Created
July 27, 2021 14:03
-
-
Save jimexist/a3d361c7e8e23796841730772e24b363 to your computer and use it in GitHub Desktop.
Torch Vision AlexNet Model
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"id": "d61a6c41", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import torch\n", | |
"import torchvision" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"id": "abe6863d", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"('1.9.0', '0.10.0')" | |
] | |
}, | |
"execution_count": 2, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"torch.__version__, torchvision.__version__" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"id": "1b55aad8", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"AlexNet(\n", | |
" (features): Sequential(\n", | |
" (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n", | |
" (1): ReLU(inplace=True)\n", | |
" (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", | |
" (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", | |
" (4): ReLU(inplace=True)\n", | |
" (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", | |
" (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", | |
" (7): ReLU(inplace=True)\n", | |
" (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", | |
" (9): ReLU(inplace=True)\n", | |
" (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", | |
" (11): ReLU(inplace=True)\n", | |
" (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n", | |
" )\n", | |
" (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n", | |
" (classifier): Sequential(\n", | |
" (0): Dropout(p=0.5, inplace=False)\n", | |
" (1): Linear(in_features=9216, out_features=4096, bias=True)\n", | |
" (2): ReLU(inplace=True)\n", | |
" (3): Dropout(p=0.5, inplace=False)\n", | |
" (4): Linear(in_features=4096, out_features=4096, bias=True)\n", | |
" (5): ReLU(inplace=True)\n", | |
" (6): Linear(in_features=4096, out_features=1000, bias=True)\n", | |
" )\n", | |
")" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"torchvision.models.AlexNet()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "c7437d1d", | |
"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": "3.8.11" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 5 | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment