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anand086/full_mnist_with_sgd_and_nn.ipynb

Created July 25, 2021 07:32
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full_mnist_with_sgd_and_nn.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"!pip install -Uqq fastbook\n",
"import fastbook\n",
"fastbook.setup_book()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from fastai.vision.all import *\n",
"from fastbook import *\n",
"\n",
"matplotlib.rc('image', cmap='Greys')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Untar MNIST dataset provided by Fastai\n",
"#path = untar_data(URLs.MNIST_SAMPLE)\n",
"path = untar_data(URLs.MNIST)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Path('/home/ec2-user/.fastai/data/mnist_png')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"Path.BASE_PATH = path"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#2) [Path('training'),Path('testing')]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# find direcrtory inside path using ls method\n",
"# This method returns an object if a special fastai class called L, which has all the same functionality\\\n",
"# of Python's built-in list, plus a lot more,\n",
"path.ls()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#10) [Path('training/6'),Path('training/0'),Path('training/5'),Path('training/1'),Path('training/7'),Path('training/8'),Path('training/3'),Path('training/2'),Path('training/9'),Path('training/4')]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lets see inside training folder\n",
"# It has folder for each digit 0 - 9\n",
"(path/'training').ls()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#10) [Path('training/0'),Path('training/1'),Path('training/2'),Path('training/3'),Path('training/4'),Path('training/5'),Path('training/6'),Path('training/7'),Path('training/8'),Path('training/9')]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prepare training and testing data\n",
"# sorted() to ensure we all get the same order of files\n",
"training_set = (path/'training').ls().sorted()\n",
"testing_set = (path/'testing').ls().sorted()\n",
"training_set"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2160x216 with 10 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# get sample of each digit using Image class from Python Imaging Library (PIL)\n",
"sample_images = [Image.open(training_set[i].ls()[i]) for i in range(10)]\n",
"show_images(sample_images)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# For each digit folder in training set\n",
"# convert the image to list of 2-D tensor and then use torch.stack to stack them to 3-D tensor \n",
"\n",
"training_data = [torch.stack([tensor(Image.open(o)) for o in path.ls()]).float()/255 for path in training_set]\n",
"testing_data = [torch.stack([tensor(Image.open(o)) for o in path.ls()]).float()/255 for path in testing_set]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Digit 0 shape: torch.Size([5923, 28, 28])\n",
"Digit 1 shape: torch.Size([6742, 28, 28])\n",
"Digit 2 shape: torch.Size([5958, 28, 28])\n",
"Digit 3 shape: torch.Size([6131, 28, 28])\n",
"Digit 4 shape: torch.Size([5842, 28, 28])\n",
"Digit 5 shape: torch.Size([5421, 28, 28])\n",
"Digit 6 shape: torch.Size([5918, 28, 28])\n",
"Digit 7 shape: torch.Size([6265, 28, 28])\n",
"Digit 8 shape: torch.Size([5851, 28, 28])\n",
"Digit 9 shape: torch.Size([5949, 28, 28])\n"
]
}
],
"source": [
"# shape of \n",
"for i in range(10):\n",
" print('Digit ' + str(i) + ' shape:', training_data[i].shape)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# training digit stacks\n",
"stacked_zeros = training_data[0]\n",
"stacked_ones = training_data[1]\n",
"stacked_twos = training_data[2]\n",
"stacked_threes = training_data[3]\n",
"stacked_fours = training_data[4]\n",
"stacked_fives = training_data[5]\n",
"stacked_sixs = training_data[6]\n",
"stacked_sevens = training_data[7]\n",
"stacked_eights = training_data[8]\n",
"stacked_nines = training_data[9]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# concatenate stacks for training data\n",
"train_x = torch.cat([stacked_zeros, \n",
" stacked_ones,\n",
" stacked_twos,\n",
" stacked_threes,\n",
" stacked_fours,\n",
" stacked_fives,\n",
" stacked_sixs,\n",
" stacked_sevens,\n",
" stacked_eights,\n",
" stacked_nines]).view(-1, 28*28)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([60000, 784])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# train dataset shape\n",
