss = criterion(out, output.to(device))\n",
"\n",
" out = torch.argmax(out.data, 1)\n",
"\n",
" correct += (out==output.to(device)).sum()\n",
"\n",
" return loss.item(), (correct.item()/(bs*len(valloader)))"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qnrCd2WDfsFz"
},
"source": [
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"\n",
"model = models.resnet18(pretrained=False)\n",
"\n",
"# uncoment the below code to download pretrained model & freeze the layers\n",
"# model = models.resnet18(pretrained=True)\n",
"# for params in model.parameters():\n",
"# params.requires_grad = False\n",
"\n",
"model.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
"model.fc = nn.Linear(model.fc.in_features, 10)\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"opt = optim.Adam(model.parameters())"
],
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "e7TNXPQEhZ2N"
},
"source": [
"model = model.to(device)"
],
"execution_count": 30,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qRsxBTW_uWYo"
},
"source": [
"## LR range test"
]
},
{
"cell_type": "code",
"metadata": {
"id": "57LbfsIYuaH5"
},
"source": [
"!pip install torch-lr-finder"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3OtSzfsEuaMO"
},
"source": [
"from torch_lr_finder import LRFinder"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zEoQcEdmumql"
},
"source": [
"lr_finder = LRFinder(model, opt, criterion, device=device)\n",
"lr_finder.range_test(trainloader, valloader, start_lr=1e-07, end_lr=10)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "D8JnIXkMvVJ_",
"outputId": "bd2ff0d6-42db-4370-a0da-3c33de6179c8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 318
}
},
"source": [
"lr_finder.plot()\n",
"lr_finder.reset()"
],
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": [
"LR suggestion: steepest gradient\n",
"Suggested LR: 5.21E-04\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q4FgzRf68jTo"
},
"source": [
"Training using cyclical lr"
]
},
{
"cell_type": "code",
"metadata": {
"id": "r9_WFforiFu5",
"outputId": "c4219a27-62ab-4ada-875e-6d7a0b19d4fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295,
"referenced_widgets": [
"da3e801b65c14b0b917cd302c5dbbe2e",
"cc3b7e8f67834d8cad470e99f7a733bf",
"c25a84f7f6dd40f98735b99d7aee9be0",
"207cd356dcac4aab8dbc370e27d02f05",
"6c9730c18f084e6b96834cc1762c0938",
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]
}
},
"source": [
"epochs = 8\n",
"acc = []\n",
"lr = []\n",
"iter_counter = 0\n",
"\n",
"for i in tqdm(range(epochs), total=epochs, leave=False):\n",
" model.train()\n",
" for data, labels in tqdm(trainloader, total=len(trainloader), leave=False):\n",
"\n",
" #updating the learning rate\n",
" opt.param_groups[0]['lr'] = cyclical_lr_np(iter_counter, 2*len(trainloader), 1e-6, 3e-2)\n",
"\n",
" opt.zero_grad()\n",
"\n",
" out = model(data.to(device))\n",
" loss = criterion(out, labels.to(device))\n",
" loss.backward()\n",
"\n",
" opt.step()\n",
"\n",
" lr.append(opt.param_groups[0]['lr'])\n",
" iter_counter += 1\n",
"\n",
" model.eval()\n",
" valid = validate(valloader)\n",
" acc.append(valid[1])\n",
"\n",
" print(f\"Epoch: {i+1}/{epochs}\\ttrain_loss: {loss.item()}\\tval_loss: {valid[0]}\\tacc: {valid[1]}\\n\") "
],
"execution_count": 61,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "da3e801b65c14b0b917cd302c5dbbe2e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=8.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c8ea057bf15458cb53c8d5177d73b98",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 1/8\ttrain_loss: 1.9550628662109375\tval_loss: 1.7536709308624268\tacc: 0.6463293650793651\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a97b5d9ec60746a0b6ab8f6c601c3420",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 2/8\ttrain_loss: 0.018391886726021767\tval_loss: 0.9135429859161377\tacc: 0.8452380952380952\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8d7671bc580b400c88e38756e3bca76f",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 3/8\ttrain_loss: 0.7393415570259094\tval_loss: 0.0035819108597934246\tacc: 0.9702380952380952\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "30c099e567244fb5bdc9c1ae1b073911",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 4/8\ttrain_loss: 0.0012923687463626266\tval_loss: 0.0001602256525075063\tacc: 