Classification with Tensorflow
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classification.ipynb
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "corrected-wholesale",
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"metadata": {},
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"outputs": [],
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"source": [
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"!kaggle datasets download -d yasserh/breast-cancer-dataset"
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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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"id": "ranging-police",
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"metadata": {},
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"outputs": [],
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"source": [
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"!unzip -o breast-cancer-dataset.zip"
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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": 109,
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"id": "ideal-spouse",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import torch\n",
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"from torch import nn\n",
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"from torch.autograd import Variable\n",
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.metrics import accuracy_score\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from tensorflow.keras.utils import to_categorical\n",
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"import torch.nn.functional as F\n",
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"import pandas as pd"
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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": 110,
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"id": "major-compromise",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Model(nn.Module):\n",
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" def __init__(self, input_dim):\n",
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" super(Model, self).__init__()\n",
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" self.layer1 = nn.Linear(input_dim,50)\n",
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" self.layer2 = nn.Linear(50, 20)\n",
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" self.layer3 = nn.Linear(20, 3)\n",
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" \n",
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" def forward(self, x):\n",
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" x = F.relu(self.layer1(x))\n",
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" x = F.relu(self.layer2(x))\n",
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" x = F.softmax(self.layer3(x)) # To check with the loss function\n",
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" return x"
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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": 111,
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"id": "czech-regular",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>diagnosis</th>\n",
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" <th>radius_mean</th>\n",
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" <th>texture_mean</th>\n",
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" <th>perimeter_mean</th>\n",
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" <th>area_mean</th>\n",
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" <th>smoothness_mean</th>\n",
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" <th>compactness_mean</th>\n",
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" <th>concavity_mean</th>\n",
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" <th>concave points_mean</th>\n",
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" <th>symmetry_mean</th>\n",
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" <th>...</th>\n",
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" <th>radius_worst</th>\n",
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" <th>texture_worst</th>\n",
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" <th>perimeter_worst</th>\n",
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" <th>area_worst</th>\n",
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" <th>smoothness_worst</th>\n",
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" <th>compactness_worst</th>\n",
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" <th>concavity_worst</th>\n",
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" <th>concave points_worst</th>\n",
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" <th>symmetry_worst</th>\n",
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" <th>fractal_dimension_worst</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>id</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>842517</th>\n",
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" <td>M</td>\n",
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" <td>20.57</td>\n",
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" <td>17.77</td>\n",
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" <td>132.90</td>\n",
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" <td>1326.0</td>\n",
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" <td>0.08474</td>\n",
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" <td>0.07864</td>\n",
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" <td>0.08690</td>\n",
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||||||
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" <td>0.07017</td>\n",
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||||||
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" <td>0.1812</td>\n",
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||||||
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" <td>...</td>\n",
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||||||