"train_x.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# generate the labels\n",
"train_y = []\n",
"for i in range(len(training_set)):\n",
" #print(training_data[i].shape)\n",
" train_y += tensor([i]*training_data[i].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# convert list to tensor\n",
"train_y = tensor(train_y)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0, 0, 0, ..., 9, 9, 9])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_y"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# create the dataset\n",
"train_dset = list(zip(train_x, train_y))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0275, 0.3490, 0.5098, 0.5098, 0.8706, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.5569, 0.0588, 0.0039, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0314, 0.3373, 0.5922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922,\n",
" 0.7765, 0.0235, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0667, 0.8039, 0.9922, 0.9922, 0.9922, 0.9922, 0.9804, 0.9216, 0.9216, 0.9216, 0.9216, 0.7647,\n",
" 0.8392, 0.9216, 0.9490, 0.9922, 0.9922, 0.0824, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0157, 0.8039, 0.9922, 0.9922, 0.9922, 0.9294, 0.5725, 0.3333, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1608, 0.9373, 0.9922, 0.8039, 0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3255, 0.9922, 0.9843, 0.5843, 0.3882,\n",
" 0.1412, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3020, 0.9569, 0.9922, 0.7098, 0.0078, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.4431,\n",
" 0.9922, 0.8471, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0549, 0.6549, 0.9922, 0.9922, 0.5294, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0235, 0.8235, 0.3608, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0549, 0.1725, 0.7098, 0.9922, 0.9922, 0.9922, 0.4510, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1373, 0.1961, 0.1961, 0.3294, 0.6824, 0.7804, 0.9922, 0.9922, 0.9922, 0.9137,\n",
" 0.5843, 0.0157, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.6824, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922,\n",
" 0.9922, 0.9922, 0.9059, 0.4549, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0863, 0.8353, 0.9922,\n",
" 0.9922, 0.9922, 0.9922, 0.9922, 0.6235, 0.2667, 0.1608, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.2627, 0.7255, 0.9216, 0.9922, 0.9922, 0.9922, 0.6275, 0.0392, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1608, 0.5843, 0.9294, 0.9922, 0.9922, 0.2863, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1333, 0.8824, 0.9922, 0.9922, 0.6275, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1333, 0.6667, 0.9922, 0.9333, 0.5020, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.4980,\n",
" 0.9922, 0.9922, 0.4275, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.5569, 0.2980, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.8039, 0.9922, 0.9922, 0.1176, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.6314, 0.9333, 0.5647, 0.1216, 0.0000, 0.1137, 0.4157, 0.6431, 0.9922, 0.9922, 0.5569, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.1137, 0.9686, 0.9922, 0.9922, 0.9451, 0.9255, 0.9451, 0.9922, 0.9922, 0.9922, 0.9922, 0.1529, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0667, 0.5961, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.4196, 0.0078, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.3451, 0.6000, 0.9922, 0.9922, 0.9922, 0.9922, 0.6000, 0.1020, 0.0039, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]),\n",
" tensor(3))"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dset[19111]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# testing digit stacks\n",
"test_stacked_zeros = testing_data[0]\n",
"test_stacked_ones = testing_data[1]\n",
"test_stacked_twos = testing_data[2]\n",
"test_stacked_threes = testing_data[3]\n",
"test_stacked_fours = testing_data[4]\n",
"test_stacked_fives = testing_data[5]\n",
"test_stacked_sixs = testing_data[6]\n",
"test_stacked_sevens = testing_data[7]\n",
"test_stacked_eights = testing_data[8]\n",
"test_stacked_nines = testing_data[9]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# concatenate stacks for testing data\n",
"test_x = torch.cat([test_stacked_zeros, \n",
" test_stacked_ones,\n",
" test_stacked_twos,\n",
" test_stacked_threes,\n",
" test_stacked_fours,\n",
" test_stacked_fives,\n",
