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@SmBito
SmBito committed Sep 25, 2019
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210 changes: 208 additions & 2 deletions 02.ipynb
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Expand Up @@ -36,7 +36,20 @@
"colab_type": "code",
"id": "bi4w0XmGFSfc"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
"Processing...\n",
"Done!\n"
]
}
],
"source": [
"train = datasets.MNIST(\"\", train = True, download = True,\n",
" transform = transforms.Compose([transforms.ToTensor()]))\n",
Expand All @@ -61,12 +74,205 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "MUUp_xefGwlL"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[tensor([[[[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]]],\n",
"\n",
"\n",
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]]],\n",
"\n",
"\n",
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]]],\n",
"\n",
"\n",
" ...,\n",
"\n",
"\n",
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]]],\n",
"\n",
"\n",
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]]],\n",
"\n",
"\n",
" [[[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]]]]), tensor([9, 1, 4, 9, 9, 3, 9, 5, 8, 6])]\n"
]
}
],
"source": [
"for data in trainset:\n",
" print(data)\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(9)\n"
]
}
],
"source": [
"x, y = data[0][0], data[1][0]\n",
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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NaYfY3r/ywYls7y/pU+q9qaiXS9o9resCSfd2sZd36ZVpvKtNM64uv3ddn/48Ijr+I+lsjX0i/5KkP+tGD1X6OkbS05WfNd3uTdKdGjus26GxI6KFkg6StELSC5Xb6T3U2z9KWiXpGY0Fa6BLvf2axv40fEbSysrP2d1+7wp9deR943RZIAnOoAOSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJP4PK79NUHwNbBkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(data[0][0].view(28,28))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 28, 28])\n"
]
}
],
"source": [
"print(data[0][0].shape)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{0: 5923, 1: 6742, 2: 5958, 3: 6131, 4: 5842, 5: 5421, 6: 5918, 7: 6265, 8: 5851, 9: 5949}\n"
]
}
],
"source": [
"total = 0\n",
"counter_dict = {0:0, 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0}\n",
"\n",
"for data in trainset:\n",
" Xs, ys = data\n",
" for y in ys:\n",
" counter_dict[int(y)] += 1\n",
" total+=1\n",
"\n",
"print(counter_dict)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: 9.871666666666666\n",
"1: 11.236666666666666\n",
"2: 9.93\n",
"3: 10.218333333333334\n",
"4: 9.736666666666666\n",
"5: 9.035\n",
"6: 9.863333333333333\n",
"7: 10.441666666666666\n",
"8: 9.751666666666667\n",
"9: 9.915000000000001\n"
]
}
],
"source": [
"for i in counter_dict:\n",
" print(f\"{i}: {counter_dict[i]/total*100}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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