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" <td>24.99</td>\n",
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||||||
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" <td>23.41</td>\n",
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||||||
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" <td>158.80</td>\n",
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||||||
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" <td>1956.0</td>\n",
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||||||
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" <td>0.1238</td>\n",
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||||||
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" <td>0.1866</td>\n",
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||||||
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" <td>0.2416</td>\n",
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||||||
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" <td>0.1860</td>\n",
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" <td>0.2750</td>\n",
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" <td>0.08902</td>\n",
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" </tr>\n",
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" <tr>\n",
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||||||
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" <th>84300903</th>\n",
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||||||
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" <td>M</td>\n",
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||||||
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" <td>19.69</td>\n",
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||||||
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" <td>21.25</td>\n",
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" <td>130.00</td>\n",
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" <td>1203.0</td>\n",
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||||||
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" <td>0.10960</td>\n",
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||||||
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" <td>0.15990</td>\n",
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||||||
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" <td>0.19740</td>\n",
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||||||
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" <td>0.12790</td>\n",
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||||||
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" <td>0.2069</td>\n",
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||||||
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" <td>...</td>\n",
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||||||
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" <td>23.57</td>\n",
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||||||
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" <td>25.53</td>\n",
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||||||
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" <td>152.50</td>\n",
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||||||
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" <td>1709.0</td>\n",
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||||||
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" <td>0.1444</td>\n",
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||||||
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" <td>0.4245</td>\n",
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||||||
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" <td>0.4504</td>\n",
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||||||
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" <td>0.2430</td>\n",
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||||||
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" <td>0.3613</td>\n",
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||||||
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" <td>0.08758</td>\n",
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" </tr>\n",
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||||||
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" <tr>\n",
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||||||
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" <th>84348301</th>\n",
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||||||
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" <td>M</td>\n",
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||||||
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" <td>11.42</td>\n",
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||||||
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" <td>20.38</td>\n",
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||||||
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" <td>77.58</td>\n",
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||||||
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" <td>386.1</td>\n",
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||||||
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" <td>0.14250</td>\n",
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||||||
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" <td>0.28390</td>\n",
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||||||
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" <td>0.24140</td>\n",
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||||||
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" <td>0.10520</td>\n",
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||||||
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" <td>0.2597</td>\n",
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||||||
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" <td>...</td>\n",
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||||||
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" <td>14.91</td>\n",
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||||||