" test_stacked_sixs,\n",
" test_stacked_sevens,\n",
" test_stacked_eights,\n",
" test_stacked_nines]).view(-1, 28*28)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([10000, 784])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# testing data tensor shape\n",
"test_x.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# generate test label\n",
"test_y = []\n",
"for i in range(len(testing_set)):\n",
" #print(training_data[i].shape)\n",
" test_y += tensor([i]*testing_data[i].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# convert test label list to tensor\n",
"test_y = tensor(test_y)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# create test dataset\n",
"test_dset = list(zip(test_x, test_y))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# create DataLoader from a Dataset\n",
"training_dl = DataLoader(train_dset, batch_size=256)\n",
"testing_dl = DataLoader(test_dset, batch_size=256)\n",
"dls = DataLoaders(training_dl, testing_dl)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# define a simple neural net, the last layer has 10 neurons \n",
"simple_net = nn.Sequential(\n",
" nn.Linear(28*28,30),\n",
" nn.ReLU(),\n",
" nn.Linear(30,10)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# define Leaner using cross_entropy as loss function \n",
"learn = Learner(\n",
" dls,\n",
" simple_net,\n",
" opt_func=SGD,\n",
" loss_func=F.cross_entropy,\n",
" metrics=accuracy\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch train_loss valid_loss accuracy time \n",
"0 0.365627 6.875360 0.100900 00:00 \n",
"1 0.252940 5.335828 0.111100 00:00 \n",
"2 0.177598 4.368156 0.161700 00:00 \n",
"3 0.144682 4.343118 0.188900 00:00 \n",
"4 0.125580 4.374200 0.213800 00:00 \n",
"5 0.113288 4.457797 0.230300 00:00 \n",
"6 0.119499 4.230380 0.257600 00:00 \n",
"7 0.119469 4.149022 0.280600 00:00 \n",
"8 0.119995 4.177030 0.286000 00:00 \n",
"9 0.121815 4.093648 0.279000 00:00 \n",
"10 0.121843 4.127574 0.263400 00:00 \n",
"11 0.134972 4.078616 0.282200 00:00 \n",
"12 0.135569 3.853341 0.303800 00:00 \n",
"13 0.144106 3.865306 0.305600 00:00 \n",
"14 0.136459 3.759574 0.282800 00:00 \n",
"15 0.139561 3.426282 0.339300 00:00 \n",
"16 0.141163 3.418278 0.354900 00:00 \n",
"17 0.146842 3.610807 0.324800 00:00 \n",
"18 0.156304 3.224627 0.344400 00:00 \n",
"19 0.142119 3.078625 0.356600 00:00 \n",
"20 0.144251 2.727549 0.388000 00:00 \n",
"21 0.146028 2.495355 0.388300 00:00 \n",
"22 0.140970 2.438941 0.411600 00:00 \n",
"23 0.143266 2.555102 0.419900 00:00 \n",
"24 0.137808 2.489123 0.427800 00:00 \n",
"25 0.140992 2.375724 0.444800 00:00 \n",
"26 0.133226 2.413160 0.425600 00:00 \n",
"27 0.142544 2.305254 0.424700 00:00 \n",
"28 0.146498 2.096729 0.458500 00:00 \n",
"29 0.143305 2.256993 0.427700 00:00 \n",
"30 0.131026 2.452002 0.404400 00:00 \n",
"31 0.137463 2.165641 0.445800 00:00 \n",
"32 0.130990 2.031114 0.463000 00:00 \n",
"33 0.121388 1.974665 0.476300 00:00 \n",
"34 0.118677 1.836841 0.511400 00:00 \n",
"35 0.115853 1.815886 0.523700 00:00 \n",
"36 0.115319 1.786843 0.538200 00:00 \n",
"37 0.116943 1.749727 0.543200 00:00 \n",
"38 0.118205 1.765618 0.539800 00:00 \n",
"39 0.113801 1.925551 0.494900 00:00 \n",
"40 0.122149 1.828682 0.512400 00:00 \n",
"41 0.122189 1.692479 0.521100 00:00 \n",
"42 0.122548 1.624075 0.524100 00:00 \n",
"43 0.121055 1.701235 0.505400 00:00 \n",
"44 0.116324 1.951558 0.470300 00:00 \n",
"45 0.111305 2.163355 0.443700 00:00 \n",
"46 0.110991 2.172930 0.444400 00:00 \n",
"47 0.117031 1.862844 0.491300 00:00 \n",
"48 0.119733 1.565353 0.549000 00:00 \n",
"49 0.115772 1.492106 0.562400 00:00 \n",
"50 0.115201 1.397005 0.584100 00:00 \n",
"51 0.114485 1.286747 0.613700 00:00 \n",
"52 0.112115 1.249112 0.630400 00:00 \n",
"53 0.110190 1.220329 0.638300 00:00 \n",
"54 0.109046 1.204940 0.639700 00:00 \n",
"55 0.108289 1.199752 0.636900 00:00 \n",
"56 0.108320 1.188535 0.632400 00:00 \n",
"57 0.108348 1.179231 0.631000 00:00 \n",
"58 0.107350 1.165900 0.634800 00:01 \n",
"59 0.105420 1.137195 0.640800 00:01 \n",
"60 0.106070 1.084395 0.654400 00:00 \n",
"61 0.107760 1.045202 0.666700 00:00 \n",
"62 0.107573 1.019282 0.674500 00:00 \n",
"63 0.107732 0.962185 0.684400 00:00 \n",
"64 0.106167 0.932296 0.688900 00:00 \n",
"65 0.103605 0.918725 0.691400 00:00 \n",
"66 0.101022 0.918862 0.691300 00:00 \n",
"67 0.099333 0.911785 0.694200 00:00 \n",
"68 0.098129 0.901881 0.694600 00:00 \n",
