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4756 lines
1.3 MiB
4756 lines
1.3 MiB
3 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 9.1 图像增广"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T14:27:44.323427Z",
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"start_time": "2019-05-29T14:27:43.032842Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.0.0\n",
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"cuda\n"
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]
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}
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],
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"source": [
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"%matplotlib inline\n",
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"import os\n",
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"import time\n",
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"import torch\n",
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"from torch import nn, optim\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"import torchvision\n",
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"from PIL import Image\n",
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"\n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
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"\n",
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"import sys\n",
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|
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"sys.path.append(\"..\") \n",
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"import d2lzh_pytorch as d2l\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"print(torch.__version__)\n",
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"print(device)"
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]
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},
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|
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{
|
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|
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"cell_type": "markdown",
|
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|
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"metadata": {},
|
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"source": [
|
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|
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"## 9.1.1 常用的图像增广方法"
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|
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]
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},
|
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{
|
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"cell_type": "code",
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|
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"execution_count": 2,
|
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|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
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|
|
"end_time": "2019-05-29T14:27:44.619786Z",
|
||
|
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"start_time": "2019-05-29T14:27:44.326107Z"
|
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}
|
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7fa9fbbc70f0>"
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]
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},
|
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|
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"execution_count": 2,
|
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|
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"metadata": {},
|
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|
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"output_type": "execute_result"
|
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|
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},
|
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|
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{
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"data": {
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"image/svg+xml": [
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" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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"<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
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||
|
|
"source": [
|
||
|
|
"d2l.set_figsize()\n",
|
||
|
|
"img = Image.open('../../img/cat1.jpg')\n",
|
||
|
|
"d2l.plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2019-05-29T14:27:44.625705Z",
|
||
|
|
"start_time": "2019-05-29T14:27:44.621800Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
|
||
|
|
"def show_images(imgs, num_rows, num_cols, scale=2):\n",
|
||
|
|
" figsize = (num_cols * scale, num_rows * scale)\n",
|
||
|
|
" _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n",
|
||
|
|
" for i in range(num_rows):\n",
|
||
|
|
" for j in range(num_cols):\n",
|
||
|
|
" axes[i][j].imshow(imgs[i * num_cols + j])\n",
|
||
|
|
" axes[i][j].axes.get_xaxis().set_visible(False)\n",
|
||
|
|
" axes[i][j].axes.get_yaxis().set_visible(False)\n",
|
||
|
|
" return axes"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2019-05-29T14:27:44.633413Z",
|
||
|
|
"start_time": "2019-05-29T14:27:44.627482Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):\n",
|
||
|
|
" Y = [aug(img) for _ in range(num_rows * num_cols)]\n",
|
||
|
|
" show_images(Y, num_rows, num_cols, scale)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### 9.1.1.1 翻转和裁剪"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2019-05-29T14:27:44.909361Z",
|
||
|
|
"start_time": "2019-05-29T14:27:44.635038Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
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|
|
"data": {
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"source": [
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"shape_aug = torchvision.transforms.RandomResizedCrop(200, scale=(0.1, 1), ratio=(0.5, 2))\n",
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"apply(img, shape_aug)"
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]
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"source": [
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"### 9.1.1.2 变化颜色"
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]
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},
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{
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"ExecuteTime": {