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" <td>26.50</td>\n",
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||||||
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" <td>98.87</td>\n",
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||||||
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" <td>567.7</td>\n",
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||||||
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" <td>0.2098</td>\n",
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||||||
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" <td>0.8663</td>\n",
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||||||
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" <td>0.6869</td>\n",
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||||||
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" <td>0.2575</td>\n",
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||||||
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" <td>0.6638</td>\n",
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||||||
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" <td>0.17300</td>\n",
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||||||
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" </tr>\n",
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" <tr>\n",
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" <th>84358402</th>\n",
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||||||
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" <td>M</td>\n",
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||||||
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" <td>20.29</td>\n",
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||||||
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" <td>14.34</td>\n",
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||||||
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" <td>135.10</td>\n",
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||||||
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" <td>1297.0</td>\n",
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||||||
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" <td>0.10030</td>\n",
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||||||
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" <td>0.13280</td>\n",
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||||||
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" <td>0.19800</td>\n",
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||||||
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" <td>0.10430</td>\n",
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||||||
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" <td>0.1809</td>\n",
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||||||
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" <td>...</td>\n",
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||||||
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" <td>22.54</td>\n",
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||||||
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" <td>16.67</td>\n",
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||||||
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" <td>152.20</td>\n",
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||||||
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" <td>1575.0</td>\n",
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||||||
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" <td>0.1374</td>\n",
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||||||
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" <td>0.2050</td>\n",
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||||||
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" <td>0.4000</td>\n",
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||||||
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" <td>0.1625</td>\n",
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||||||
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" <td>0.2364</td>\n",
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" <td>0.07678</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>843786</th>\n",
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||||||
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" <td>M</td>\n",
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" <td>12.45</td>\n",
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" <td>15.70</td>\n",
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" <td>82.57</td>\n",
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" <td>477.1</td>\n",
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" <td>0.12780</td>\n",
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" <td>0.17000</td>\n",
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" <td>0.15780</td>\n",
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" <td>0.08089</td>\n",
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||||||
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" <td>0.2087</td>\n",
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" <td>...</td>\n",
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" <td>15.47</td>\n",
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" <td>23.75</td>\n",
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" <td>103.40</td>\n",
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" <td>741.6</td>\n",
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" <td>0.1791</td>\n",
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" <td>0.5249</td>\n",
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" <td>0.5355</td>\n",
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" <td>0.1741</td>\n",
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" <td>0.3985</td>\n",
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" <td>0.12440</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>844359</th>\n",
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" <td>M</td>\n",
