Athena/Untitled.ipynb
2023-05-16 14:23:02 +00:00

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1.7 KiB
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e1e8183d-1c75-43d5-84f2-11ed4a043e95",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"5488"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"56*98"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e179529c-1e9e-484c-810d-cc5283b301e0",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.4938, 0.6272, 0.7424, 0.8706, 0.0350, 0.4712, 0.2920, 0.3668, 0.1011],\n",
" [0.2043, 0.3699, 0.6485, 0.3753, 0.7622, 0.6652, 0.9545, 0.9230, 0.1376],\n",
" [0.0024, 0.9363, 0.4466, 0.0182, 0.6990, 0.2950, 0.9408, 0.4459, 0.4602],\n",
" [0.9974, 0.5928, 0.1695, 0.4071, 0.9829, 0.9592, 0.5724, 0.5596, 0.1615],\n",
" [0.7799, 0.5518, 0.4942, 0.0703, 0.3000, 0.8426, 0.4958, 0.7940, 0.2984]])\n"
]
}
],
"source": [
"import torch\n",
"A = torch.rand(5,9)\n",
"print(A)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c4ff8b0e-5adc-482c-a224-425c3794ec80",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}