"69 0.097743 0.889860 0.698000 00:00 \n",
"70 0.097616 0.888518 0.699900 00:00 \n",
"71 0.097614 0.889647 0.696900 00:00 \n",
"72 0.097900 0.883701 0.698500 00:00 \n",
"73 0.097128 0.877586 0.701600 00:00 \n",
"74 0.095679 0.880407 0.703600 00:00 \n",
"75 0.094549 0.889061 0.700700 00:00 \n",
"76 0.094396 0.870874 0.702200 00:00 \n",
"77 0.094513 0.864689 0.704800 00:00 \n",
"78 0.094323 0.864622 0.707300 00:00 \n",
"79 0.093474 0.871405 0.707000 00:00 \n",
"80 0.092343 0.883165 0.702100 00:00 \n",
"81 0.091584 0.881556 0.704800 00:00 \n",
"82 0.091246 0.867203 0.709400 00:00 \n",
"83 0.090882 0.845849 0.716500 00:00 \n",
"84 0.090573 0.820750 0.723800 00:00 \n",
"85 0.090370 0.784679 0.735200 00:00 \n",
"86 0.089918 0.749065 0.746900 00:00 \n",
"87 0.089121 0.713022 0.757300 00:00 \n",
"88 0.088152 0.680328 0.769200 00:00 \n",
"89 0.087013 0.655130 0.778600 00:00 \n",
"90 0.085891 0.632035 0.786700 00:00 \n",
"91 0.084873 0.616020 0.793100 00:00 \n",
"92 0.083951 0.602306 0.797200 00:00 \n",
"93 0.082876 0.597030 0.800500 00:00 \n",
"94 0.081964 0.598282 0.801800 00:00 \n",
"95 0.080888 0.597212 0.802100 00:00 \n",
"96 0.079831 0.599451 0.801200 00:00 \n",
"97 0.078637 0.603609 0.799600 00:00 \n",
"98 0.077703 0.601796 0.800600 00:00 \n",
"99 0.076791 0.604042 0.801800 00:00 \n",
"100 0.075937 0.605047 0.800900 00:00 \n",
"101 0.075327 0.602206 0.801400 00:00 \n",
"102 0.074748 0.598237 0.801700 00:00 \n",
"103 0.074309 0.594399 0.803900 00:00 \n",
"104 0.073861 0.587037 0.805300 00:00 \n",
"105 0.073275 0.579766 0.807400 00:00 \n",
"106 0.072929 0.570852 0.809800 00:00 \n",
"107 0.072511 0.562673 0.812000 00:01 \n",
"108 0.072020 0.557062 0.813800 00:00 \n",
"109 0.071432 0.551267 0.814800 00:01 \n",
"110 0.070925 0.546085 0.815500 00:00 \n",
"111 0.070559 0.542208 0.816800 00:00 \n",
"112 0.070266 0.538191 0.818500 00:00 \n",
"113 0.069681 0.535782 0.818900 00:00 \n",
"114 0.069285 0.532737 0.820700 00:00 \n",
"115 0.068869 0.531190 0.821000 00:00 \n",
"116 0.068360 0.530864 0.821300 00:00 \n",
"117 0.067992 0.530015 0.821900 00:00 \n",
"118 0.067498 0.530870 0.821600 00:00 \n",
"119 0.067095 0.531042 0.822100 00:00 \n",
"120 0.066573 0.532354 0.822000 00:00 \n",
"121 0.066161 0.533885 0.821700 00:00 \n",
"122 0.065549 0.534225 0.822000 00:00 \n",
"123 0.065156 0.535010 0.822200 00:00 \n",
"124 0.064738 0.534997 0.822700 00:00 \n",
"125 0.064276 0.535700 0.823500 00:00 \n",
"126 0.063797 0.534820 0.824400 00:00 \n",
"127 0.063417 0.534314 0.824900 00:00 \n",
"128 0.063016 0.531990 0.826200 00:00 \n",
"129 0.062497 0.528877 0.826600 00:00 \n",
"130 0.062045 0.526769 0.827600 00:00 \n",
"131 0.061629 0.525839 0.828000 00:00 \n",
"132 0.061327 0.522811 0.829400 00:00 \n",
"133 0.060760 0.522021 0.829800 00:00 \n",
"134 0.060525 0.519876 0.831000 00:00 \n",
"135 0.060148 0.518509 0.831900 00:00 \n",
"136 0.059658 0.516873 0.832900 00:00 \n",
"137 0.059231 0.513588 0.834100 00:00 \n",
"138 0.058844 0.511182 0.834600 00:00 \n",
"139 0.058475 0.509841 0.835300 00:00 \n",
"140 0.058049 0.508050 0.836200 00:00 \n",
"141 0.057701 0.505620 0.838000 00:00 \n",
"142 0.057263 0.503939 0.837800 00:00 \n",
"143 0.056708 0.503204 0.838300 00:00 \n",
"144 0.056329 0.501115 0.839000 00:00 \n",
"145 0.055936 0.500663 0.839800 00:00 \n",
"146 0.055598 0.500561 0.841100 00:00 \n",
"147 0.055194 0.499634 0.841200 00:00 \n",
"148 0.054850 0.499140 0.841600 00:00 \n",
"149 0.054362 0.499025 0.842300 00:00 \n",
"150 0.054105 0.497738 0.842000 00:00 \n",
"151 0.053649 0.496221 0.843000 00:00 \n",
"152 0.053344 0.495901 0.843000 00:00 \n",
"153 0.052907 0.496236 0.843500 00:00 \n",
"154 0.052744 0.496574 0.843700 00:00 \n",
"155 0.052448 0.494856 0.844400 00:00 \n",
"156 0.052075 0.494341 0.844800 00:00 \n",
"157 0.051684 0.494806 0.845800 00:00 \n",
"158 0.051336 0.494364 0.846100 00:00 \n",
"159 0.050896 0.493417 0.845900 00:00 \n",
"160 0.050592 0.492800 0.846800 00:00 \n",
"161 0.050298 0.493057 0.846400 00:00 \n",
"162 0.049961 0.491330 0.847600 00:00 \n",
"163 0.049633 0.491354 0.847600 00:00 \n",
"164 0.049366 0.490607 0.847400 00:00 \n",
"165 0.048955 0.490634 0.847800 00:00 \n",
"166 0.048699 0.490707 0.848100 00:00 \n",
"167 0.048463 0.489041 0.849900 00:00 \n",
"168 0.048210 0.488337 0.849900 00:00 \n",
"169 0.048028 0.488385 0.850700 00:00 \n",
"170 0.047589 0.486554 0.851300 00:00 \n",
"171 0.047420 0.487244 0.851600 00:00 \n",