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" <td>18.25</td>\n",
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" <td>19.98</td>\n",
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" <td>119.60</td>\n",
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||||||
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" <td>1040.0</td>\n",
|
||||||
|
" <td>0.09463</td>\n",
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" <td>0.10900</td>\n",
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||||||
|
" <td>0.11270</td>\n",
|
||||||
|
" <td>0.07400</td>\n",
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||||||
|
" <td>0.1794</td>\n",
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||||||
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" <td>...</td>\n",
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||||||
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" <td>22.88</td>\n",
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" <td>27.66</td>\n",
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" <td>153.20</td>\n",
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" <td>1606.0</td>\n",
|
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" <td>0.1442</td>\n",
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|
" <td>0.2576</td>\n",
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||||||
|
" <td>0.3784</td>\n",
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||||||
|
" <td>0.1932</td>\n",
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|
" <td>0.3063</td>\n",
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" <td>0.08368</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>84458202</th>\n",
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|
" <td>M</td>\n",
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|
" <td>13.71</td>\n",
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||||||
|
" <td>20.83</td>\n",
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" <td>90.20</td>\n",
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|
" <td>577.9</td>\n",
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||||||
|
" <td>0.11890</td>\n",
|
||||||
|
" <td>0.16450</td>\n",
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||||||
|
" <td>0.09366</td>\n",
|
||||||
|
" <td>0.05985</td>\n",
|
||||||
|
" <td>0.2196</td>\n",
|
||||||
|
" <td>...</td>\n",
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||||||
|
" <td>17.06</td>\n",
|
||||||
|
" <td>28.14</td>\n",
|
||||||
|
" <td>110.60</td>\n",
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||||||
|
" <td>897.0</td>\n",
|
||||||
|
" <td>0.1654</td>\n",
|
||||||
|
" <td>0.3682</td>\n",
|
||||||
|
" <td>0.2678</td>\n",
|
||||||
|
" <td>0.1556</td>\n",
|
||||||
|
" <td>0.3196</td>\n",
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|
" <td>0.11510</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>844981</th>\n",
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||||||
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" <td>M</td>\n",
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||||||
|
" <td>13.00</td>\n",
|
||||||
|
" <td>21.82</td>\n",
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||||||
|
" <td>87.50</td>\n",
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||||||
|
" <td>519.8</td>\n",
|
||||||
|
" <td>0.12730</td>\n",
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||||||
|
" <td>0.19320</td>\n",
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||||||
|
" <td>0.18590</td>\n",
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||||||
|
" <td>0.09353</td>\n",
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||||||
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" <td>0.2350</td>\n",
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||||||
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" <td>...</td>\n",
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||||||
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" <td>15.49</td>\n",
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||||||
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" <td>30.73</td>\n",
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||||||
|
" <td>106.20</td>\n",
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||||||
|
" <td>739.3</td>\n",
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||||||
|
" <td>0.1703</td>\n",
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||||||
|
" <td>0.5401</td>\n",
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|
" <td>0.5390</td>\n",
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|
" <td>0.2060</td>\n",
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" <td>0.4378</td>\n",
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" <td>0.10720</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>84501001</th>\n",
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" <td>M</td>\n",
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" <td>12.46</td>\n",
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" <td>24.04</td>\n",
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||||||
|
" <td>83.97</td>\n",
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||||||
|
" <td>475.9</td>\n",
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||||||
|
" <td>0.11860</td>\n",
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||||||
|
" <td>0.23960</td>\n",
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||||||
|
" <td>0.22730</td>\n",
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||||||
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" <td>0.08543</td>\n",
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||||||
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" <td>0.2030</td>\n",
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||||||
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" <td>...</td>\n",
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||||||
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" <td>15.09</td>\n",
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||||||
