2018-04-25 21:14:22 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
2019-03-06 16:46:12 +00:00
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Óscar Araque"
2018-04-25 21:14:22 +00:00
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Introduction to Machine Learning III](2_4_0_Intro_NN.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MultiLayer Perceptron (MLP) applied to Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we are going to apply a MLP to a simple regression task: learning the Fresnel functions.\n",
"\n",
"\n",
"Answer directly in your copy of the exercise and submit it as a moodle task."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-27T19:44:45.989818",
"start_time": "2017-03-27T19:44:45.624698"
},
"collapsed": true
},
"outputs": [],
"source": [
"# Show plots in the notebooks\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-27T19:44:47.582964",
"start_time": "2017-03-27T19:44:46.844267"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set(color_codes=True)\n",
"\n",
"# function generator\n",
"from scipy.special import fresnel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Change this variables to change the train and test dataset."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-27T19:44:49.811655",
"start_time": "2017-03-27T19:44:49.805379"
},
"collapsed": true
},
"outputs": [],
"source": [
"# variables to change\n",
"\n",
"n_examples = 500\n",
"x_min = 0\n",
"x_max = 3\n",
"\n",
"fresnel_func = 0 # can be 0,1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-27T19:45:21.738411",
"start_time": "2017-03-27T19:45:21.489043"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fad88a8aa58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Generate dataset (do not change)\n",
"x = np.linspace(x_min, x_max, num=n_examples)\n",
"y = fresnel(x)[fresnel_func]\n",
"\n",
"X = x.reshape(-1,1)\n",
"\n",
"plt.plot(x, y)\n",
"plt.title('Fresnel S function')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make the MLP learn"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-27T19:45:33.201217",
"start_time": "2017-03-27T19:45:33.184488"
},
"collapsed": true
},
"outputs": [],
"source": [
"# do not change\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import r2_score\n",
"from sklearn.neural_network import MLPRegressor\n",
"\n",
"# split the dataset\n",
"X_train, X_test, y_train, y_test = \\\n",
" train_test_split(X, y, test_size=0.3, random_state=42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Change the parameters of the MLPRegressor to generate a network that learns the function."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-27T19:44:57.970215",
"start_time": "2017-03-27T19:44:57.916064"
}
},
"outputs": [
{
"data": {
"text/plain": [
"MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
" beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
" hidden_layer_sizes=(100,), learning_rate='constant',\n",
" learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
" nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
" shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
" verbose=False, warm_start=False)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# CHANGE THIS CODE TO MAKE THE MLP LEARN\n",
"\n",
"# train the network\n",
"mlp = MLPRegressor()\n",
"mlp.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this next cell, we can see how the network approximates the original function. \n",
"\n",
"The R score (a regression metric) must be between 0.95 and 1."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-27T19:45:44.523305",
"start_time": "2017-03-27T19:45:44.221047"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fadb8f2ee80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"R score: 0.226100740276\n"
]
}
],
"source": [
"# do not change\n",
"\n",
"# predict from test\n",
"preds = mlp.predict(X_test)\n",
"\n",
"plt.plot(x, y)\n",
"plt.scatter(X_test, preds, color='g')\n",
"plt.show()\n",
"\n",
"print('R score:',r2_score(y_test, preds))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [MLP documentation](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Licence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
2019-03-06 16:46:12 +00:00
"© Óscar Araque, Universidad Politécnica de Madrid."
2018-04-25 21:14:22 +00:00
]
}
],
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2019-03-06 16:46:12 +00:00
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2018-04-25 21:14:22 +00:00
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