3568 lines
296 KiB
Plaintext
3568 lines
296 KiB
Plaintext
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{
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"cells": [
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-05T02:10:33.113447Z",
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"start_time": "2021-04-05T02:10:31.845159Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"import torch\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 torchvision \n",
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"from torchvision import transforms\n",
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"\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"\n",
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"from tqdm.autonotebook import tqdm\n",
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"\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.pyplot import imshow\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"import time\n",
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"\n",
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"from idlmam import train_simple_network, Flatten, weight_reset"
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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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"ExecuteTime": {
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"end_time": "2021-04-05T02:10:33.119112Z",
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"start_time": "2021-04-05T02:10:33.115084Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from IPython.display import set_matplotlib_formats\n",
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"set_matplotlib_formats('png', 'pdf')"
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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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"ExecuteTime": {
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"end_time": "2021-04-05T02:10:33.126087Z",
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"start_time": "2021-04-05T02:10:33.120528Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"torch.backends.cudnn.deterministic=True\n",
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"from idlmam import set_seed, moveTo\n",
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"set_seed(42)"
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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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"ExecuteTime": {
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"end_time": "2021-04-05T02:10:33.139614Z",
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"start_time": "2021-04-05T02:10:33.127481Z"
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},
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"tags": [
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"remove_cell"
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]
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},
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"outputs": [],
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"source": [
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"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
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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": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-05T02:10:33.176455Z",
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"start_time": "2021-04-05T02:10:33.148272Z"
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}
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},
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"outputs": [],
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"source": [
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"mnist_data_train = torchvision.datasets.MNIST(\"./data\", train=True, download=True, transform=transforms.ToTensor())\n",
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"mnist_data_test = torchvision.datasets.MNIST(\"./data\", train=False, download=True, transform=transforms.ToTensor())\n",
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" \n",
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"mnist_train_loader = DataLoader(mnist_data_train, batch_size=64, shuffle=True)\n",
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"mnist_test_loader = DataLoader(mnist_data_test, batch_size=64)\n",
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"\n",
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"#How many values are in the input? We use this to help determine the size of subsequent layers\n",
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"D = 28*28 #28 * 28 images \n",
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"#Hidden layer size\n",
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"n = 256 \n",
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"#How many channels are in the input?\n",
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"C = 1\n",
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"#How many classes are there?\n",
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"classes = 10\n",
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"\n",
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"#Create our regular model \n",
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"model_regular = nn.Sequential(\n",
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" Flatten(), \n",
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" nn.Linear(D, n), \n",
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" nn.Tanh(),\n",
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" nn.Linear(n, n), \n",