"172 0.047112 0.486257 0.851800 00:00 \n",
"173 0.046798 0.485207 0.851800 00:00 \n",
"174 0.046536 0.484240 0.853300 00:00 \n",
"175 0.046296 0.483819 0.854200 00:00 \n",
"176 0.046070 0.483009 0.854800 00:00 \n",
"177 0.045802 0.482812 0.855500 00:00 \n",
"178 0.045516 0.482484 0.855700 00:00 \n",
"179 0.045316 0.481198 0.856100 00:00 \n",
"180 0.045025 0.481421 0.856700 00:00 \n",
"181 0.044647 0.480125 0.857100 00:00 \n",
"182 0.044488 0.480220 0.857400 00:00 \n",
"183 0.044315 0.478880 0.858600 00:00 \n",
"184 0.043943 0.479177 0.859200 00:00 \n",
"185 0.043844 0.479057 0.859700 00:00 \n",
"186 0.043547 0.478110 0.859800 00:00 \n",
"187 0.043341 0.477894 0.860400 00:00 \n",
"188 0.043096 0.475656 0.861200 00:00 \n",
"189 0.042759 0.475483 0.861300 00:00 \n",
"190 0.042562 0.475209 0.861600 00:00 \n",
"191 0.042335 0.474098 0.862200 00:00 \n",
"192 0.042196 0.473494 0.862200 00:00 \n",
"193 0.041902 0.472303 0.862500 00:00 \n",
"194 0.041744 0.472205 0.863100 00:00 \n",
"195 0.041414 0.472380 0.863500 00:00 \n",
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"1332 0.003580 0.377297 0.952200 00:00 \n",
"1333 0.003573 0.377164 0.952100 00:00 \n",
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"1352 0.003484 0.377792 0.952500 00:00 \n",
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"1354 0.003479 0.377772 0.952400 00:00 \n",
"1355 0.003468 0.377785 0.952400 00:00 \n",
"1356 0.003460 0.377667 0.952400 00:00 \n",
"1357 0.003458 0.377854 0.952400 00:00 \n",
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"1362 0.003435 0.378052 0.952500 00:00 \n",
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"1369 0.003404 0.378298 0.952500 00:00 \n",
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"1374 0.003382 0.378328 0.952500 00:00 \n",
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"1376 0.003365 0.378410 0.952600 00:00 \n",
"1377 0.003373 0.378550 0.952500 00:00 \n",
"1378 0.003365 0.378376 0.952600 00:00 \n",
"1379 0.003358 0.378672 0.952500 00:00 \n",
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"1384 0.003334 0.378734 0.952500 00:00 \n",
"1385 0.003332 0.378615 0.952500 00:00 \n",
"1386 0.003328 0.378693 0.952500 00:00 \n",
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"1391 0.003312 0.378982 0.952600 00:01 \n",
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"1396 0.003285 0.379095 0.952600 00:00 \n",
"1397 0.003283 0.379116 0.952500 00:00 \n",
"1398 0.003276 0.379075 0.952400 00:00 \n",
"1399 0.003269 0.379171 0.952600 00:00 \n",
"1400 0.003272 0.379060 0.952600 00:00 \n",
"1401 0.003260 0.379230 0.952500 00:00 \n",
"1402 0.003259 0.379121 0.952600 00:00 \n",
"1403 0.003252 0.379176 0.952600 00:00 \n",
"1404 0.003245 0.379308 0.952600 00:00 \n",
"1405 0.003246 0.379185 0.952600 00:00 \n",
"1406 0.003239 0.379162 0.952700 00:00 \n",
"1407 0.003234 0.379294 0.952600 00:00 \n",
"1408 0.003231 0.379518 0.952600 00:00 \n",
"1409 0.003232 0.379385 0.952700 00:00 \n",
"1410 0.003219 0.379366 0.952700 00:00 \n",
"1411 0.003221 0.379384 0.952700 00:00 \n",
"1412 0.003209 0.379322 0.952700 00:00 \n",
"1413 0.003206 0.379430 0.952800 00:00 \n",
"1414 0.003207 0.379564 0.952700 00:00 \n",
"1415 0.003201 0.379540 0.952800 00:00 \n",
"1416 0.003194 0.379506 0.953000 00:00 \n",
"1417 0.003192 0.379642 0.952800 00:00 \n",
"1418 0.003186 0.379573 0.952600 00:00 \n",
"1419 0.003186 0.379719 0.952900 00:00 \n",
"1420 0.003176 0.379584 0.952700 00:00 \n",
"1421 0.003177 0.379703 0.952900 00:00 \n",
"1422 0.003169 0.379791 0.952700 00:00 \n",
"1423 0.003167 0.379808 0.952800 00:00 \n",
"1424 0.003163 0.379658 0.953100 00:00 \n",
"1425 0.003157 0.379710 0.953000 00:00 \n",
"1426 0.003153 0.379763 0.953000 00:00 \n",
"1427 0.003152 0.379897 0.952900 00:00 \n",
"1428 0.003144 0.379830 0.953100 00:00 \n",
"1429 0.003142 0.379922 0.952900 00:00 \n",
"1430 0.003138 0.380100 0.953100 00:00 \n",
"1431 0.003136 0.379844 0.953200 00:00 \n",
"1432 0.003127 0.379961 0.953200 00:00 \n",
"1433 0.003128 0.380070 0.952900 00:00 \n",
"1434 0.003115 0.379954 0.953200 00:00 \n",
"1435 0.003116 0.380186 0.953000 00:00 \n",
"1436 0.003111 0.379997 0.953000 00:00 \n",
"1437 0.003108 0.380146 0.953200 00:00 \n",
"1438 0.003107 0.380202 0.953300 00:00 \n",
"1439 0.003097 0.380249 0.953200 00:00 \n",
"1440 0.003096 0.380165 0.953200 00:00 \n",
"1441 0.003092 0.380248 0.953300 00:00 \n",
"1442 0.003090 0.380232 0.953100 00:00 \n",
