160 lines
3.4 KiB
Plaintext
160 lines
3.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4a54ab90",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch"
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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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"id": "6533ad3a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 3,
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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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"torch.cuda.is_available()"
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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": 9,
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"id": "d44a0818",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[6.7262e-44, 0.0000e+00, 6.8664e-44],\n",
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" [0.0000e+00, 3.4130e+20, 3.0694e-41],\n",
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" [3.8088e+20, 3.0694e-41, 1.4013e-45],\n",
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" [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
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" [1.3452e-43, 0.0000e+00, 1.3452e-43]])"
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]
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},
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"execution_count": 9,
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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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"x = torch.empty(5, 3)\n",
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"x"
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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": 16,
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"id": "637d9ebe",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[0., 0., 0., 0., 0.],\n",
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" [0., 0., 0., 0., 0.],\n",
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" [0., 0., 0., 0., 0.]])"
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]
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},
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"execution_count": 16,
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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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"x = torch.zeros((3, 5), dtype=torch.float)\n",
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"x"
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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": 28,
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"id": "dd60207e",
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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([[0.1210, 0.4432, 0.5018, 0.2111, 0.1256],\n",
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" [0.6894, 0.3928, 0.0216, 0.1406, 0.5416],\n",
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" [0.0044, 0.4856, 0.2907, 0.9889, 0.2060]])\n",
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"tensor([[0.7810, 0.4015, 0.9711, 0.1654, 0.3593],\n",
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" [0.5972, 0.1060, 0.6192, 0.6601, 0.9575],\n",
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" [0.3084, 0.4609, 0.6921, 0.3311, 0.6630]])\n",
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"tensor([[0.9020, 0.8447, 1.4729, 0.3764, 0.4850],\n",
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" [1.2866, 0.4987, 0.6409, 0.8006, 1.4991],\n",
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" [0.3128, 0.9465, 0.9828, 1.3200, 0.8690]])\n"
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]
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}
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],
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"source": [
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"x.shape\n",
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"x = torch.rand_like(x, dtype=torch.float)\n",
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"y = torch.rand_like(x, dtype=torch.float)\n",
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"print(x)\n",
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"print(y)\n",
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"z = x+y\n",
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"print(z)"
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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": 35,
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"id": "0d2ed6cd",
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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([[0.2814, 0.6475, 0.3659, 0.4057],\n",
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" [0.9773, 0.5319, 0.7534, 0.8681],\n",
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" [0.4856, 0.3504, 0.0362, 0.6139]], requires_grad=True)\n"
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]
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}
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],
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"source": [
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"x = torch.rand((3, 4), requires_grad=True)\n",
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"print(x)"
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]
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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": "3.10.8"
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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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