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" nn.Tanh(),\n",
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" nn.Linear(n, n), \n",
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" nn.Tanh(),\n",
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" nn.Linear(n, classes),\n",
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")"
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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": 7,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-05T02:11:50.740875Z",
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"start_time": "2021-04-05T02:10:33.178585Z"
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},
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"tags": [
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"remove_output"
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"text/plain": [
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||
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"HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=938.0), HTML(value='')))"
|
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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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"data": {
|
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|
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"model_id": "",
|
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"version_major": 2,
|
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"version_minor": 0
|
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},
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"text/plain": [
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"HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=157.0), HTML(value='')))"
|
||
|
|
]
|
||
|
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},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
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},
|
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|
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{
|
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|
|
"name": "stdout",
|
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|
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"output_type": "stream",
|
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|
|
"text": [
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|
|
"\n"
|
||
|
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]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"shared_results = train_simple_network(model_shared, loss_func, mnist_train_loader, test_loader=mnist_test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
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"execution_count": 10,
|
||
|
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|
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|
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"ExecuteTime": {
|
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"end_time": "2021-04-05T02:13:06.703906Z",
|
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|
|
"start_time": "2021-04-05T02:13:06.452654Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
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"<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
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"output_type": "execute_result"
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},
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#Now we can plot the results, and compare them\n",
|
||
|
|
"sns.lineplot(x='epoch', y='test Accuracy', data=regular_results, label='Normal')\n",
|
||
|
|
"sns.lineplot(x='epoch', y='test Accuracy', data=shared_results, label='Shared')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:13:07.424029Z",
|
||
|
|
"start_time": "2021-04-05T02:13:06.705150Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"zip_file_url = \"https://download.pytorch.org/tutorial/data.zip\"\n",
|
||
|
|
"\n",
|
||
|
|
"import requests, zipfile, io\n",
|
||
|
|
"r = requests.get(zip_file_url)\n",
|
||
|
|
"z = zipfile.ZipFile(io.BytesIO(r.content))\n",
|
||
|
|
"z.extractall()\n",
|
||
|
|
"\n",
|
||
|
|
"#Zip file is organized as data/names/[LANG].txt , where [LANG] is a specific language"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:13:07.578702Z",
|
||
|
|
"start_time": "2021-04-05T02:13:07.425705Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Arabic : 2000\n",
|
||
|
|
"Chinese : 268\n",
|
||
|
|
"Czech : 519\n",
|
||
|
|
"Dutch : 297\n",
|
||
|
|
"English : 3668\n",
|
||
|
|
"French : 277\n",
|
||
|
|
"German : 724\n",
|
||
|
|
"Greek : 203\n",
|
||
|
|
"Irish : 232\n",
|
||
|
|
"Italian : 709\n",
|
||
|
|
"Japanese : 991\n",
|
||
|
|
"Korean : 94\n",
|
||
|
|
"Polish : 139\n",
|
||
|
|
"Portuguese : 74\n",
|
||
|
|
"Russian : 9408\n",
|
||
|
|
"Scottish : 100\n",
|
||
|
|
"Spanish : 298\n",
|
||
|
|
"Vietnamese : 73\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"namge_language_data = {}\n",
|
||
|
|
"\n",
|
||
|
|
"#We will use some code to remove UNICODE tokens to make life easy for us processing wise\n",
|
||
|
|
"#e.g., convert something like \"Ślusàrski\" to Slusarski\n",
|
||
|
|
"import unicodedata\n",
|
||
|
|
"import string\n",
|
||
|
|
"\n",
|
||
|
|
"all_letters = string.ascii_letters + \" .,;'\"\n",
|
||
|
|
"n_letters = len(all_letters)\n",
|
||
|
|
"alphabet = {}\n",
|
||
|
|
"for i in range(n_letters):\n",
|
||
|
|
" alphabet[all_letters[i]] = i\n",
|
||
|
|
" \n",
|
||
|
|
"# Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427\n",
|
||
|
|
"def unicodeToAscii(s):\n",
|
||
|
|
" return ''.join(\n",
