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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/jangsoopark/3637c7526afed776d9f19d87503c8af8/torch-mnist-pull-3498.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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},
"id": "Dm8lVjqXPOPD",
"outputId": "3dc40a67-9887-4086-dea8-b5a1faed6ee3"
},
"source": [
"!git clone https://github.com/pytorch/vision\r\n",
"!cp vision/torchvision/datasets/utils.py /usr/local/lib/python3.7/dist-packages/torchvision/datasets/"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'vision'...\n",
"remote: Enumerating objects: 118, done.\u001b[K\n",
"remote: Counting objects: 100% (118/118), done.\u001b[K\n",
"remote: Compressing objects: 100% (110/110), done.\u001b[K\n",
"remote: Total 17022 (delta 72), reused 27 (delta 3), pack-reused 16904\u001b[K\n",
"Receiving objects: 100% (17022/17022), 16.47 MiB | 21.48 MiB/s, done.\n",
"Resolving deltas: 100% (12254/12254), done.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rwOcOJetPRsE"
},
"source": [
"import torchvision #using torchvision, we can eaily download MNIST dataset\r\n",
"import torchvision.transforms as transforms #to transform MNIST \"images\" to \"tensor\""
],
"execution_count": 2,
"outputs": []
},
{
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"source": [
"train_data = torchvision.datasets.MNIST(root='./datasets',\r\n",
" train=True,\r\n",
" transform=transforms.ToTensor(),\r\n",
" download=True)\r\n",
"test_data = torchvision.datasets.MNIST(root='./datasets',\r\n",
" train=False,\r\n",
" transform=transforms.ToTensor())"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"
],
"name": "stdout"
},
{
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},
"metadata": {
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},
{
"output_type": "stream",
"text": [
"\n",
"Extracting ./datasets/MNIST/raw/train-images-idx3-ubyte.gz to ./datasets/MNIST/raw\n",
"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"
],
"name": "stdout"
},
{
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"text": [
"\n",
"Extracting ./datasets/MNIST/raw/train-labels-idx1-ubyte.gz to ./datasets/MNIST/raw\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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"metadata": {
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{
"output_type": "stream",
"text": [
"\n",
"Extracting ./datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to ./datasets/MNIST/raw\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ea1bc0d3ecd94d1185e7e9bb32252c47",
"version_minor": 0,
"version_major": 2
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},
"metadata": {
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},
{
"output_type": "stream",
"text": [
"\n",
"Extracting ./datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./datasets/MNIST/raw\n",
"Processing...\n",
"Done!\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Kw_t1ehIPZfU"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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