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" <td>40.68</td>\n",
|
||||||
|
" <td>97.65</td>\n",
|
||||||
|
" <td>711.4</td>\n",
|
||||||
|
" <td>0.1853</td>\n",
|
||||||
|
" <td>1.0580</td>\n",
|
||||||
|
" <td>1.1050</td>\n",
|
||||||
|
" <td>0.2210</td>\n",
|
||||||
|
" <td>0.4366</td>\n",
|
||||||
|
" <td>0.20750</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"<p>9 rows × 31 columns</p>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
|
||||||
|
"id \n",
|
||||||
|
"842517 M 20.57 17.77 132.90 1326.0 \n",
|
||||||
|
"84300903 M 19.69 21.25 130.00 1203.0 \n",
|
||||||
|
"84348301 M 11.42 20.38 77.58 386.1 \n",
|
||||||
|
"84358402 M 20.29 14.34 135.10 1297.0 \n",
|
||||||
|
"843786 M 12.45 15.70 82.57 477.1 \n",
|
||||||
|
"844359 M 18.25 19.98 119.60 1040.0 \n",
|
||||||
|
"84458202 M 13.71 20.83 90.20 577.9 \n",
|
||||||
|
"844981 M 13.00 21.82 87.50 519.8 \n",
|
||||||
|
"84501001 M 12.46 24.04 83.97 475.9 \n",
|
||||||
|
"\n",
|
||||||
|
" smoothness_mean compactness_mean concavity_mean \\\n",
|
||||||
|
"id \n",
|
||||||
|
"842517 0.08474 0.07864 0.08690 \n",
|
||||||
|
"84300903 0.10960 0.15990 0.19740 \n",
|
||||||
|
"84348301 0.14250 0.28390 0.24140 \n",
|
||||||
|
"84358402 0.10030 0.13280 0.19800 \n",
|
||||||
|
"843786 0.12780 0.17000 0.15780 \n",
|
||||||
|
"844359 0.09463 0.10900 0.11270 \n",
|
||||||
|
"84458202 0.11890 0.16450 0.09366 \n",
|
||||||
|
"844981 0.12730 0.19320 0.18590 \n",
|
||||||
|
"84501001 0.11860 0.23960 0.22730 \n",
|
||||||
|
"\n",
|
||||||
|
" concave points_mean symmetry_mean ... \\\n",
|
||||||
|
"id ... \n",
|
||||||
|
"842517 0.07017 0.1812 ... \n",
|
||||||
|
"84300903 0.12790 0.2069 ... \n",
|
||||||
|
"84348301 0.10520 0.2597 ... \n",
|
||||||
|
"84358402 0.10430 0.1809 ... \n",
|
||||||
|
"843786 0.08089 0.2087 ... \n",
|
||||||
|
"844359 0.07400 0.1794 ... \n",
|
||||||
|
"84458202 0.05985 0.2196 ... \n",
|
||||||
|
"844981 0.09353 0.2350 ... \n",
|
||||||
|
"84501001 0.08543 0.2030 ... \n",
|
||||||
|
"\n",
|
||||||
|
" radius_worst texture_worst perimeter_worst area_worst \\\n",
|
||||||
|
"id \n",
|
||||||
|
"842517 24.99 23.41 158.80 1956.0 \n",
|
||||||
|
"84300903 23.57 25.53 152.50 1709.0 \n",
|
||||||
|
"84348301 14.91 26.50 98.87 567.7 \n",
|
||||||
|
"84358402 22.54 16.67 152.20 1575.0 \n",
|
||||||
|
"843786 15.47 23.75 103.40 741.6 \n",
|
||||||
|
"844359 22.88 27.66 153.20 1606.0 \n",
|
||||||
|
"84458202 17.06 28.14 110.60 897.0 \n",
|
||||||
|
"844981 15.49 30.73 106.20 739.3 \n",
|
||||||
|
"84501001 15.09 40.68 97.65 711.4 \n",
|
||||||
|
"\n",
|
||||||
|
" smoothness_worst compactness_worst concavity_worst \\\n",
|
||||||
|
"id \n",
|
||||||
|
"842517 0.1238 0.1866 0.2416 \n",
|
||||||
|
"84300903 0.1444 0.4245 0.4504 \n",
|
||||||
|
"84348301 0.2098 0.8663 0.6869 \n",
|
||||||
|
"84358402 0.1374 0.2050 0.4000 \n",
|
||||||
|
"843786 0.1791 0.5249 0.5355 \n",
|
||||||
|
"844359 0.1442 0.2576 0.3784 \n",
|
||||||
|
"84458202 0.1654 0.3682 0.2678 \n",
|
||||||
|
"844981 0.1703 0.5401 0.5390 \n",
|
||||||
|
"84501001 0.1853 1.0580 1.1050 \n",
|
||||||
|
"\n",
|
||||||
|
" concave points_worst symmetry_worst fractal_dimension_worst \n",
|
||||||
|
"id \n",
|
||||||
|
"842517 0.1860 0.2750 0.08902 \n",
|
||||||
|
"84300903 0.2430 0.3613 0.08758 \n",
|
||||||
|
"84348301 0.2575 0.6638 0.17300 \n",
|
||||||
|
"84358402 0.1625 0.2364 0.07678 \n",
|
||||||
|
"843786 0.1741 0.3985 0.12440 \n",
|
||||||
|
"844359 0.1932 0.3063 0.08368 \n",
|
||||||
|
"84458202 0.1556 0.3196 0.11510 \n",
|
||||||
|
"844981 0.2060 0.4378 0.10720 \n",
|
||||||
|
"84501001 0.2210 0.4366 0.20750 \n",
|
||||||
|
"\n",
|
||||||
|
"[9 rows x 31 columns]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 111,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"class_names = ['Malignant', 'Benign']\n",
|
||||||
|
"data = pd.read_csv('breast-cancer.csv', index_col=0)\n",
|
||||||
|
"data[1:10]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 112,
|
||||||
|
"id": "outdoor-element",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"lb = LabelEncoder()\n",
|
||||||
|
"data['diagnosis'] = lb.fit_transform(data['diagnosis'])\n",
|
||||||
|
"features = data.iloc[:, 1:32].values\n",
|
||||||
|
"labels = np.array(data['diagnosis'])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 113,
|
||||||
|
"id": "buried-community",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([[2.057e+01, 1.777e+01, 1.329e+02, 1.326e+03, 8.474e-02, 7.864e-02,\n",
|
||||||
|
" 8.690e-02, 7.017e-02, 1.812e-01, 5.667e-02, 5.435e-01, 7.339e-01,\n",
|
||||||
|
" 3.398e+00, 7.408e+01, 5.225e-03, 1.308e-02, 1.860e-02, 1.340e-02,\n",
|
||||||
|
" 1.389e-02, 3.532e-03, 2.499e+01, 2.341e+01, 1.588e+02, 1.956e+03,\n",
|
||||||
|
" 1.238e-01, 1.866e-01, 2.416e-01, 1.860e-01, 2.750e-01, 8.902e-02],\n",
|
||||||
|
" [1.969e+01, 2.125e+01, 1.300e+02, 1.203e+03, 1.096e-01, 1.599e-01,\n",
|
||||||
|
" 1.974e-01, 1.279e-01, 2.069e-01, 5.999e-02, 7.456e-01, 7.869e-01,\n",
|
||||||
|
" 4.585e+00, 9.403e+01, 6.150e-03, 4.006e-02, 3.832e-02, 2.058e-02,\n",
|
||||||
|
" 2.250e-02, 4.571e-03, 2.357e+01, 2.553e+01, 1.525e+02, 1.709e+03,\n",
|
||||||
|
" 1.444e-01, 4.245e-01, 4.504e-01, 2.430e-01, 3.613e-01, 8.758e-02],\n",
|
||||||
|
" [1.142e+01, 2.038e+01, 7.758e+01, 3.861e+02, 1.425e-01, 2.839e-01,\n",
|
||||||
|
" 2.414e-01, 1.052e-01, 2.597e-01, 9.744e-02, 4.956e-01, 1.156e+00,\n",
|
||||||
|
" 3.445e+00, 2.723e+01, 9.110e-03, 7.458e-02, 5.661e-02, 1.867e-02,\n",
|
||||||
|
" 5.963e-02, 9.208e-03, 1.491e+01, 2.650e+01, 9.887e+01, 5.677e+02,\n",
|
||||||
|
" 2.098e-01, 8.663e-01, 6.869e-01, 2.575e-01, 6.638e-01, 1.730e-01],\n",
|
||||||
|
" [2.029e+01, 1.434e+01, 1.351e+02, 1.297e+03, 1.003e-01, 1.328e-01,\n",
|
||||||
|
" 1.980e-01, 1.043e-01, 1.809e-01, 5.883e-02, 7.572e-01, 7.813e-01,\n",
|
||||||
|
" 5.438e+00, 9.444e+01, 1.149e-02, 2.461e-02, 5.688e-02, 1.885e-02,\n",
|
||||||
|
" 1.756e-02, 5.115e-03, 2.254e+01, 1.667e+01, 1.522e+02, 1.575e+03,\n",
|
||||||
|
" 1.374e-01, 2.050e-01, 4.000e-01, 1.625e-01, 2.364e-01, 7.678e-02],\n",
|
||||||
|
" [1.245e+01, 1.570e+01, 8.257e+01, 4.771e+02, 1.278e-01, 1.700e-01,\n",