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"end_time": "2019-05-29T14:27:45.711617Z",
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"start_time": "2019-05-29T14:27:45.454846Z"
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}
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},
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"outputs": [
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]
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},
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}
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],
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"source": [
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"color_aug = torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n",
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|
"apply(img, color_aug)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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|
|
"### 9.1.1.3 叠加多个图像增广方法"
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|
]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-29T14:27:47.176935Z",
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"start_time": "2019-05-29T14:27:46.796137Z"
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}
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},
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"outputs": [
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{
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"data": {
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}
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],
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"source": [
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"augs = torchvision.transforms.Compose([\n",
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|
|
" torchvision.transforms.RandomHorizontalFlip(), color_aug, shape_aug])\n",
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"apply(img, augs)"
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]
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"## 9.1.2 使用图像增广训练模型"
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]
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},
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{
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"name": "stdout",
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"Files already downloaded and verified\n"
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]
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|
|
"### 9.1.2.1 使用图像增广训练模型"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2019-05-29T14:27:51.010527Z",
|
||
|
|
"start_time": "2019-05-29T14:27:51.005395Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
|
||
|
|
"def train(train_iter, test_iter, net, loss, optimizer, device, num_epochs):\n",
|
||
|
|
" net = net.to(device)\n",
|
||
|
|
" print(\"training on \", device)\n",
|
||
|
|
" batch_count = 0\n",
|
||
|
|
" for epoch in range(num_epochs):\n",
|
||
|
|
" train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()\n",
|
||
|
|
" for X, y in train_iter:\n",
|
||
|
|
" X = X.to(device)\n",
|
||
|
|
" y = y.to(device)\n",
|
||
|
|
" y_hat = net(X)\n",
|
||
|
|
" l = loss(y_hat, y)\n",
|
||
|
|
" optimizer.zero_grad()\n",
|
||
|
|
" l.backward()\n",
|
||
|
|
" optimizer.step()\n",
|
||
|
|
" train_l_sum += l.cpu().item()\n",
|
||
|
|
" train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()\n",
|
||
|
|
" n += y.shape[0]\n",
|
||
|
|
" batch_count += 1\n",
|
||
|
|
" test_acc = d2l.evaluate_accuracy(test_iter, net)\n",
|
||
|
|
" print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'\n",
|
||
|
|
" % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2019-05-29T14:27:51.016923Z",
|
||
|
|
"start_time": "2019-05-29T14:27:51.012423Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def train_with_data_aug(train_augs, test_augs, lr=0.001):\n",
|
||
|
|
" batch_size, net = 256, d2l.resnet18(10)\n",
|
||
|
|
" optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
|
||
|
|
" loss = torch.nn.CrossEntropyLoss()\n",
|
||
|
|
" train_iter = load_cifar10(True, train_augs, batch_size)\n",
|
||
|
|
" test_iter = load_cifar10(False, test_augs, batch_size)\n",
|
||
|
|
" train(train_iter, test_iter, net, loss, optimizer, device, num_epochs=10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2019-05-29T14:48:32.502485Z",
|
||
|
|
"start_time": "2019-05-29T14:27:51.018678Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Files already downloaded and verified\n",
|
||
|
|
"Files already downloaded and verified\n",
|
||
|
|
"training on cuda\n",
|
||
|
|
"epoch 1, loss 1.3615, train acc 0.505, test acc 0.493, time 123.2 sec\n",
|
||
|
|
"epoch 2, loss 0.5003, train acc 0.645, test acc 0.620, time 123.0 sec\n",
|
||
|
|
"epoch 3, loss 0.2811, train acc 0.703, test acc 0.616, time 123.1 sec\n",
|
||
|
|
"epoch 4, loss 0.1890, train acc 0.735, test acc 0.686, time 123.0 sec\n",
|
||
|
|
"epoch 5, loss 0.1346, train acc 0.765, test acc 0.671, time 123.1 sec\n",
|
||
|
|
"epoch 6, loss 0.1029, train acc 0.787, test acc 0.674, time 123.1 sec\n",
|
||
|
|
"epoch 7, loss 0.0803, train acc 0.804, test acc 0.749, time 123.1 sec\n",
|
||
|
|
"epoch 8, loss 0.0644, train acc 0.822, test acc 0.717, time 123.1 sec\n",
|
||
|
|
"epoch 9, loss 0.0526, train acc 0.836, test acc 0.750, time 123.0 sec\n",
|
||
|
|
"epoch 10, loss 0.0433, train acc 0.851, test acc 0.754, time 123.1 sec\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"train_with_data_aug(flip_aug, no_aug)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python [conda env:py36]",
|
||
|
|
"language": "python",
|
||
|
|
"name": "conda-env-py36-py"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.6.8"
|
||
|
|
},
|
||
|
|
"varInspector": {
|
||
|
|
"cols": {
|
||
|
|
"lenName": 16,
|
||
|
|
"lenType": 16,
|
||
|
|
"lenVar": 40
|
||
|
|
},
|
||
|
|
"kernels_config": {
|
||
|
|
"python": {
|
||
|
|
"delete_cmd_postfix": "",
|
||
|
|
"delete_cmd_prefix": "del ",
|
||
|
|
"library": "var_list.py",
|
||
|
|
"varRefreshCmd": "print(var_dic_list())"
|
||
|
|
},
|
||
|
|
"r": {
|
||
|
|
"delete_cmd_postfix": ") ",
|
||
|
|
"delete_cmd_prefix": "rm(",
|
||
|
|
"library": "var_list.r",
|
||
|
|
"varRefreshCmd": "cat(var_dic_list()) "
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"types_to_exclude": [
|
||
|
|
"module",
|
||
|
|
"function",
|
||
|
|
"builtin_function_or_method",
|
||
|
|
"instance",
|
||
|
|
"_Feature"
|
||
|
|
],
|
||
|
|
"window_display": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|