|
||
|
|
" c for c in unicodedata.normalize('NFD', s)\n",
|
||
|
|
" if unicodedata.category(c) != 'Mn'\n",
|
||
|
|
" and c in all_letters\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
"#Loop through every language, open the zip file entry, and read all the lines from the text file. \n",
|
||
|
|
"for zip_path in z.namelist():\n",
|
||
|
|
" if \"data/names/\" in zip_path and zip_path.endswith(\".txt\"):\n",
|
||
|
|
" lang = zip_path[len(\"data/names/\"):-len(\".txt\")]\n",
|
||
|
|
" with z.open(zip_path) as myfile:\n",
|
||
|
|
" lang_names = [unicodeToAscii(line).lower() for line in str(myfile.read(), encoding='utf-8').strip().split(\"\\n\")]\n",
|
||
|
|
" namge_language_data[lang] = lang_names\n",
|
||
|
|
" print(lang, \": \", len(lang_names)) #Print out the name of each language too. "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:13:07.587570Z",
|
||
|
|
"start_time": "2021-04-05T02:13:07.580165Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class LanguageNameDataset(Dataset):\n",
|
||
|
|
" \n",
|
||
|
|
" def __init__(self, lang_name_dict, vocabulary):\n",
|
||
|
|
" self.label_names = [x for x in lang_name_dict.keys()]\n",
|
||
|
|
" self.data = []\n",
|
||
|
|
" self.labels = []\n",
|
||
|
|
" self.vocabulary = vocabulary\n",
|
||
|
|
" for y, language in enumerate(self.label_names):\n",
|
||
|
|
" for sample in lang_name_dict[language]:\n",
|
||
|
|
" self.data.append(sample)\n",
|
||
|
|
" self.labels.append(y)\n",
|
||
|
|
" \n",
|
||
|
|
" def __len__(self):\n",
|
||
|
|
" return len(self.data)\n",
|
||
|
|
" \n",
|
||
|
|
" def string2InputVec(self, input_string):\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" This method will convert any input string into a vector of long values, according to the vocabulary used by this object. \n",
|
||
|
|
" input_string: the string to convert to a tensor\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" T = len(input_string) #How many characters long is the string?\n",
|
||
|
|
" \n",
|
||
|
|
" #Create a new tensor to store the result in\n",
|
||
|
|
" name_vec = torch.zeros((T), dtype=torch.long)\n",
|
||
|
|
" #iterate through the string and place the appropriate values into the tensor\n",
|
||
|
|
" for pos, character in enumerate(input_string):\n",
|
||
|
|
" name_vec[pos] = self.vocabulary[character]\n",
|
||
|
|
" \n",
|
||
|
|
" return name_vec\n",
|
||
|
|
" \n",
|
||
|
|
" def __getitem__(self, idx):\n",
|
||
|
|
" name = self.data[idx]\n",
|
||
|
|
" label = self.labels[idx]\n",
|
||
|
|
" \n",
|
||
|
|
" #Conver the correct class label into a tensor for PyTorch\n",
|
||
|
|
" label_vec = torch.tensor([label], dtype=torch.long)\n",
|
||
|
|
" \n",
|
||
|
|
" return self.string2InputVec(name), label"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:13:07.597263Z",
|
||
|
|
"start_time": "2021-04-05T02:13:07.589150Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dataset = LanguageNameDataset(namge_language_data, alphabet)\n",
|
||
|
|
"\n",
|
||
|
|
"train_data, test_data = torch.utils.data.random_split(dataset, (len(dataset)-300, 300))\n",
|
||
|
|
"train_loader = DataLoader(train_data, batch_size=1, shuffle=True)\n",
|
||
|
|
"test_loader = DataLoader(test_data, batch_size=1, shuffle=False)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:13:07.606766Z",
|
||
|
|
"start_time": "2021-04-05T02:13:07.598682Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"torch.Size([5]) torch.Size([5, 2])\n",
|
||
|
|
"tensor([[ 0.7626, 0.1343],\n",
|
||
|
|
" [ 1.5189, 0.6567],\n",
|
||
|
|
" [ 1.5189, 0.6567],\n",
|
||
|
|
" [ 0.7626, 0.1343],\n",
|
||
|
|
" [-0.5718, 0.2879]])\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"with torch.no_grad():\n",
|
||
|
|
" input_sequence = torch.tensor([0, 1, 1, 0, 2], dtype=torch.long)\n",
|
||
|
|
" embd = nn.Embedding(3, 2)\n",
|
||
|
|
" x_seq = embd(input_sequence)\n",
|
||
|
|
" print(input_sequence.shape, x_seq.shape)\n",
|
||
|
|
" print(x_seq)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:13:07.613422Z",
|
||
|
|
"start_time": "2021-04-05T02:13:07.608156Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class LastTimeStep(nn.Module):\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" A class for extracting the hidden activations of the last time step following \n",
|
||
|
|
" the output of a PyTorch RNN module. \n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" def __init__(self, rnn_layers=1, bidirectional=False):\n",
|
||
|
|
" super(LastTimeStep, self).__init__()\n",
|
||
|
|
" self.rnn_layers = rnn_layers\n",
|
||
|
|
" if bidirectional:\n",
|
||
|
|
" self.num_driections = 2\n",
|
||
|
|
" else:\n",
|
||
|
|
" self.num_driections = 1 \n",
|
||
|
|
" \n",
|
||
|
|
" def forward(self, input):\n",
|
||
|
|
" #Result is either a tupe (out, h_t)\n",
|
||
|
|
" #or a tuple (out, (h_t, c_t))\n",
|
||
|
|
" rnn_output = input[0]\n",
|
||
|
|
" last_step = input[1] #this will be h_t\n",
|
||
|
|
" if(type(last_step) == tuple):#unless it's a tuple, \n",
|
||
|
|
" last_step = last_step[0]#then h_t is the first item in the tuple\n",
|
||
|
|
" batch_size = last_step.shape[1] #per docs, shape is: '(num_layers * num_directions, batch, hidden_size)'\n",
|
||
|
|
" #reshaping so that everything is separate \n",
|
||
|
|
" last_step = last_step.view(self.rnn_layers, self.num_driections, batch_size, -1)\n",
|
||
|
|
" #We want the last layer's results\n",
|
||
|
|
" last_step = last_step[self.rnn_layers-1] \n",
|
||
|
|
" #Re order so batch comes first\n",
|
||
|
|
" last_step = last_step.permute(1, 0, 2)\n",
|
||
|
|
" #Finally, flatten the last two dimensions into one\n",
|
||
|
|
" return last_step.reshape(batch_size, -1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:13:07.620042Z",
