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