0.9811507936507936\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37165ce8f0684c89b969ea2e86d30e44",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 5/8\ttrain_loss: 0.0018894554814323783\tval_loss: 0.0033130920492112637\tacc: 0.9801587301587301\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2e209664b6a947baae17797066bb248b",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 6/8\ttrain_loss: 0.1019459217786789\tval_loss: 0.01210165023803711\tacc: 0.9241071428571429\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c763e3c6559469c8e2d3533d0242a61",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 7/8\ttrain_loss: 0.0006356866215355694\tval_loss: 0.0003234919277019799\tacc: 0.9776785714285714\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d7d637235c347f187b345b9ba6c2dca",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 8/8\ttrain_loss: 0.012749851681292057\tval_loss: 0.00015056262782309204\tacc: 0.9826388888888888\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IbJQxdNK8oFi"
},
"source": [
"Training with out cyclical lr"
]
},
{
"cell_type": "code",
"metadata": {
"id": "E3wmSpF29JQr"
},
"source": [
"model = models.resnet18(pretrained=False)\n",
"\n",
"# for params in model.parameters():\n",
"# params.requires_grad = False\n",
"\n",
"model.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
"model.fc = nn.Linear(model.fc.in_features, 10)\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"opt = optim.Adam(model.parameters())"
],
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "t5Vjn8nU9R1E"
},
"source": [
"model = model.to(device)"
],
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "to1FdmCT8rX-",
"outputId": "dc37fdf8-03d9-418f-bd39-0df0ad2519c5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295,
"referenced_widgets": [
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]
}
},
"source": [
"epochs = 8\n",
"acc1 = []\n",
"\n",
"for i in tqdm(range(epochs), total=epochs, leave=False):\n",
" model.train()\n",
" for (data, labels) in tqdm(trainloader, total=len(trainloader), leave=False):\n",
" opt.zero_grad()\n",
"\n",
" out = model(data.to(device))\n",
" loss = criterion(out, labels.to(device))\n",
" loss.backward()\n",
"\n",
" opt.step()\n",
"\n",
" model.eval()\n",
" valid = validate(valloader)\n",
" acc1.append(valid[1])\n",
"\n",
" print(f\"Epoch: {i+1}/{epochs}\\ttrain_loss: {loss.item()}\\tval_loss: {valid[0]}\\tacc: {valid[1]}\\n\") "
],
"execution_count": 31,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ac5884abbc646dea8384f429fb41eef",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=8.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37c5d34f659243369d5a4664bdb89e33",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 1/8\ttrain_loss: 0.10663249343633652\tval_loss: 0.046826764941215515\tacc: 0.9449404761904762\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "471326d64205447db70c3bc31e7f0d92",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 2/8\ttrain_loss: 0.15328238904476166\tval_loss: 0.18664966523647308\tacc: 0.9662698412698413\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2bc614708ad44615b0cd5f7f0578102f",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 3/8\ttrain_loss: 0.019105538725852966\tval_loss: 0.006364534143358469\tacc: 0.9464285714285714\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bbd65051d234408a9f8a04bbd37637ff",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 4/8\ttrain_loss: 0.04831218346953392\tval_loss: 0.00444094929844141\tacc: 0.9702380952380952\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c759f387c7354f8a82f05587dade6954",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 5/8\ttrain_loss: 0.106694795191288\tval_loss: 0.005443398840725422\tacc: 0.9737103174603174\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7241ed565d374b26bef94a60f4a2c719",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 6/8\ttrain_loss: 0.004056185949593782\tval_loss: 0.004668809473514557\tacc: 0.9742063492063492\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b10d1605a0f744a5895323d81c4bb42f",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 7/8\ttrain_loss: 0.005273723043501377\tval_loss: 0.0004423517675604671\tacc: 0.9672619047619048\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c79c753938ea4c438684928f362df24e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Epoch: 8/8\ttrain_loss: 0.033707428723573685\tval_loss: 0.003034663386642933\tacc: 0.9766865079365079\n",