"1443 0.003080 0.380295 0.953200 00:00 \n",
"1444 0.003082 0.380423 0.953200 00:00 \n",
"1445 0.003070 0.380242 0.953200 00:00 \n",
"1446 0.003073 0.380396 0.953200 00:00 \n",
"1447 0.003067 0.380561 0.953100 00:00 \n",
"1448 0.003068 0.380406 0.953100 00:00 \n",
"1449 0.003054 0.380427 0.953100 00:00 \n",
"1450 0.003051 0.380343 0.953200 00:00 \n",
"1451 0.003048 0.380646 0.953200 00:00 \n",
"1452 0.003049 0.380535 0.953200 00:00 \n",
"1453 0.003039 0.380560 0.953200 00:00 \n",
"1454 0.003031 0.380603 0.953200 00:00 \n",
"1455 0.003034 0.380563 0.953100 00:00 \n",
"1456 0.003023 0.380655 0.953200 00:00 \n",
"1457 0.003025 0.380585 0.953200 00:00 \n",
"1458 0.003019 0.380667 0.953200 00:00 \n",
"1459 0.003016 0.380821 0.953200 00:00 \n",
"1460 0.003005 0.380822 0.953200 00:00 \n",
"1461 0.003009 0.380712 0.953200 00:00 \n",
"1462 0.003004 0.380869 0.953200 00:00 \n",
"1463 0.002996 0.380901 0.953200 00:00 \n",
"1464 0.002989 0.380936 0.953200 00:00 \n",
"1465 0.002993 0.380893 0.953200 00:00 \n",
"1466 0.002987 0.381020 0.953200 00:00 \n",
"1467 0.002974 0.380930 0.953200 00:00 \n",
"1468 0.002979 0.380974 0.953200 00:00 \n",
"1469 0.002979 0.381007 0.953200 00:00 \n",
"1470 0.002960 0.380990 0.953200 00:00 \n",
"1471 0.002969 0.381165 0.953200 00:00 \n",
"1472 0.002959 0.381183 0.953200 00:00 \n",
"1473 0.002957 0.381145 0.953200 00:00 \n",
"1474 0.002951 0.381155 0.953200 00:00 \n",
"1475 0.002946 0.381334 0.953200 00:00 \n",
"1476 0.002945 0.381410 0.953200 00:00 \n",
"1477 0.002939 0.381226 0.953200 00:00 \n",
"1478 0.002938 0.381339 0.953200 00:00 \n",
"1479 0.002933 0.381388 0.953100 00:00 \n",
"1480 0.002929 0.381462 0.953200 00:00 \n",
"1481 0.002929 0.381444 0.953200 00:00 \n",
"1482 0.002922 0.381431 0.953200 00:00 \n",
"1483 0.002920 0.381602 0.953200 00:00 \n",
"1484 0.002915 0.381512 0.953200 00:00 \n",
"1485 0.002910 0.381583 0.953200 00:00 \n",
"1486 0.002904 0.381507 0.953200 00:00 \n",
"1487 0.002908 0.381633 0.953200 00:00 \n",
"1488 0.002891 0.381611 0.953200 00:00 \n",
"1489 0.002899 0.381743 0.953200 00:00 \n",
"1490 0.002889 0.381791 0.953200 00:00 \n",
"1491 0.002886 0.381776 0.953200 00:00 \n",
"1492 0.002885 0.381776 0.953200 00:00 \n",
"1493 0.002876 0.381790 0.953200 00:00 \n",
"1494 0.002879 0.381947 0.953200 00:00 \n",
"1495 0.002867 0.381873 0.953300 00:00 \n",
"1496 0.002869 0.381926 0.953200 00:00 \n",
"1497 0.002861 0.381866 0.953200 00:00 \n",
"1498 0.002858 0.382017 0.953200 00:00 \n",
"1499 0.002859 0.382009 0.953200 00:00 \n"
]
}
],
"source": [
"# fit with 1500 epoch and 0.1 learning rate\n",
"learn.fit(1500, lr=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(L(learn.recorder.values).itemgot(2));"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9531999826431274"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.recorder.values[-1][2] #accuracy"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# define a simple neural net with 2 linear layer having ReLU to add non-linearity\n",
"# the output is from LogSoftmax\n",
"simple_net_1 = nn.Sequential(\n",
" nn.Linear(28*28,30),\n",
" nn.ReLU(),\n",
" nn.Linear(30,10),\n",
" nn.LogSoftmax(dim=1)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# define Leaner using cross_entropy as loss function \n",
"learn_1 = Learner(\n",
" dls,\n",
" simple_net_1,\n",
" opt_func=SGD,\n",
" loss_func=F.cross_entropy,\n",
" metrics=accuracy\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch train_loss valid_loss accuracy time \n",
"0 0.390828 6.471811 0.100900 00:00 \n",
"1 0.246668 4.959109 0.111900 00:00 \n",
"2 0.174252 4.263743 0.166900 00:00 \n",
"3 0.144635 4.049888 0.201400 00:00 \n",
"4 0.136244 4.010701 0.231200 00:00 \n",
"5 0.130640 4.235359 0.236800 00:00 \n",
"6 0.119474 4.523220 0.232400 00:00 \n",
"7 0.113107 4.618871 0.246100 00:00 \n",
"8 0.116061 4.334595 0.263700 00:00 \n",
"9 0.125086 3.866522 0.276700 00:00 \n",
"10 0.133110 3.561406 0.288300 00:00 \n",
"11 0.148558 3.258873 0.324500 00:00 \n",
"12 0.144508 3.108094 0.356200 00:00 \n",
"13 0.141899 3.183088 0.319200 00:00 \n",
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"1364 0.003817 0.347501 0.950700 00:00 \n",
"1365 0.003795 0.347535 0.950800 00:00 \n",
"1366 0.003805 0.347592 0.950900 00:00 \n",
"1367 0.003789 0.347717 0.950900 00:00 \n",
"1368 0.003794 0.347639 0.951000 00:00 \n",
"1369 0.003783 0.347678 0.951000 00:00 \n",