|
||
|
|
"start_time": "2021-04-05T02:13:07.614864Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"D = 64\n",
|
||
|
|
"vocab_size = len(all_letters)\n",
|
||
|
|
"hidden_nodes = 256\n",
|
||
|
|
"classes = len(dataset.label_names)\n",
|
||
|
|
"\n",
|
||
|
|
"first_rnn = nn.Sequential(\n",
|
||
|
|
" nn.Embedding(vocab_size, D), #(B, T) -> (B, T, D)\n",
|
||
|
|
" nn.RNN(D, hidden_nodes, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
|
||
|
|
" #the tanh activation is built into the RNN object, so we don't need to do it here\n",
|
||
|
|
" LastTimeStep(), #We need to take the RNN output and reduce it to one item, (B, D)\n",
|
||
|
|
" nn.Linear(hidden_nodes, classes), #(B, D) -> (B, classes)\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:17:46.189455Z",
|
||
|
|
"start_time": "2021-04-05T02:13:07.621526Z"
|
||
|
|
},
|
||
|
|
"tags": [
|
||
|
|
"remove_output"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
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|
|
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|
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|
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|
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|
||
|
|
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|
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|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=5.0), HTML(value='')))"
|
||
|
|
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|
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|
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|
|
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|
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|
|
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|
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|
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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},
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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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"sns.lineplot(x='epoch', y='test Accuracy', data=batch_one_train, label='RNN')"
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]
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},
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{
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"metadata": {
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||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:17:46.509455Z",
|
||
|
|
"start_time": "2021-04-05T02:17:46.444214Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Arabic : 0.002683540151338093 %\n",
|
||
|
|
"Chinese : 0.2679025521501899 %\n",
|
||
|
|
"Czech : 10.59301644563675 %\n",
|
||
|
|
"Dutch : 7.299012690782547 %\n",
|
||
|
|
"English : 36.81915104389191 %\n",
|
||
|
|
"French : 0.5335223395377398 %\n",
|
||
|
|
"German : 37.42799460887909 %\n",
|
||
|
|
"Greek : 0.018611310224514455 %\n",
|
||
|
|
"Irish : 0.7783998735249043 %\n",
|
||
|
|
"Italian : 1.1141937226057053 %\n",
|
||
|
|
"Japanese : 0.00488687728648074 %\n",
|
||
|
|
"Korean : 0.421459274366498 %\n",
|
||
|
|
"Polish : 1.1676722206175327 %\n",
|
||
|
|
"Portuguese : 0.08807195699773729 %\n",
|
||
|
|
"Russian : 1.2793921865522861 %\n",
|
||
|
|
"Scottish : 1.6346706077456474 %\n",
|
||
|
|
"Spanish : 0.14639737782999873 %\n",
|
||
|
|
"Vietnamese : 0.40296311490237713 %\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pred_rnn = first_rnn.to(\"cpu\").eval()\n",
|
||
|
|
"with torch.no_grad():\n",
|
||
|
|
" preds = F.softmax(pred_rnn(dataset.string2InputVec(\"frank\").reshape(1,-1)), dim=-1)\n",
|
||
|
|
" for class_id in range(len(dataset.label_names)):\n",
|
||
|
|
" print(dataset.label_names[class_id], \":\", preds[0,class_id].item()*100 , \"%\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:17:46.516173Z",
|
||
|
|
"start_time": "2021-04-05T02:17:46.511040Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def pad_and_pack(batch):\n",
|
||
|
|
" #1, 2, & 3: organize the batch input lengths, inputs, and outputs as seperate lists\n",
|
||
|
|
" input_tensors = []\n",
|
||
|
|
" labels = []\n",
|
||
|
|
" lengths = []\n",
|
||
|
|
" for x, y in batch:\n",
|
||
|
|
" input_tensors.append(x)\n",
|
||
|
|
" labels.append(y)\n",
|
||
|
|
" lengths.append(x.shape[0]) #Assume shape is (T, *)\n",
|
||
|
|
" #4: create the padded version of the input\n",
|
||
|
|
" x_padded = torch.nn.utils.rnn.pad_sequence(input_tensors, batch_first=False)\n",
|
||
|
|
" #5: create the packed version from the padded & lengths\n",
|
||
|
|
" x_packed = torch.nn.utils.rnn.pack_padded_sequence(x_padded, lengths, batch_first=False, enforce_sorted=False)\n",
|
||
|
|
" #Convert the lengths into a tensor\n",
|
||
|
|
" y_batched = torch.as_tensor(labels, dtype=torch.long)\n",
|
||
|
|
" #6: return a tuple of the packed inputs and their labels\n",
|
||
|
|
" return x_packed, y_batched\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:17:46.521723Z",
|
||
|
|
"start_time": "2021-04-05T02:17:46.517329Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class EmbeddingPackable(nn.Module):\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" The embedding layer in PyTorch does not support Packed Sequence objects. \n",
|
||
|
|
" This wrapper class will fix that. If a normal input comes in, it will \n",
|
||
|
|
" use the regular Embedding layer. Otherwise, it will work on the packed \n",
|
||
|
|
" sequence to return a new Packed sequence of the appropriate result. \n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" def __init__(self, embd_layer):\n",
|
||
|
|