|
||||||
|
" 1.578e-01, 8.089e-02, 2.087e-01, 7.613e-02, 3.345e-01, 8.902e-01,\n",
|
||||||
|
" 2.217e+00, 2.719e+01, 7.510e-03, 3.345e-02, 3.672e-02, 1.137e-02,\n",
|
||||||
|
" 2.165e-02, 5.082e-03, 1.547e+01, 2.375e+01, 1.034e+02, 7.416e+02,\n",
|
||||||
|
" 1.791e-01, 5.249e-01, 5.355e-01, 1.741e-01, 3.985e-01, 1.244e-01],\n",
|
||||||
|
" [1.825e+01, 1.998e+01, 1.196e+02, 1.040e+03, 9.463e-02, 1.090e-01,\n",
|
||||||
|
" 1.127e-01, 7.400e-02, 1.794e-01, 5.742e-02, 4.467e-01, 7.732e-01,\n",
|
||||||
|
" 3.180e+00, 5.391e+01, 4.314e-03, 1.382e-02, 2.254e-02, 1.039e-02,\n",
|
||||||
|
" 1.369e-02, 2.179e-03, 2.288e+01, 2.766e+01, 1.532e+02, 1.606e+03,\n",
|
||||||
|
" 1.442e-01, 2.576e-01, 3.784e-01, 1.932e-01, 3.063e-01, 8.368e-02],\n",
|
||||||
|
" [1.371e+01, 2.083e+01, 9.020e+01, 5.779e+02, 1.189e-01, 1.645e-01,\n",
|
||||||
|
" 9.366e-02, 5.985e-02, 2.196e-01, 7.451e-02, 5.835e-01, 1.377e+00,\n",
|
||||||
|
" 3.856e+00, 5.096e+01, 8.805e-03, 3.029e-02, 2.488e-02, 1.448e-02,\n",
|
||||||
|
" 1.486e-02, 5.412e-03, 1.706e+01, 2.814e+01, 1.106e+02, 8.970e+02,\n",
|
||||||
|
" 1.654e-01, 3.682e-01, 2.678e-01, 1.556e-01, 3.196e-01, 1.151e-01],\n",
|
||||||
|
" [1.300e+01, 2.182e+01, 8.750e+01, 5.198e+02, 1.273e-01, 1.932e-01,\n",
|
||||||
|
" 1.859e-01, 9.353e-02, 2.350e-01, 7.389e-02, 3.063e-01, 1.002e+00,\n",
|
||||||
|
" 2.406e+00, 2.432e+01, 5.731e-03, 3.502e-02, 3.553e-02, 1.226e-02,\n",
|
||||||
|
" 2.143e-02, 3.749e-03, 1.549e+01, 3.073e+01, 1.062e+02, 7.393e+02,\n",
|
||||||
|
" 1.703e-01, 5.401e-01, 5.390e-01, 2.060e-01, 4.378e-01, 1.072e-01],\n",
|
||||||
|
" [1.246e+01, 2.404e+01, 8.397e+01, 4.759e+02, 1.186e-01, 2.396e-01,\n",
|
||||||
|
" 2.273e-01, 8.543e-02, 2.030e-01, 8.243e-02, 2.976e-01, 1.599e+00,\n",
|
||||||
|
" 2.039e+00, 2.394e+01, 7.149e-03, 7.217e-02, 7.743e-02, 1.432e-02,\n",
|
||||||
|
" 1.789e-02, 1.008e-02, 1.509e+01, 4.068e+01, 9.765e+01, 7.114e+02,\n",
|
||||||
|
" 1.853e-01, 1.058e+00, 1.105e+00, 2.210e-01, 4.366e-01, 2.075e-01]])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 113,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"features[1:10]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 114,
|
||||||
|
"id": "incredible-quantum",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([1, 1, 1, 1, 1, 1, 1, 1, 1])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 114,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"labels[1:10]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 115,
|
||||||
|
"id": "brazilian-butler",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"features_train, features_test, labels_train, labels_test = train_test_split(features, labels, random_state=42, shuffle=True)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 116,
|
||||||
|
"id": "exotic-method",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Training\n",
|
||||||
|
"model = Model(features_train.shape[1])\n",
|
||||||
|
"optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
|
||||||
|
"loss_fn = nn.CrossEntropyLoss()\n",
|
||||||
|
"epochs = 100\n",
|
||||||
|
"\n",
|
||||||
|
"def print_(loss):\n",
|
||||||
|
" print (\"The loss calculated: \", loss)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 117,
|
||||||
|
"id": "sharp-month",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Epoch # 1\n",
|
||||||
|
"The loss calculated: 0.922476053237915\n",
|
||||||
|
"Epoch # 2\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 3\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 4\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 5\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 6\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 7\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 8\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 9\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 10\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 11\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 12\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 13\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 14\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 15\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 16\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 17\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 18\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 19\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 20\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 21\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 22\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 23\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 24\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 25\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 26\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 27\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 28\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 29\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 30\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 31\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 32\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 33\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 34\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 35\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 36\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 37\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 38\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 39\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 40\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 41\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 42\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 43\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 44\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 45\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 46\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 47\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 48\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 