"1370 0.003775 0.347770 0.951000 00:00 \n",
"1371 0.003773 0.347770 0.950900 00:00 \n",
"1372 0.003767 0.347808 0.950900 00:00 \n",
"1373 0.003766 0.347817 0.950800 00:00 \n",
"1374 0.003756 0.347802 0.950700 00:00 \n",
"1375 0.003753 0.347956 0.950800 00:00 \n",
"1376 0.003748 0.347888 0.950800 00:00 \n",
"1377 0.003742 0.347935 0.950700 00:01 \n",
"1378 0.003732 0.347976 0.950800 00:00 \n",
"1379 0.003733 0.348105 0.950800 00:00 \n",
"1380 0.003723 0.348116 0.950700 00:00 \n",
"1381 0.003720 0.348152 0.950800 00:00 \n",
"1382 0.003716 0.348140 0.950800 00:00 \n",
"1383 0.003708 0.348266 0.950800 00:00 \n",
"1384 0.003706 0.348251 0.950800 00:00 \n",
"1385 0.003699 0.348248 0.950900 00:00 \n",
"1386 0.003692 0.348299 0.950800 00:00 \n",
"1387 0.003685 0.348297 0.951000 00:00 \n",
"1388 0.003687 0.348382 0.951000 00:00 \n",
"1389 0.003674 0.348415 0.950900 00:00 \n",
"1390 0.003677 0.348468 0.951000 00:00 \n",
"1391 0.003667 0.348442 0.951000 00:00 \n",
"1392 0.003659 0.348492 0.951200 00:01 \n",
"1393 0.003662 0.348349 0.951200 00:00 \n",
"1394 0.003652 0.348590 0.951200 00:00 \n",
"1395 0.003651 0.348537 0.951100 00:00 \n",
"1396 0.003644 0.348627 0.951200 00:00 \n",
"1397 0.003639 0.348627 0.951200 00:00 \n",
"1398 0.003633 0.348670 0.951100 00:00 \n",
"1399 0.003626 0.348775 0.951000 00:00 \n",
"1400 0.003624 0.348729 0.950900 00:00 \n",
"1401 0.003615 0.348845 0.951000 00:00 \n",
"1402 0.003613 0.348784 0.951000 00:00 \n",
"1403 0.003603 0.348881 0.951000 00:00 \n",
"1404 0.003607 0.348911 0.951100 00:00 \n",
"1405 0.003598 0.348904 0.951000 00:00 \n",
"1406 0.003598 0.348981 0.950900 00:00 \n",
"1407 0.003587 0.348936 0.951100 00:01 \n",
"1408 0.003580 0.349035 0.951100 00:00 \n",
"1409 0.003580 0.349050 0.951100 00:00 \n",
"1410 0.003578 0.349099 0.951000 00:00 \n",
"1411 0.003566 0.349146 0.951000 00:00 \n",
"1412 0.003562 0.349257 0.950900 00:00 \n",
"1413 0.003561 0.349164 0.951000 00:00 \n",
"1414 0.003558 0.349290 0.950900 00:00 \n",
"1415 0.003552 0.349391 0.951100 00:00 \n",
"1416 0.003540 0.349286 0.950900 00:00 \n",
"1417 0.003546 0.349352 0.951000 00:00 \n",
"1418 0.003530 0.349417 0.950900 00:00 \n",
"1419 0.003530 0.349441 0.951000 00:00 \n",
"1420 0.003529 0.349578 0.951000 00:00 \n",
"1421 0.003522 0.349510 0.951100 00:00 \n",
"1422 0.003510 0.349386 0.951100 00:01 \n",
"1423 0.003518 0.349473 0.951000 00:00 \n",
"1424 0.003513 0.349587 0.951000 00:00 \n",
"1425 0.003496 0.349656 0.951100 00:00 \n",
"1426 0.003500 0.349657 0.950900 00:00 \n",
"1427 0.003491 0.349684 0.951000 00:00 \n",
"1428 0.003490 0.349748 0.951000 00:00 \n",
"1429 0.003483 0.349765 0.950900 00:00 \n",
"1430 0.003473 0.349785 0.951000 00:00 \n",
"1431 0.003474 0.349918 0.950900 00:00 \n",
"1432 0.003474 0.349914 0.951100 00:00 \n",
"1433 0.003467 0.349948 0.951000 00:00 \n",
"1434 0.003463 0.349973 0.951100 00:00 \n",
"1435 0.003452 0.349959 0.951200 00:00 \n",
"1436 0.003452 0.350071 0.951300 00:00 \n",
"1437 0.003450 0.350076 0.951300 00:01 \n",
"1438 0.003437 0.350093 0.951300 00:00 \n",
"1439 0.003443 0.350123 0.951500 00:00 \n",
"1440 0.003427 0.350166 0.951500 00:00 \n",
"1441 0.003429 0.350210 0.951500 00:00 \n",
"1442 0.003422 0.350314 0.951400 00:00 \n",
"1443 0.003417 0.350315 0.951600 00:00 \n",
"1444 0.003417 0.350260 0.951600 00:00 \n",
"1445 0.003406 0.350273 0.951600 00:00 \n",
"1446 0.003403 0.350424 0.951600 00:00 \n",
"1447 0.003402 0.350461 0.951600 00:00 \n",
"1448 0.003395 0.350450 0.951700 00:00 \n",
"1449 0.003393 0.350484 0.951700 00:00 \n",
"1450 0.003388 0.350616 0.951600 00:00 \n",
"1451 0.003384 0.350555 0.951700 00:01 \n",
"1452 0.003372 0.350592 0.951600 00:00 \n",
"1453 0.003375 0.350704 0.951600 00:00 \n",
"1454 0.003369 0.350724 0.951700 00:00 \n",
"1455 0.003362 0.350705 0.951700 00:00 \n",
"1456 0.003362 0.350806 0.951700 00:00 \n",
"1457 0.003348 0.350720 0.951600 00:00 \n",
"1458 0.003345 0.350853 0.951700 00:00 \n",
"1459 0.003345 0.350840 0.951700 00:00 \n",
"1460 0.003342 0.350973 0.951700 00:00 \n",
"1461 0.003333 0.350831 0.951800 00:00 \n",
"1462 0.003329 0.350947 0.951800 00:00 \n",
"1463 0.003328 0.350994 0.951800 00:00 \n",
"1464 0.003317 0.351121 0.951700 00:00 \n",
"1465 0.003319 0.351080 0.951700 00:00 \n",
"1466 0.003311 0.351174 0.951600 00:00 \n",
"1467 0.003317 0.351159 0.951700 00:00 \n",
"1468 0.003306 0.351206 0.951700 00:00 \n",
"1469 0.003297 0.351168 0.951900 00:00 \n",
