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‎Pytorch/(Pytorch) Sentiment Analysis with Transformer BERT.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Twitter Sentiment BERT Classification"
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"# Google-play App Review Sentiment Analysis with BERT "
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]
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},
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{

‎Pytorch/(Pytorch) VISUALIZING MODELS, DATA, AND TRAINING WITH TENSORBOARD.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# VISUALIZING MODELS, DATA, AND TRAINING WITH TENSORBOARD"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# From \n",
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"# https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Steps in Tensorboard \n",
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"### 1. Set up TensorBoard.\n",
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"### 2. Write to TensorBoard.\n",
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"### 3. Inspect a model architecture using TensorBoard.\n",
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"### 4. Use TensorBoard to create interactive versions of the visualizations we created in last tutorial, with less code"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Use Boilerplate code as in the CIFAR-10:"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"import torch\n",
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"import torchvision\n",
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"import torchvision.transforms as transforms\n",
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"\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"import torch.optim as optim\n",
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"\n",
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"# transforms\n",
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"transform = transforms.Compose(\n",
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" [transforms.ToTensor(),\n",
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" transforms.Normalize((0.5,), (0.5,))])\n",
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"\n",
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"# datasets\n",
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"trainset = torchvision.datasets.FashionMNIST('./data',\n",
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" download=True,\n",
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" train=True,\n",
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" transform=transform)\n",
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"testset = torchvision.datasets.FashionMNIST('./data',\n",
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" download=True,\n",
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" train=False,\n",
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" transform=transform)\n",
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"\n",
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"# dataloaders\n",
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"trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
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" shuffle=True, num_workers=2)\n",
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"\n",
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"\n",
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"testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
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" shuffle=False, num_workers=2)\n",
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"\n",
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"# constant for classes\n",
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"classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
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" 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')\n",
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"\n",
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"# helper function to show an image\n",
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"# (used in the `plot_classes_preds` function below)\n",
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"def matplotlib_imshow(img, one_channel=False):\n",
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" if one_channel:\n",
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" img = img.mean(dim=0)\n",
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" img = img / 2 + 0.5 # unnormalize\n",
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" npimg = img.numpy()\n",
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" if one_channel:\n",
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" plt.imshow(npimg, cmap=\"Greys\")\n",
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" else:\n",
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" plt.imshow(np.transpose(npimg, (1, 2, 0)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Model \n",
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"# Minor modifications (images are now one channel instead of three, and 28x28 instead of 32x32:)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"class Net(nn.Module):\n",
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" def __init__(self):\n",
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" super(Net, self).__init__()\n",
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" self.conv1 = nn.Conv2d(1, 6, 5)\n",
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" self.pool = nn.MaxPool2d(2, 2)\n",
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" self.conv2 = nn.Conv2d(6, 16, 5)\n",
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" self.fc1 = nn.Linear(16 * 4 * 4, 120)\n",
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" self.fc2 = nn.Linear(120, 84)\n",
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" self.fc3 = nn.Linear(84, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.pool(F.relu(self.conv1(x)))\n",
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" x = self.pool(F.relu(self.conv2(x)))\n",
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" x = x.view(-1, 16 * 4 * 4)\n",
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" x = F.relu(self.fc1(x))\n",
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" x = F.relu(self.fc2(x))\n",
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" x = self.fc3(x)\n",
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" return x\n",
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"\n",
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"\n",
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"net = Net()"
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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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"source": [
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"# Set up criterion and optimizer\n",
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"criterion = nn.CrossEntropyLoss()\n",
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"optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Set up TensorBoard"
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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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"source": [
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"from torch.utils.tensorboard import SummaryWriter\n",
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"\n",
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"# default `log_dir` is \"runs\" - we'll be more specific here\n",
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"# it creates a runs/fashion_mnist_experiment_1 folder.\n",
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"writer = SummaryWriter('runs/fashion_mnist_experiment_1')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Writing to TensorBoard"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
177+
"text/plain": [
178+
"<Figure size 432x288 with 1 Axes>"
179+
]
180+
},
181+
"metadata": {
182+
"needs_background": "light"
183+
},
184+
"output_type": "display_data"
185+
}
186+
],
187+
"source": [
188+
"# get some random training images\n",
189+
"dataiter = iter(trainloader)\n",
190+
"images, labels = dataiter.next()\n",
191+
"\n",
192+
"# create grid of images\n",
193+
"img_grid = torchvision.utils.make_grid(images)\n",
194+
"\n",
195+
"# show images\n",
196+
"matplotlib_imshow(img_grid, one_channel=True)\n",
197+
"\n",
198+
"# write to tensorboard\n",
199+
"writer.add_image('four_fashion_mnist_images', img_grid)"
200+
]
201+
},
202+
{
203+
"cell_type": "code",
204+
"execution_count": 6,
205+
"metadata": {},
206+
"outputs": [],
207+
"source": [
208+
"%load_ext tensorboard"
209+
]
210+
},
211+
{
212+
"cell_type": "code",
213+
"execution_count": 9,
214+
"metadata": {},
215+
"outputs": [
216+
{
217+
"data": {
218+
"text/plain": [
219+
"Reusing TensorBoard on port 6006 (pid 7256), started 0:01:11 ago. (Use '!kill 7256' to kill it.)"
220+
]
221+
},
222+
"metadata": {},
223+
"output_type": "display_data"
224+
},
225+
{
226+
"data": {
227+
"text/html": [
228+
"\n",
229+
" <iframe id=\"tensorboard-frame-ee9465fe0576b007\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
230+
" </iframe>\n",
231+
" <script>\n",
232+
" (function() {\n",
233+
" const frame = document.getElementById(\"tensorboard-frame-ee9465fe0576b007\");\n",
234+
" const url = new URL(\"/\", window.location);\n",
235+
" url.port = 6006;\n",
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" frame.src = url;\n",
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" })();\n",
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" </script>\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"%tensorboard --logdir runs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## run tensorboard --logdir=runs\n",
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"## Then go to https://localhost:6006"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Inspect the model using TensorBoard"
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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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"source": [
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"writer.add_graph(net, images)\n",
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"writer.close()"
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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": "3.7.6"
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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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