49\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 50\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 51\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 52\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 53\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 54\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 55\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 56\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 57\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 58\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 59\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 60\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 61\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 62\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 63\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 64\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 65\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 66\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 67\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 68\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 69\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 70\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 71\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 72\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 73\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 74\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 75\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 76\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 77\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 78\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 79\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 80\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 81\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 82\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 83\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 84\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 85\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 86\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 87\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 88\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 89\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 90\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 91\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 92\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 93\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 94\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 95\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 96\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 97\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 98\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 99\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n",
|
||||||
|
"Epoch # 100\n",
|
||||||
|
"The loss calculated: 0.9223369359970093\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||||||
|
" # This is added back by InteractiveShellApp.init_path()\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Not using dataloader\n",
|
||||||
|
"x_train, y_train = Variable(torch.from_numpy(features_train)).float(), Variable(torch.from_numpy(labels_train)).long()\n",
|
||||||
|
"for epoch in range(1, epochs+1):\n",
|
||||||
|
" print (\"Epoch #\",epoch)\n",
|
||||||
|
" y_pred = model(x_train)\n",
|
||||||
|
" loss = loss_fn(y_pred, y_train)\n",
|
||||||
|
" print_(loss.item())\n",
|
||||||
|
" \n",
|
||||||
|
" # Zero gradients\n",
|
||||||
|
" optimizer.zero_grad()\n",
|
||||||
|
" loss.backward() # Gradients\n",
|
||||||
|
" optimizer.step() # Update"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 118,
|
||||||
|
"id": "mechanical-humidity",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||||||
|
" # This is added back by InteractiveShellApp.init_path()\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Prediction\n",
|
||||||
|
"x_test = Variable(torch.from_numpy(features_test)).float()\n",
|
||||||
|
"pred = model(x_test)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 119,
|
||||||
|
"id": "based-charleston",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([[1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.],\n",
|
||||||
|
" [1., 0., 0.]], dtype=float32)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 119,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"pred = pred.detach().numpy()\n",
|
||||||
|
"pred[1:10]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 120,
|
||||||
|
"id": "dried-accessory",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"The accuracy is 0.6223776223776224\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"print (\"The accuracy is\", accuracy_score(labels_test, np.argmax(pred, axis=1)))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 121,
|
||||||
|
"id": "effective-characterization",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"0"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 121,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"labels_test[0]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 122,
|
||||||
|
"id": "oriented-determination",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"torch.save(model, \"travel_insurance-pytorch.pkl\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 123,
|
||||||
|
"id": "infectious-wagon",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"saved_model = torch.load(\"travel_insurance-pytorch.pkl\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 124,
|
||||||
|
"id": "built-contributor",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||||||
|
" # This is added back by InteractiveShellApp.init_path()\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"0"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 124,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"np.argmax(saved_model(x_test[0]).detach().numpy(), axis=0)"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.7.3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user