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sethjuarezsethjuarez
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updated computational graph visualization
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‎data.ipynb

+202-1
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{"cells":[{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":"import torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.dataset import Dataset\nfrom torchvision import datasets, transforms"},{"cell_type":"markdown","metadata":{},"outputs":[],"source":"# Dataset"},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":"class SquareDataset(Dataset):\n def __init__(self, size):\n self.size = size\n self.X = torch.randint(255, (size, 9), dtype=torch.float)\n\n real_w = torch.tensor([[1,1,1,0,0,0,0,0,0],\n [0,0,0,1,1,1,0,0,0],\n [0,0,0,0,0,0,1,1,1]], \n dtype=torch.float)\n\n y = torch.argmax(self.X.mm(real_w.t()), 1)\n \n self.Y = torch.zeros(size, 3, dtype=torch.float) \\\n .scatter_(1, y.view(-1, 1), 1)\n\n def __getitem__(self, index):\n return (self.X[index], self.Y[index])\n\n def __len__(self):\n return self.size"},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":"squares = SquareDataset(256)\nprint(squares[34])\nprint(squares[254])\nprint(squares[25])"},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":"dataloader = DataLoader(squares, batch_size=5)\n\nfor batch, (X, Y) in enumerate(dataloader):\n print(X, '\\n\\n', Y)\n break"},{"cell_type":"markdown","metadata":{},"outputs":[],"source":"# Digits\nTransforms!"},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":"digits = datasets.MNIST('data', train=True, download=True,\n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Lambda(lambda x: x.view(28*28))\n ]),\n target_transform=transforms.Compose([\n transforms.Lambda(lambda y: \n torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))\n ])\n )"},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":"dataloader = DataLoader(digits, batch_size=10, shuffle=True)\n\nfor batch, (X, Y) in enumerate(dataloader):\n print(X, '\\n\\n', Y)\n break"}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"name":"python","codemirror_mode":{"name":"ipython","version":3}},"orig_nbformat":2,"file_extension":".py","mimetype":"text/x-python","name":"python","npconvert_exporter":"python","pygments_lexer":"ipython3","version":3}}
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{
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"cells": [
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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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"outputs": [],
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"source": [
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"import torch\n",
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"from torch.utils.data import DataLoader\n",
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"from torch.utils.data.dataset import Dataset\n",
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"from torchvision import datasets, transforms"
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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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"# Dataset"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"class SquareDataset(Dataset):\n",
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" def __init__(self, size):\n",
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" self.size = size\n",
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" self.X = torch.randint(255, (size, 9), dtype=torch.float)\n",
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"\n",
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" real_w = torch.tensor([[1,1,1,0,0,0,0,0,0],\n",
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" [0,0,0,1,1,1,0,0,0],\n",
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" [0,0,0,0,0,0,1,1,1]], \n",
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" dtype=torch.float)\n",
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"\n",
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" y = torch.argmax(self.X.mm(real_w.t()), 1)\n",
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" \n",
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" self.Y = torch.zeros(size, 3, dtype=torch.float) \\\n",
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" .scatter_(1, y.view(-1, 1), 1)\n",
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"\n",
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" def __getitem__(self, index):\n",
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" return (self.X[index], self.Y[index])\n",
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"\n",
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" def __len__(self):\n",
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" return self.size"
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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": 3,
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"metadata": {},
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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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"(tensor([ 54., 182., 47., 142., 200., 197., 220., 215., 33.]), tensor([0., 1., 0.]))\n",
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"(tensor([198., 171., 26., 140., 28., 9., 205., 48., 113.]), tensor([1., 0., 0.]))\n",
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"(tensor([ 64., 7., 167., 4., 9., 160., 169., 113., 214.]), tensor([0., 0., 1.]))\n"
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]
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}
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],
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"source": [
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"squares = SquareDataset(256)\n",
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"print(squares[34])\n",
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"print(squares[254])\n",
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"print(squares[25])"
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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": 4,
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"metadata": {},
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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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"tensor([[152., 127., 155., 219., 81., 140., 112., 77., 102.],\n",
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" [ 77., 58., 228., 164., 229., 155., 111., 223., 141.],\n",
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" [106., 250., 87., 62., 105., 254., 0., 210., 136.],\n",
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" [190., 108., 134., 204., 145., 251., 146., 171., 99.],\n",
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" [ 88., 36., 190., 108., 122., 4., 231., 22., 70.]]) \n",
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"\n",
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" tensor([[0., 1., 0.],\n",
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" [0., 1., 0.],\n",
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" [1., 0., 0.],\n",
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" [0., 1., 0.],\n",
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" [0., 0., 1.]])\n"
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]
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}
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],
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"source": [
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"dataloader = DataLoader(squares, batch_size=5)\n",
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"\n",
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"for batch, (X, Y) in enumerate(dataloader):\n",
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" print(X, '\\n\\n', Y)\n",
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" break"
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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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"# Digits\n",
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"Transforms!"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"digits = datasets.MNIST('data', train=True, download=True,\n",
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" transform=transforms.Compose([\n",
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" transforms.ToTensor(),\n",
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" transforms.Lambda(lambda x: x.view(28*28))\n",
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" ]),\n",
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" target_transform=transforms.Compose([\n",
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" transforms.Lambda(lambda y: \n",
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" torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))\n",
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" ])\n",
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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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"execution_count": 6,
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"metadata": {},
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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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"tensor([[0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" ...,\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.]]) \n",
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"\n",
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" tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
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" [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
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" [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
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" [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
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" [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
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" [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
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" [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
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" [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
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" [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
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" [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.]])\n"
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]
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}
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],
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"source": [
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"dataloader = DataLoader(digits, batch_size=10, shuffle=True)\n",
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"\n",
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"for batch, (X, Y) in enumerate(dataloader):\n",
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" print(X, '\\n\\n', Y)\n",
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" break"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"file_extension": ".py",
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.10"
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},
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"mimetype": "text/x-python",
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"name": "python",
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"npconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": 3
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎digits.ipynb

+430-1
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