" super(EmbeddingPackable, self).__init__()\n",
|
||
|
|
" self.embd_layer = embd_layer \n",
|
||
|
|
" \n",
|
||
|
|
" def forward(self, input):\n",
|
||
|
|
" if type(input) == torch.nn.utils.rnn.PackedSequence:\n",
|
||
|
|
" # We need to unpack the input, \n",
|
||
|
|
" sequences, lengths = torch.nn.utils.rnn.pad_packed_sequence(input.cpu(), batch_first=True)\n",
|
||
|
|
" #Embed it\n",
|
||
|
|
" sequences = self.embd_layer(sequences.to(input.data.device))\n",
|
||
|
|
" #And pack it into a new sequence\n",
|
||
|
|
" return torch.nn.utils.rnn.pack_padded_sequence(sequences, lengths.cpu(), \n",
|
||
|
|
" batch_first=True, enforce_sorted=False)\n",
|
||
|
|
" else:#apply to normal data\n",
|
||
|
|
" return self.embd_layer(input)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:17:46.527559Z",
|
||
|
|
"start_time": "2021-04-05T02:17:46.522964Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"B = 16\n",
|
||
|
|
"train_loader = DataLoader(train_data, batch_size=B, shuffle=True, collate_fn=pad_and_pack)\n",
|
||
|
|
"test_loader = DataLoader(test_data, batch_size=B, shuffle=False, collate_fn=pad_and_pack)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:17:46.536853Z",
|
||
|
|
"start_time": "2021-04-05T02:17:46.530407Z"
|
||
|
|
},
|
||
|
|
"tags": [
|
||
|
|
"remove_output"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Sequential(\n",
|
||
|
|
" (0): EmbeddingPackable(\n",
|
||
|
|
" (embd_layer): Embedding(57, 64)\n",
|
||
|
|
" )\n",
|
||
|
|
" (1): RNN(64, 256, batch_first=True)\n",
|
||
|
|
" (2): LastTimeStep()\n",
|
||
|
|
" (3): Linear(in_features=256, out_features=18, bias=True)\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"rnn_packed = nn.Sequential(\n",
|
||
|
|
" EmbeddingPackable(nn.Embedding(vocab_size, D)), #(B, T) -> (B, T, D)\n",
|
||
|
|
" nn.RNN(D, hidden_nodes, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
|
||
|
|
" LastTimeStep(), #We need to take the RNN output and reduce it to one item, (B, D)\n",
|
||
|
|
" nn.Linear(hidden_nodes, classes), #(B, D) -> (B, classes)\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"rnn_packed.to(device)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:19:39.317005Z",
|
||
|
|
"start_time": "2021-04-05T02:17:46.538194Z"
|
||
|
|
},
|
||
|
|
"tags": [
|
||
|
|
"remove_output"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"outputs": [
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|
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|
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"HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=19.0), HTML(value='')))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"packed_train = train_simple_network(rnn_packed, loss_func, train_loader, test_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:19:39.582038Z",
|
||
|
|
"start_time": "2021-04-05T02:19:39.318290Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1BhZ2VzIDIgMCBSIC9UeXBlIC9DYXRhbG9nID4+CmVuZG9iago4IDAgb2JqCjw8IC9FeHRHU3RhdGUgNCAwIFIgL0ZvbnQgMyAwIFIgL1BhdHRlcm4gNSAwIFIKL1Byb2NTZXQgWyAvUERGIC9UZXh0IC9JbWFnZUIgL0ltYWdlQyAvSW1hZ2VJIF0gL1NoYWRpbmcgNiAwIFIKL1hPYmplY3QgNyAwIFIgPj4KZW5kb2JqCjEwIDAgb2JqCjw8IC9Bbm5vdHMgWyBdIC9Db250ZW50cyA5IDAgUgovR3JvdXAgPDwgL0NTIC9EZXZpY2VSR0IgL1MgL1RyYW5zcGFyZW5jeSAvVHlwZSAvR3JvdXAgPj4KL01lZGlhQm94IFsgMCAwIDM5Mi4xMzc1IDI2Mi4xODM3NSBdIC9QYXJlbnQgMiAwIFIgL1Jlc291cmNlcyA4IDAgUgovVHlwZSAvUGFnZSA+PgplbmRvYmoKOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDExIDAgUiA+PgpzdHJlYW0KeJyVV0tvGzcQvu+v4LE9hJ4Zcvgo0IONtkZzCBrHQM+Goth1bTeNnRr99/1mtdKSejY2BOx+GvKb94zY3Q9n5+xunx25e3xeHbtLd/bT8p8/Fsurywu3eB7eEL54HEIVzyErXh7mF0l4KPb0YILt690wPA24Gwcuce3tMCitDoXsNdoDbi3R1w57mDFRPEy3rc+2GBg+QX9Z6X8LEtjgC6wwSiBDUh9UU5GWswGjp4l0uICur8PFtTv7hR2Tu/40aPaRxEQZwiK+qrv+OHxHnr531/fu5+thpBuYFJJBpbY8LXqcqIJICbKZCjdE4nWLKOJKhSNSR9Sgx4kg42MtoqS1Y9IdkwrurFWkc12LnmDKdipRzbGUhihvmySiEMyUOt+16HEi4egjR0GUckvEO1ESBF5rrNw5r0VPMCH1YhJGmFKuLdVOnAJCn6LkFLpkb9ATVDV6pVqVkvZUO4EKCH6qlLhjmsHjRAHfq+ZcM7zdua8JVK8aiQ8UWNRVL7nYgx1Yfv5rcdertq5Zq6OQOAg0i8mnHjvgAVyRuZhkwIkqmimWMNWf6n4muK2mUqU0TDN2iqmy5yykISStE1Oi/UzQHBGCuDZUDXiKi6V4syhCt7BuK+mAWZyRzIipcEs2gyfJUvKCrEU9EstElg9YJoTQpxw4N2QNeIpMKPgM06IERulPZAcsEyQqQymOLdkMniSLbNkooaSQ4kRWaG/mEgbEG8ZcQMfI44BhVK7QmMGPg405O/+yfH5x54vF1y83i3/XF/3tdoaXlQ4qBoUSo/uydL+7JyfurWOvNoE8C6MnJbGRib4x/eXBPK+lyDgYrranbTOdrNlSRNTgmgIz0UkRa8boVY1cK5yDwRFqqGotAsIF89cKijmgJwaqpfclS0XVCVJhy8cfhvfum01ks6sWQnGyml2ksRLXROWoXRHXSUbEOrtgFpyUo3RmMfIu5grZzioWFHVVTtpbxTAB9UfB4HkqdqXCiXElKSzHeeDE0XqDTaxYatIAecNLqaPTakBRl1ylx4WqV0EiISYFMYn4N95mdCFF8Yh5a+pbqmpKAu9wNWdZ5hmOVqhFQ8xb8hmND2UE23u8VJ8kJIk93kyXLkeCsE8JlNAfjlVwZeMNCDM2G4qWU7BLUyjj+oWGm5EZNncIeibKav4JqfoMZ3Dp8Sl5LC9os4D1wd+z+O3b53DT7j74uH8fhOz/XCcbyfmCw7eenYfVPvnWllZ8XkeTphUWa4an1TFUxViaAXnTIvBUtslIJYQ1ZsgkU9CvrA+0WDWZxTAjKJG1Sg1YPfovrnUtAdCV6KzHBlq0+m7Qh3GTycgtzg1sIG0ztWBubt2gK90fOmhjYsuzcsSO/xa2xV+st/ixuSCJTvRQd7iHDkg30bJRmn2ptuXlLWiMNY05OwV5+snQD5qyUTfAqtVScPXu3Q/u4uZlcfcjr+fDoa7ojnfFRlPJHp7jcZ9otJ3hb9ZYUMe82c9M699u/lwsP7pfnz5/fVlr/n74D+df2voKZW5kc3RyZWFtCmVuZG9iagoxMSAwIG9iagoxMTM5CmVuZG9iagoxNiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDg4ID4+CnN0cmVhbQp4nDWMuw3AMAhEe6a4Efg4gPeJUpH92xBbLrh70hPnOcDIPg9H6MQtZEPhpnhJOaE+UTRabzq2SHO/vGQzFxX9M9x9he3mgGQ0SeQh0eVy5Vkpej6X2ht+CmVuZHN0cmVhbQplbmRvYmoKMTcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNTkgPj4Kc3RyZWFtCnicPVJJcsMwDLv7FXwCd0nvSacn5//XAvTUlxBjigAIpneLytn4aStZfuTHLjtLIrd8B22T+4qqB6RugswlxyVXiFnK50qQWLSUmVifqQ7KzzWoVfjCT8xMTIckEUvIST2KsH5eB/egfr2k81tk/KNjg9JbkkwFnRrulaOU2LBUnxrkHjTdlFafmXZlByyNN2SlJnX69dPjB0swpS8S0UGIxJ/kcocsCykH8Xau3kB4V7sg1VMrDztEmo+R3lIFqzkzAUtG0w4jYG3WmCBVArxFawi0FuLyhU4rIj45N3QwTVWehciwUuFYe89oDRe6hrzScHqScf/5H7wr3tfvH9rzZmQKZW5kc3RyZWFtCmVuZG9iagoxOCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDQ5ID4+CnN0cmVhbQp4nDOyNFUwULC0ABKGluYK5kaWCimGXEA+iJXLBRPLAbMMgDRYaQ5MRQ5XGgClRAzkCmVuZHN0cmVhbQplbmRvYmoKMTkgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA3NSA+PgpzdHJlYW0KeJw1jbERwDAIA3umYATLARP2yaXC+7cJ+GikFxzI5ObBnrJ0sE3nB/TnpE3zQlGQqjDQdGZ9cWjkDlYOF15oSq+K6M9B7weZ6heaCmVuZHN0cmVhbQplbmRvYmoKMjAgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNjUgPj4Kc3RyZWFtCnicRY87EgMhDEN7TqEjgH/AeTaTir1/G8s7SRosjCU/ois69srDY2PKxmu0sSfCFu5SOg2nqYyviqdnXaDLYTJTb1zNXGCqsMhuTrH6GHyh8uzmhK9VnhjCl0wJDTCVO7mH9fpRnJZ8JLsLguqUjcrCMEfS90BMTZunhYH8jy95akFQmeaNa5aVR2sVUzRnmCpbC4L1gaA6pfoD0/9Mp70/3PQ9gAplbmRzdHJlYW0KZW5kb2JqCjIxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjI3ID4+CnN0cmVhbQp4nEWQS44DIRBD95zCR6D+cJ6OsurcfzsuOtFssCUo1zO5AxN78chMlG68ZLg7zBWf4Rkwc/hKmGzETOhOXCOUrhThVJ8IjsvevOmgiXtEzqOeBVnVzg1qAWeS5oLtgi7njBU3zsmtRuXN9KPXEL5pdx/XeYf2SOPew1S+zjnVzruKCGkLWdW0vpBsFMkOaz8qTdvOyxCx4GwaVugc3gi7V3cnSxh+v/IwJRM/D936UXxdN6PrFGcnVyZrz3noSelf9cqjD8VxKegXse3MJPdfp1OSqVN7Z+9p/ae4x/sPkG5WOQplbmRzdHJlYW0KZW5kb2JqCjIyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzA0ID4+CnN0cmVhbQp4nD2SO5LDMAxDe52CF8iM+JPk82Qnlff+7T4yyVaASYkAKC91mbKmPCBpJgn/0eHhYjvld9iezczAtUQvE8spz6Erx
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='epoch', y='test Accuracy', data=batch_one_train, label='RNN: Batch=1')\n",
|
||
|
|
"sns.lineplot(x='epoch', y='test Accuracy', data=packed_train, label='RNN:Pakced Input')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:19:39.832368Z",
|
||
|
|
"start_time": "2021-04-05T02:19:39.583652Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='total time', ylabel='test Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/pdf": "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
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='total time', y='test Accuracy', data=batch_one_train, label='RNN: Batch=1')\n",
|
||
|
|
"sns.lineplot(x='total time', y='test Accuracy', data=packed_train, label='RNN:Pakced Input')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:19:39.901151Z",
|
||
|
|
"start_time": "2021-04-05T02:19:39.833986Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Arabic : 0.586722744628787 %\n",
|
||
|
|
"Chinese : 0.5682710558176041 %\n",
|
||
|
|
"Czech : 15.79725593328476 %\n",
|
||
|
|
"Dutch : 5.215919017791748 %\n",
|
||
|
|
"English : 42.07158088684082 %\n",
|
||
|
|
"French : 1.7968742176890373 %\n",
|
||
|
|
"German : 13.949412107467651 %\n",
|
||
|
|
"Greek : 0.40299338288605213 %\n",
|
||
|
|
"Irish : 2.425672672688961 %\n",
|
||
|
|
"Italian : 5.216174945235252 %\n",
|
||
|
|
"Japanese : 0.3031977219507098 %\n",
|
||
|
|
"Korean : 0.7202120032161474 %\n",
|
||
|
|
"Polish : 2.772565931081772 %\n",
|
||
|
|
"Portuguese : 0.9149040095508099 %\n",
|
||
|
|
"Russian : 4.370814561843872 %\n",
|
||
|
|
"Scottish : 1.0111995041370392 %\n",
|
||
|
|
"Spanish : 1.2703102082014084 %\n",
|
||
|
|
"Vietnamese : 0.6059217266738415 %\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pred_rnn = rnn_packed.to(\"cpu\").eval()\n",
|
||
|
|
"\n",
|
||
|
|
"with torch.no_grad():\n",
|
||
|
|
" preds = F.softmax(pred_rnn(dataset.string2InputVec(\"frank\").reshape(1,-1)), dim=-1)\n",
|
||
|
|
" for class_id in range(len(dataset.label_names)):\n",
|
||
|
|
" print(dataset.label_names[class_id], \":\", preds[0,class_id].item()*100 , \"%\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-05T02:22:10.628335Z",
|
||
|
|
"start_time": "2021-04-05T02:19:39.902969Z"
|
||
|
|
},
|
||
|
|
"tags": [
|
||
|
|
"remove_output"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "356efdc9c90d4534aa381cc713b8bd23",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"text/plain": [
|
||
|
|
"HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=20.0), HTML(value='')))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "",
|
||
|
|
"version_major": 2,
|
||
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"source": [
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"rnn_3layer = nn.Sequential(\n",
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" EmbeddingPackable(nn.Embedding(vocab_size, D)), #(B, T) -> (B, T, D)\n",
|
||
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" nn.RNN(D, hidden_nodes, num_layers=3, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
|
||
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|
" LastTimeStep(rnn_layers=3), #We need to take the RNN output and reduce it to one item, (B, D)\n",
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" nn.Linear(hidden_nodes, classes), #(B, D) -> (B, classes)\n",
|
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")\n",
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"\n",
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"rnn_3layer.to(device)\n",
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"rnn_3layer_results = train_simple_network(rnn_3layer, loss_func, train_loader, test_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=20, lr=0.01)"
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{
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"ExecuteTime": {
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"end_time": "2021-04-05T02:22:10.895760Z",
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"start_time": "2021-04-05T02:22:10.629737Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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]
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},
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"execution_count": 30,
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"name": "stdout",
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"text": [
