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MHenderson/pytorch_1.ipynb

Last active November 21, 2019 15:15
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PyTorch tutorial
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pytorch_1.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1.8293e+25, 4.5808e-41, 2.7649e-37],\n",
" [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
" [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
" [0.0000e+00, 0.0000e+00, 0.0000e+00],\n",
" [0.0000e+00, 0.0000e+00, 0.0000e+00]])\n"
]
}
],
"source": [
"x = torch.empty(5, 3)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.0999, 0.0514, 0.5746],\n",
" [0.2492, 0.3858, 0.2630],\n",
" [0.2320, 0.5569, 0.0581],\n",
" [0.5044, 0.3515, 0.9185],\n",
" [0.0800, 0.5181, 0.6103]])\n"
]
}
],
"source": [
"x = torch.rand(5, 3)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0, 0, 0],\n",
" [0, 0, 0],\n",
" [0, 0, 0],\n",
" [0, 0, 0],\n",
" [0, 0, 0]])\n"
]
}
],
"source": [
"x = torch.zeros(5, 3, dtype=torch.long)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([5.5000, 3.0000])\n"
]
}
],
"source": [
"x = torch.tensor([5.5, 3])\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1., 1., 1.],\n",
" [1., 1., 1.],\n",
" [1., 1., 1.],\n",
" [1., 1., 1.],\n",
" [1., 1., 1.]], dtype=torch.float64)\n"
]
}
],
"source": [
"x = x.new_ones(5, 3, dtype=torch.double)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.4409, -1.0526, -0.1251],\n",
" [-3.1898, -0.9379, -0.1052],\n",
" [ 0.1024, -0.3951, -1.3711],\n",
" [ 0.0613, 0.0635, -0.7447],\n",
" [-0.1321, -0.1839, -1.8460]])\n"
]
}
],
"source": [
"x = torch.randn_like(x, dtype=torch.float)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([5, 3])\n"
]
}
],
"source": [
"print(x.size())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.5931, -0.9253, 0.3852],\n",
" [-2.8199, 0.0564, -0.0479],\n",
" [ 1.0666, -0.3886, -0.8085],\n",
" [ 0.1084, 1.0452, 0.0571],\n",
" [-0.0160, 0.5582, -1.0145]])\n"
]
}
],
"source": [
"y = torch.rand(5, 3)\n",
"print(x + y)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.5931, -0.9253, 0.3852],\n",
" [-2.8199, 0.0564, -0.0479],\n",
" [ 1.0666, -0.3886, -0.8085],\n",
" [ 0.1084, 1.0452, 0.0571],\n",
" [-0.0160, 0.5582, -1.0145]])\n"
]
}
],
"source": [
"print(torch.add(x, y))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0.5931, -0.9253, 0.3852],\n",
" [-2.8199, 0.0564, -0.0479],\n",
" [ 1.0666, -0.3886, -0.8085],\n",
" [ 0.1084, 1.0452, 0.0571],\n",
" [-0.0160, 0.5582, -1.0145]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = torch.empty(5, 3)\n",
"torch.add(x, y, out=result)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0.5931, -0.9253, 0.3852],\n",
" [-2.8199, 0.0564, -0.0479],\n",
" [ 1.0666, -0.3886, -0.8085],\n",
" [ 0.1084, 1.0452, 0.0571],\n",
" [-0.0160, 0.5582, -1.0145]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.add_(x)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([-1.0526, -0.9379, -0.3951, 0.0635, -0.1839])\n"
]
}
],
"source": [
"print(x[:, 1])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"x = torch.randn(4, 4)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"y = x.view(16)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"z = x.view(-1, 8)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])\n"
]
}
],
"source": [
"print(x.size(), y.size(), z.size())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = torch.randn(1)\n",
"print(x)\n",
"print(x.item())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"a = torch.ones(5)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([1., 1., 1., 1., 1.])\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 1. 1. 1. 1.]\n"
]
}
],
"source": [
"b = a.numpy()\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([2., 2., 2., 2., 2.])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.add_(1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([2., 2., 2., 2., 2.])\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2. 2. 2. 2. 2.]\n"
]
}
],
"source": [
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2. 2. 2. 2. 2.]\n",
"tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\n"
]
}
],
"source": [
"import numpy as np\n",
"a = np.ones(5)\n",
"b = torch.from_numpy(a)\n",
"np.add(a, 1, out=a)\n",
"print(a)\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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pytorch_2.ipynb
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