"1470 0.003291 0.351321 0.951700 00:00 \n",
"1471 0.003288 0.351268 0.951700 00:00 \n",
"1472 0.003284 0.351431 0.951700 00:00 \n",
"1473 0.003283 0.351372 0.952000 00:00 \n",
"1474 0.003277 0.351353 0.951900 00:00 \n",
"1475 0.003270 0.351396 0.951900 00:00 \n",
"1476 0.003263 0.351401 0.952000 00:00 \n",
"1477 0.003260 0.351594 0.951900 00:00 \n",
"1478 0.003260 0.351534 0.952000 00:00 \n",
"1479 0.003257 0.351605 0.952100 00:00 \n",
"1480 0.003254 0.351587 0.952100 00:00 \n",
"1481 0.003244 0.351709 0.952100 00:00 \n",
"1482 0.003239 0.351751 0.952000 00:01 \n",
"1483 0.003237 0.351744 0.952000 00:00 \n",
"1484 0.003231 0.351732 0.952300 00:00 \n",
"1485 0.003227 0.351836 0.952100 00:00 \n",
"1486 0.003223 0.351896 0.952000 00:00 \n",
"1487 0.003214 0.351917 0.952200 00:00 \n",
"1488 0.003222 0.351971 0.952300 00:00 \n",
"1489 0.003208 0.351890 0.952400 00:00 \n",
"1490 0.003204 0.352016 0.952500 00:00 \n",
"1491 0.003204 0.352066 0.952300 00:00 \n",
"1492 0.003194 0.352134 0.952400 00:00 \n",
"1493 0.003195 0.352150 0.952400 00:00 \n",
"1494 0.003193 0.352268 0.952400 00:00 \n",
"1495 0.003184 0.352211 0.952500 00:00 \n",
"1496 0.003178 0.352288 0.952500 00:01 \n",
"1497 0.003175 0.352356 0.952500 00:01 \n",
"1498 0.003171 0.352342 0.952500 00:00 \n",
"1499 0.003170 0.352355 0.952500 00:00 \n"
]
}
],
"source": [
"learn_1.fit(1500, lr=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(L(learn_1.recorder.values).itemgot(2));"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9524999856948853"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn_1.recorder.values[-1][2] #accuracy"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"block = DataBlock(\n",
" blocks=(ImageBlock, CategoryBlock),\n",
" get_items=get_image_files,\n",
" splitter=RandomSplitter(valid_pct=0.2, seed=42),\n",
" get_y=parent_label,\n",
" batch_tfms=aug_transforms(mult=2., do_flip=False))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ec2-user/SageMaker/.env/fastai/lib/python3.6/site-packages/torch/_tensor.py:1023: UserWarning: torch.solve is deprecated in favor of torch.linalg.solveand will be removed in a future PyTorch release.\n",
"torch.linalg.solve has its arguments reversed and does not return the LU factorization.\n",
"To get the LU factorization see torch.lu, which can be used with torch.lu_solve or torch.lu_unpack.\n",
"X = torch.solve(B, A).solution\n",
"should be replaced with\n",
"X = torch.linalg.solve(A, B) (Triggered internally at /pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:760.)\n",
" ret = func(*args, **kwargs)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x216 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds = block.dataloaders(path/\"training\")\n",
"ds.train.show_batch(max_n=4, nrows=1)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /home/ec2-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a14e1e987264d8b852fd93d1f8aa42c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0.00/44.7M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ec2-user/SageMaker/.env/fastai/lib/python3.6/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n",
" return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
]
}
],
"source": [
"learn_3 = cnn_learner(ds, resnet18, loss_func=F.cross_entropy, metrics=accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch train_loss valid_loss accuracy time \n",
"0 1.193218 0.756094 0.752333 01:01 \n",
"epoch train_loss valid_loss accuracy time \n",
"0 0.357096 0.135447 0.957500 01:12 \n",
"1 0.206699 0.076614 0.976250 01:13 \n",
"2 0.142501 0.052034 0.984000 01:12 \n",
"3 0.131077 0.042051 0.986417 01:12 \n",
"4 0.098921 0.040675 0.986833 01:12 \n"
]
}
],
"source": [
"learn_3.fine_tune(5, cbs=[ShowGraphCallback()])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"test_dl = learn_3.dls.test_dl(get_image_files(path/\"testing\"), with_labels=True)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"█\r"
]
}
],
"source": [
"loss, accuracy = learn_3.validate(dl=test_dl)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.036956559866666794, 0.9876000285148621)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss, accuracy"
]
}
],
"metadata": {
"jupytext": {
"split_at_heading": true
},
"kernelspec": {
"display_name": "fastai",
"language": "python",
"name": "fastai"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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