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"\n"
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}
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],
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"source": [
|
||
|
|
"rnn_3layer_bidir = nn.Sequential(\n",
|
||
|
|
" EmbeddingPackable(nn.Embedding(vocab_size, D)), #(B, T) -> (B, T, D)\n",
|
||
|
|
" nn.RNN(D, hidden_nodes, num_layers=3, batch_first=True, bidirectional=True), #(B, T, D) -> ( (B,T,D) , (S, B, D) )\n",
|
||
|
|
" LastTimeStep(rnn_layers=3, bidirectional=True), #We need to take the RNN output and reduce it to one item, (B, D)\n",
|
||
|
|
" nn.Linear(hidden_nodes*2, classes), #(B, D) -> (B, classes)\n",
|
||
|
|
")\n",
|
||
|
|
"\n",
|
||
|
|
"rnn_3layer_bidir.to(device)\n",
|
||
|
|
"rnn_3layer_bidir_results = train_simple_network(rnn_3layer_bidir, loss_func, train_loader, test_loader=test_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=20, lr=0.01)"
|
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},
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{
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"cell_type": "code",
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"execution_count": 32,
|
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-05T02:25:25.846176Z",
|
||
|
|
"start_time": "2021-04-05T02:25:25.546310Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1BhZ2VzIDIgMCBSIC9UeXBlIC9DYXRhbG9nID4+CmVuZG9iago4IDAgb2JqCjw8IC9FeHRHU3RhdGUgNCAwIFIgL0ZvbnQgMyAwIFIgL1BhdHRlcm4gNSAwIFIKL1Byb2NTZXQgWyAvUERGIC9UZXh0IC9JbWFnZUIgL0ltYWdlQyAvSW1hZ2VJIF0gL1NoYWRpbmcgNiAwIFIKL1hPYmplY3QgNyAwIFIgPj4KZW5kb2JqCjEwIDAgb2JqCjw8IC9Bbm5vdHMgWyBdIC9Db250ZW50cyA5IDAgUgovR3JvdXAgPDwgL0NTIC9EZXZpY2VSR0IgL1MgL1RyYW5zcGFyZW5jeSAvVHlwZSAvR3JvdXAgPj4KL01lZGlhQm94IFsgMCAwIDM5Mi4xMzc1IDI2Mi4xODM3NSBdIC9QYXJlbnQgMiAwIFIgL1Jlc291cmNlcyA4IDAgUgovVHlwZSAvUGFnZSA+PgplbmRvYmoKOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDExIDAgUiA+PgpzdHJlYW0KeJydV8tuHTcM3c9XaNkuLIuUqEd3NtIGCIoAcQ10Hdw6iQ07bZ20Rv6+h3PnzlDj+4gbwMj4mNKhyENSInc3nF+Q+/jFBXeHnydH7rU7f3Xz7+3m5ur1pdt8Gc4C/vAwxMaeYhH8cr/8whkfVb/u1dD++mkYPg/YGwteY9uPwyBhuygWL0k/sGtNvnXY/YKx4GPabbfWYmD4AP956/9HkOAMvuIUSglkyOKjSK5sOQ2YfJhIh0v4+jRcXrvzX8hRcNcfBik+BVZTgjGzb+Ku/xh+CD786K7v3M/Xw0g3UBBYRuFmeSx6nKiBSAJsS6hkiNjLiihhS0Egckdk0ONEsPGpVZYgrWOSZ0eq2LM15i50Fj3BVHRVDq2kWg1RWR+JWWBYQu5iZ9HjREzJJ0qMLBVLRM+yxEi8tNSoC55FTzBBeikzIU25NEv1LE8Rqc+JS46d2A16gqolL6E1CVl6qmeJikh+biFTx7SAx4ki/i5SSiuIdhc+k6jetcA+hkgsrnkuVT90wc1ff24+9a7tajYHXyTmWOFZyj732IEIYItCFZaS4VMumXMKaao/CfuZEDZIhnM2TAt2iqkFHIhjCzXyrtJF9jMRF49GVVsyVAY8xUUcvdQaqAVOO7J84Fik0QpBUHaGbAFPkmkMQ8ypMOWZ7NDJNGCUSSzXjJ2k0iBSY+gp1DxRlQPnYo1XRKtohsuAp8hYgxiTHi2EmWy/bgPGwxlhKqBflHG8EOqWw6jfh0GHnK7/evPlq7vYbP55fL/5ttvob/dsdGnhoF5QJim5xxv3u/vs2L1x5EXnjydG0GtmHZjoGtO/MmgwkHcex8LVetaa2YROi24bmhZNJR/RRxlnQV8kVI1o0jE2YotNmqNY0ZcDWhNgooiOGHUpoWWhXcVWFOfmU0TMiqOSPaPgx4Sa8WHzjP88OrBQclSbl5JCHrdBZ4fEs0SYg1ZwGNU7NSQj1NK4xzlgMSPkmDdogAhM1FI0HX60yDGl0TwhhDlz0OiSLzWlqu4wprFUZLus7EtE3cAor3D4nDlmTj1umrCODhQQnFOcyees5Qgc7qeSiRRHynEBCAnuE6KGeZFVmVGilh+N7RnVhGSLRj/m5gtiQbXHh9+Gd+7FQiJVT6sBFy4SVU8QzXPLCM8x9SRsh8jU3KnHtkIrHhxLpwWvxFPI1yAl8ko8FUsl4ma0Fg/S1YRCW6nHisqKB7QRoeK1eAz+PeKBN4lxUVuJp8O/RzzW3ojHirATj8WNeDqxWfFYeyOeToRGPNb+/4kH1oUnvUA6aEZJGwqmqP3LUR3pHZYKikH6NmRnj1FS11aMkrpUL0rqYm6U1OOLkrrYGil1sTJS6gqwk5LVgNGSHT1WSxH7azWttaSVQ7g95pWWko7XOnZjKyUDWyXF6BtuoCGtlISAEERCayUlxgBsofBKSXZ7IyQDTzpSiYT5sdQnf88jbd/bCzs9f7s97H+7wfY7n37Gctng8K7nF3H79nujD0z8PI1Hmp6bLHiMbi8KiNhMC9mMe80YolX0KotrX1xQxbZ2ETlG3IuB0mS0GWYMTVGkLfttwepr052dodBhKtPJJ18WaGPdXmCVHY6GwZ/YwArubGcqCxa77wzP/t936HxQwzXHY180N/oEv9w9wcfGA1WduAK5w1egAfrTFO+8R+Fj2vL4TCt74FEEYRTzlP3p3d/fF+vsucZ7u1Tveldv3/7k6OzX999uHndXvUOj1x0fvcbrnKf7Zef0jL7Y54y0jCtnl+Mhl7u2/oKGb7zXLPP41bm/wC/2P1U4Mn6tD+Aub1/dzsd4N/wHfmPFTAplbmRzdHJlYW0KZW5kb2JqCjExIDAgb2JqCjEzNjMKZW5kb2JqCjE2IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggODggPj4Kc3RyZWFtCnicNYy7DcAwCER7prgR+DiA94lSkf3bEFsuuHvSE+c5wMg+D0foxC1kQ+GmeEk5oT5RNFpvOrZIc7+8ZDMXFf0z3H2F7eaAZDRJ5CHR5XLlWSl6PpfaG34KZW5kc3RyZWFtCmVuZG9iagoxNyAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI1OSA+PgpzdHJlYW0KeJw9UklywzAMu/sVfAJ3Se9Jpyfn/9cC9NSXEGOKAAimd4vK2fhpK1l+5McuO0sit3wHbZP7iqoHpG6CzCXHJVeIWcrnSpBYtJSZWJ+pDsrPNahV+MJPzExMhyQRS8hJPYqwfl4H96B+vaTzW2T8o2OD0luSTAWdGu6Vo5TYsFSfGuQeNN2UVp+ZdmUHLI03ZKUmdfr10+MHSzClLxLRQYjEn+RyhywLKQfxdq7eQHhXuyDVUysPO0Saj5HeUgWrOTMBS0bTDiNgbdaYIFUCvEVrCLQW4vKFTisiPjk3dDBNVZ6FyLBS4Vh7z2gNF7qGvNJwepJx//kfvCve1+8f2vNmZAplbmRzdHJlYW0KZW5kb2JqCjE4IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTYwID4+CnN0cmVhbQp4nD2QSxLDIAxD95xCR8D4f550ukruv60NnWywGIT0wH1iIqMWJ4KvxIcGpcMk8GwVhHsw2RFyhOQETYaygyRxDQ0GpcKmY5nvyaZ10kqsvNF+2Xc0Zp10ii1Fh3Yh/+f1Ityj4BrrGTwPYDHI2kql7JkwWmWmrvKo6LUf0+E9D0SrxeX1Bq7w2kc3qSgqS+r+RHc02P6Rt/se3x+ZZTgkCmVuZHN0cmVhbQplbmRvYmoKMTkgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA1OSA+PgpzdHJlYW0KeJwzNTVXMFCwtAASpqZGCuZGlgophlxAPoiVy2VoaQ5m5YBZFsZABkgZnGEApMGac2B6crjSAKnhEFoKZW5kc3RyZWFtCmVuZG9iagoyMCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDc1ID4+CnN0cmVhbQp4nDWNsRHAMAgDe6ZgBMsBE/bJpcL7twn4aKQXHMjk5sGesnSwTecH9OekTfNCUZCqMNB0Zn1xaOQOVg4XXmhKr4roz0HvB5nqF5oKZW5kc3RyZWFtCmVuZG9iagoyMSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIyNyA+PgpzdHJlYW0KeJxFkEuOAyEQQ/ecwkeg/nCejrLq3H87LjrRbLAlKNczuQMTe/HITJRuvGS4O8wVn+EZMHP4SphsxEzoTlwjlK4U4VSfCI7L3rzpoIl7RM6jngVZ1c4NagFnkuaC7YIu54wVN87JrUblzfSj1xC+aXcf13mH9kjj3sNUvs451c67ighpC1nVtL6Qb
