Perceptron, MLPs Examples added

Educational/Hands-On-Machine-Learning/CH10 Introduction to Artificial Neural Networks with Keras/Introduction to Artificial Neural Networks with Keras.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CH10 Introduction to Artificial Neural Networks with Keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "example of using Perceptron:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.linear_model import Perceptron\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "iris = load_iris(as_frame=True)\n",
+    "X, y = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]\n",
+    "                 ].values, (iris.target == 0)\n",
+    "\n",
+    "per_clf = Perceptron(random_state=34)\n",
+    "per_clf.fit(X, y)\n",
+    "\n",
+    "X_new = [[2, 0.5], [3, 1]]\n",
+    "y_pred = per_clf.predict(X_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "example of using MLPs (multi-layer-perceptron)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sayed\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.5324025638801033"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPRegressor\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "housing = fetch_california_housing()\n",
+    "X_train, xtemp, y_train, ytemp = train_test_split(\n",
+    "    housing.data, housing.target, test_size=.4, random_state=34)\n",
+    "X_valid, X_test, y_valid, y_test = train_test_split(\n",
+    "    xtemp, ytemp, test_size=.5, random_state=3)\n",
+    "\n",
+    "mlp = MLPRegressor(hidden_layer_sizes=[50, 50, 50], random_state=3)\n",
+    "pipeline = make_pipeline(StandardScaler(), mlp)\n",
+    "pipeline.fit(X_train, y_train)\n",
+    "y_pred = pipeline.predict(X_valid)\n",
+    "rmse = mean_squared_error(y_valid, y_pred, squared=False)\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}