"\n",
"\r"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Id1jRojnQvOY"
},
"source": [
"## Comparing the results"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lU3f2TPkAjZ3",
"outputId": "683d7c2f-a5c4-430a-affc-4994227cca22",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 392
}
},
"source": [
"plt.figure(figsize=(10, 6))\n",
"\n",
"plt.plot(acc, 'r-', label='Cyclical LR', linewidth=2)\n",
"plt.plot(acc1, 'b-', label='Fixed LR', linewidth=2)\n",
"\n",
"plt.xlabel(\"Iterations\", fontsize=14)\n",
"plt.ylabel(\"Accuracy\", fontsize=14)\n",
"plt.legend()\n",
"plt.savefig(\"result.jpg\")"
],
"execution_count": 28,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w7cdbADquacx"
},
"source": [
"## Fast ai"
]
},
{
"cell_type": "code",
"metadata": {
"id": "okH44smJncBG"
},
"source": [
"!pip install fastai --upgrade"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZnfPMmw4rh9U"
},
"source": [
"from fastai.vision.all import *"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JaeZsQ3NurUz",
"outputId": "375555d6-9d19-457c-f193-0d1b0883f3b5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"path = untar_data(URLs.MNIST)\n",
"path.ls()[1]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Path('/root/.fastai/data/mnist_png/training')"
]
},
"metadata": {
"tags": []
},
"execution_count": 276
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7R7m9QG8uw-D"
},
"source": [
"def splitter(x): return str(x.parent.parent.name) == 'testing'\n",
"\n",
"db = DataBlock(\n",
" blocks=(ImageBlock, CategoryBlock),\n",
" get_items=get_image_files,\n",
" get_y=parent_label\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5ydaPNQMvI4O"
},
"source": [
"ds = db.datasets(path.ls()[0])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "p1Up0sv0KAdJ"
},
"source": [
"ds.train, ds.valid"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KaWFZlnpvOpU"
},
"source": [
"dls = db.dataloaders(path.ls()[0], bs=32)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KTHyXNwGvV1S",
"outputId": "6673b858-9b00-406d-da65-ba2408ac0e78",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 536
}
},
"source": [
"dls.show_batch()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x648 with 9 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "E2H2imEavepF"
},
"source": [
"db.summary(path)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zdRRLIX4vifs"
},
"source": [
"learn = Learner(dls, resnet18(pretrained=False), loss_func=CrossEntropyLossFlat(), metrics=accuracy)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mn1sO1kGOQmz",
"outputId": "33417020-ee28-4d73-919b-26a94e83339c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"learn.lr_find()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
""
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"SuggestedLRs(lr_min=0.02089296132326126, lr_steep=0.0010000000474974513)"
]
},
"metadata": {
"tags": []
},
"execution_count": 284
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ryg_Jg4FUYrJ",
"outputId": "b555c920-a0ba-418e-9496-81cc96046e09",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"learn.fit_one_cycle(8, base_lr=3e-3, max_lr=1e-2)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.350900</td>\n",
" <td>0.586152</td>\n",
" <td>0.840500</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.188595</td>\n",
" <td>0.284602</td>\n",
" <td>0.913000</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.213820</td>\n",
" <td>0.122601</td>\n",
" <td>0.962500</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.102351</td>\n",
" <td>0.079087</td>\n",
" <td>0.977000</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.072047</td>\n",
" <td>0.083941</td>\n",
" <td>0.976500</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.026256</td>\n",
" <td>0.040823</td>\n",
" <td>0.985500</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.013390</td>\n",
" <td>0.030870</td>\n",
" <td>0.989000</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>0.004982</td>\n",
" <td>0.028754</td>\n",
" <td>0.990000</td>\n",
" <td>00:33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
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