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABEP0lEQVR4nO3deXhU1fnA8e+bjYSQBEJCgIQQ9h3CYpRVERfQum9gSwsVqVasttqKWrX6q9adolCtVZG6gYoLtS6IVQn7GiBsYQ9JgJB932bO74+ZhBAmYUJmMlnez/PkSebec++8GcK8c8+55z1ijEEppZSqycvTASillGqaNEEopZRySBOEUkophzRBKKWUckgThFJKKYd8PB2AK4WFhZmYmBhPh6GUUs3Gli1bMowx4Y72tagEERMTw+bNmz0dhlJKNRsicrS2fdrFpJRSyiFNEEoppRzSBKGUUsohTRBKKaUc0gShlFLKIU0QSimlHNIEoZRSyqEWNQ9CKaXqa8vJLew8tZPIoEiig6LpFtSNtr5tPR1Wk6AJQinVKpVUlDB/63ze2/PeWfs6+nckOtiWLCq/KpNHSJsQRMQDETc+TRBKqVZnV+YuHol/hEO5h7i9/+3cOfRO0ovSOZZ/rOorOS+ZDcc3sPzg8jOODfILOiNhVCWQ4GjCA8JbVPLQBKGUajUqrBW8tfMtXt/+OqEBofzz8n8ypusYAMICwhjYceBZx5RUlJBakEpyXjLJ+clVCWRX5i6+O/odFmOpauvv7U9UUNSZCSTYlkC6BHbBx6t5veU2r2iVUk2OxWqhsKKwQedo69PW7W+eR/OO8sjqR9hxagdTekzh0QsfJaRNyDmP8/fxp1f7XvRq3+usfeXWck4UnKhKHNUTyNq0tZRaSqva+ogPXdt1tSWMdt2qurCig6LpGNCxQVceghDkF3Tex9dGE4RSqt5OFJ5gTeoa1qStYf3x9eSX5TfofB3adOCGPjdwa79biWwX6aIobYwxfJz0MS9ufhEfLx+en/A8U3pMccm5fb18bW/4wd3O2mc1Vk4VnSI5P5mU/JTTSSQvme3p2ykoL3BJDGAbM/nxth9ddr5KmiCUUudUXFHMlpNbWJO6hrVpazmUewiATgGdmBQ9iT7t+5z3J2BjDFtObuGdXe+wKHERE6ImMLX/VMZ0HYOXNOxO/FNFp3hi7RPEp8Yzusto/m/s/xERGNGgczrLS7yICIwgIjCCCzpfcMY+Yww5pTlVVx3ZJdkNei5/H/8GHV8bMca45cSeMGrUKKPlvpVqOGMMB3IOsDZtLWtS17Dl5BbKrGX4efkxqvMoxnQdw9iuY+nVvpfLBmVPFJ7go30fsWz/MrJKsugW1I3b+t3G9b2vd6orqKbvjn7HU+ueoriimD+M/ANT+09tcMJpiURkizFmlMN9miCUUgA5JTmsP76eNWm2q4T0onQAeoX0YkykLSGMjBjptk+rlcot5Xx39DuW7lvK1vSttPFuw5QeU5jabyqDwgad8/j8snz+tuFv/OfQfxjUcRDPjH+GniE93Rpzc6YJQil1lgprBTszdlZ1GyVmJGIwBPsFc1GXixgbOZYxXcfQObCzx2Lcl7WPpfuW8uWhLymuKGZI2BBu63cbk3tMpo13m7Pabzy+kT+v+TPpRencOfROZg+dja+Xrwcibz40QSilAEgtSK1KCBuOb6CgvAAv8WJI2BDGdh3LmMgxDO44GG8vb0+Heob8snyWH1zO0n1LOZx7mPZt2nNDb9ugdlRQFKWWUuZvnc+7u9+le3B3/jbubwwJH+LpsJsFTRBKtVJF5UVsPrm5KikcyTsCQJfALrZxhMixXNjlQoL9gj0bqJOMMWw8sZGl+5byv+T/YTVWxkWO43jhcQ7kHOC2frfxh5F/0FIZ9VBXgtC7mJRqQYwxJGUn2cYRUteyNX0r5dZy/L39GdV5FLf1u40xkWPoEdyjWc74FREu7HIhF3a5kBOFJ1i2fxmfJH2CF168dtlrjIsc5+kQWxS9glCqmcsqyWJd2jrWpq1lbdpaMoozAOjToY+t26jrGEZEjHDYZ98SWKwWvMSrWSa8pkCvIJRqQcqt5WxP3267BTVtDXsy92AwtG/TntFdRjMmcgxjuo6hU9tOng61UTS18ZKWRBOEUs3AsfxjrE21JYSNJzZSWF6It3gzLHwY98Tew9jIsQwIHaBvlsql3JogRGQyMB/wBt40xjxbY/8fgZ9Xi2UAEG6MyRKRI0A+YAEqarsEUqolKiovYuOJjVWDy8n5yQBEtovkqh5XMbbrWOK6xLml/o5SldyWIETEG1gIXA6kAJtEZLkxZndlG2PMC8AL9vbXAL83xmRVO81EY0yGu2JUqqmwGiv7svZVTVLblr6NCmsFAT4BXND5Am4fcDtju46le3B37WtXjcadVxBxwAFjzCEAEVkCXAfsrqX9NOBDN8ajVJNjjOGNHW/wwd4PyCqxfTbq16Ef0wdOZ2zXsQzvNBw/bz8PR6laK3cmiEjgWLXHKcCFjhqKSFtgMjCn2mYDrBARA/zTGPNGLcfOBmYDREdHuyBspRrPW4lvsSBhAeMjxzO5x2RGdxlNeNtwT4elFODeBOHoOri2e2qvAdbU6F4aa4xJE5FOwHcistcYs+qsE9oSxxtgu821oUEr1Vg+P/A587fO56oeV/G38X/TQnKqyXHnX2QKUL1IehSQVkvbqdToXjLGpNm/pwOfYeuyUqpFWJWyir+s/Quju4zmr2P/qslBNUnu/KvcBPQRkR4i4octCSyv2UhEQoCLgS+qbQsUkaDKn4ErgEQ3xqpUo9lxagcP/vQg/UL7MW/iPHy9tZicaprc1sVkjKkQkTnAt9huc33bGLNLRO6y73/d3vQGYIUxpvqahRHAZ/a7NXyAD4wx37grVqUay+Hcw9zz/T2EBYSxcNJCAn0DPR2SUrXSUhuq1auwVuAlXm7v5kkvSmf6V9MpsZTw3pT3HC5TqVofq9WQW1zeoHOIQPu253e3m5baUMoBi9XC8oPLeXXbq4S0CeFv4/9G/9D+bnmuvLI87lp5FzmlOSyavEiTQyt3Mq+EVUmnWLU/g9X7T5Fd1LAEEdauDZv/fJmLojtNE4RqldamruXFLS+yP3s/Q8KGcLzwONP+O417Yu9h5qCZLi1ZUWop5b7/3cfh3MP8Y9I/GNhxoMvOrZqHknILm49ks2r/KVYlnWLviXwAwoPaMLF/JwZ3DcGrAfMfA/zcU2JFE4RqVZKyk3h588usSVtDZLtIXrz4Ra7ofgW5pbk8tf4p5m+dz6qUVTw97mm6BTX8U77FauHh+IfZfHIzz094ntFdR7vgt1ANZYxhf3pB1af4zIJSokPb0r1jIN07trV/BdIl2B+v83jnNsZw8FQBPyVlEL//FOsPZVJSbsXP24tRMR2YO6U/E/qEM6BLUJOeGa9jEKpVSC9KZ2HCQj4/8DntfNvxm6G/YWr/qWfMUjbG8OWhL3lmwzNYjIWHLniIG/vceN7/gY0xPL3haZbuW8qfLvgT0wdOd9Wvo85DTlEZqw9ksCrpFPH7MzieWwJAr/BAojq05VhWEceyiyi3nH5P9PPxoluHAGI6BhLdse0Z3yPbB+Dnc3rcKreonDUHT58/NacYgJ5hgUzoG86EvmFc1LMjbf2a1udyXVFOtVpF5UW8s+sd3tn1DuXWcm7vfzuzh84mpE1IrcccLzjOn9f8mY0nNnJJ1CU8MeYJwgLC6v3c/9z+TxYkLGDmoJn8YdQfGvJrqPNQYbGyPSWHn5Jsb9o7UnKwGgjy92Fc7zD7m3Y4ke0Dqo6xWA1pOcUkZxVxJLOQ5Ezb96OZRRzNLKK43FLV1ksgskMA3UMDKSqrIOGY/fxtfBjTu6Pt/H3C6RbatFe30wShWh2L1cLnBz5nQcICMoozuDLmSu4bcZ/T3UZWY+W93e8xf+t82vm14/HRjzMpepLTz78saRl/WfcXrul5DX8dpxPhGktaTrG92+gUq/dnkFdSgQgMi2rPhL7hXNw3jGFR7fHxrv+/hzGGUwWlVcniaGXiyCpCgPF9bEkntlt7fM/j/J6iCUK
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.lineplot(x='epoch', y='test Accuracy', data=packed_train, label='RNN: 1-Layer')\n",
|
||
|
|
"sns.lineplot(x='epoch', y='test Accuracy', data=rnn_3layer_results, label='RNN: 3-Layer')\n",
|
||
|
|
"sns.lineplot(x='epoch', y='test Accuracy', data=rnn_3layer_bidir_results, label='RNN: 3-Layer BiDir')"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"author": "A Note About Classification & Meaning",
|
||
|
|
"celltoolbar": "Tags",
|
||
|
|
"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": "3.7.9"
|
||
|
|
},
|
||
|
|
"latex_envs": {
|
||
|
|
"LaTeX_envs_menu_present": true,
|
||
|
|
"autoclose": false,
|
||
|
|
"autocomplete": true,
|
||
|
|
"bibliofile": "biblio.bib",
|
||
|
|
"cite_by": "apalike",
|
||
|
|
"current_citInitial": 1,
|
||
|
|
"eqLabelWithNumbers": true,
|
||
|
|
"eqNumInitial": 1,
|
||
|
|
"hotkeys": {
|
||
|
|
"equation": "Ctrl-E",
|
||
|
|
"itemize": "Ctrl-I"
|
||
|
|
},
|
||
|
|
"labels_anchors": false,
|
||
|
|
"latex_user_defs": false,
|
||
|
|
"report_style_numbering": false,
|
||
|
|
"user_envs_cfg": false
|
||
|
|
},
|
||
|
|
"latex_metadata": {
|
||
|
|
"title": "Recurrent Neural Networks"
|
||
|
|
},
|
||
|
|
"varInspector": {
|
||
|
|
"cols": {
|
||
|
|
"lenName": 16,
|
||
|
|
"lenType": 16,
|
||
|
|
"lenVar": 40
|
||
|
|
},
|
||
|
|
"kernels_config": {
|
||
|
|
"python": {
|
||
|
|
"delete_cmd_postfix": "",
|
||
|
|
"delete_cmd_prefix": "del ",
|
||
|
|
"library": "var_list.py",
|
||
|
|
"varRefreshCmd": "print(var_dic_list())"
|
||
|
|
},
|
||
|
|
"r": {
|
||
|
|
"delete_cmd_postfix": ") ",
|
||
|
|
"delete_cmd_prefix": "rm(",
|
||
|
|
"library": "var_list.r",
|
||
|
|
"varRefreshCmd": "cat(var_dic_list()) "
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"types_to_exclude": [
|
||
|
|
"module",
|
||
|
|
"function",
|
||
|
|
"builtin_function_or_method",
|
||
|
|
"instance",
|
||
|
|
"_Feature"
|
||
|
|
],
|
||
|
|
"window_display": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|