283 lines
12 KiB
Plaintext
283 lines
12 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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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torchvision\n",
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"from torchvision import models"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /home/eddie/.cache/torch/checkpoints/vgg16-397923af.pth\n",
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"100.0%\n"
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]
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}
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],
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"source": [
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"vgg16 = models.vgg16(pretrained=True)"
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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": 4,
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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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"VGG(\n",
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" (features): Sequential(\n",
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" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU(inplace=True)\n",
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" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (3): ReLU(inplace=True)\n",
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" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (6): ReLU(inplace=True)\n",
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" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (8): ReLU(inplace=True)\n",
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" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (11): ReLU(inplace=True)\n",
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" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (13): ReLU(inplace=True)\n",
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" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (15): ReLU(inplace=True)\n",
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" (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (18): ReLU(inplace=True)\n",
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" (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (20): ReLU(inplace=True)\n",
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" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (22): ReLU(inplace=True)\n",
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" (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (25): ReLU(inplace=True)\n",
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" (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (27): ReLU(inplace=True)\n",
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" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (29): ReLU(inplace=True)\n",
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" (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
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" (classifier): Sequential(\n",
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" (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
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" (1): ReLU(inplace=True)\n",
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" (2): Dropout(p=0.5, inplace=False)\n",
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" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
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" (4): ReLU(inplace=True)\n",
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" (5): Dropout(p=0.5, inplace=False)\n",
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" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
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" )\n",
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")\n"
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]
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}
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],
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"source": [
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"print(vgg16)"
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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": 8,
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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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"odict_items([('0', Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('1', ReLU(inplace=True)), ('2', Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('3', ReLU(inplace=True)), ('4', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('5', Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('6', ReLU(inplace=True)), ('7', Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('8', ReLU(inplace=True)), ('9', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('10', Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('11', ReLU(inplace=True)), ('12', Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('13', ReLU(inplace=True)), ('14', Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('15', ReLU(inplace=True)), ('16', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('17', Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('18', ReLU(inplace=True)), ('19', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('20', ReLU(inplace=True)), ('21', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('22', ReLU(inplace=True)), ('23', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('24', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('25', ReLU(inplace=True)), ('26', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('27', ReLU(inplace=True)), ('28', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('29', ReLU(inplace=True)), ('30', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))])\n"
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]
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}
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],
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"source": [
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"print(vgg16.features._modules.items())"
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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": 11,
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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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"Layer: 0\n",
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"Name: 0\n",
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"Module: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 2\n",
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"Name: 2\n",
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"Module: Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 5\n",
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"Name: 5\n",
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"Module: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 7\n",
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"Name: 7\n",
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"Module: Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 10\n",
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"Name: 10\n",
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"Module: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 12\n",
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"Name: 12\n",
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"Module: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 14\n",
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"Name: 14\n",
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"Module: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 17\n",
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"Name: 17\n",
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"Module: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 19\n",
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"Name: 19\n",
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"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 21\n",
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"Name: 21\n",
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"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 24\n",
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"Name: 24\n",
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"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 26\n",
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"Name: 26\n",
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"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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"Layer: 28\n",
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"Name: 28\n",
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"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"
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]
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}
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],
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"source": [
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"for layer, (name, module) in enumerate(vgg16.features._modules.items()):\n",
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" if isinstance(module, torch.nn.modules.conv.Conv2d):\n",
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" print(\"Layer: {}\".format(layer))\n",
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" print(\"Name: {}\".format(name))\n",
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" print(\"Module: {}\".format(module))"
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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": 12,
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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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"<generator object Module.parameters at 0x7f99d03001d0>"
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]
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},
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"execution_count": 12,
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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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"vgg16.features.parameters()"
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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": 14,
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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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"<bound method VGG.forward of VGG(\n",
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" (features): Sequential(\n",
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" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU(inplace=True)\n",
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" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (3): ReLU(inplace=True)\n",
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" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (6): ReLU(inplace=True)\n",
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" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (8): ReLU(inplace=True)\n",
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" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (11): ReLU(inplace=True)\n",
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" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (13): ReLU(inplace=True)\n",
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" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (15): ReLU(inplace=True)\n",
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" (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (18): ReLU(inplace=True)\n",
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" (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (20): ReLU(inplace=True)\n",
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" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (22): ReLU(inplace=True)\n",
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" (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (25): ReLU(inplace=True)\n",
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" (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (27): ReLU(inplace=True)\n",
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" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (29): ReLU(inplace=True)\n",
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" (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
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" (classifier): Sequential(\n",
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" (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
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" (1): ReLU(inplace=True)\n",
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" (2): Dropout(p=0.5, inplace=False)\n",
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" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
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" (4): ReLU(inplace=True)\n",
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" (5): Dropout(p=0.5, inplace=False)\n",
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" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
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" )\n",
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")>"
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]
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},
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"execution_count": 14,
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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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"vgg16.forward"
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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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"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",
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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": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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