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@amirziai
Last active December 21, 2017 18:32
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing the Gradient Descent Algorithm\n",
"\n",
"In this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"#Some helper functions for plotting and drawing lines\n",
"\n",
"def plot_points(X, y):\n",
" admitted = X[np.argwhere(y==1)]\n",
" rejected = X[np.argwhere(y==0)]\n",
" plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'blue', edgecolor = 'k')\n",
" plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'red', edgecolor = 'k')\n",
"\n",
"def display(m, b, color='g--'):\n",
" plt.xlim(-0.05,1.05)\n",
" plt.ylim(-0.05,1.05)\n",
" x = np.arange(-10, 10, 0.1)\n",
" plt.plot(x, m*x+b, color)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading and plotting the data"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
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Vyp97EhDWnbu7L5rZ7ZTqtbqAB939GTO7rfz9w+7+rJl9F3gSeJlSGufpUK9CKRRXfXYn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\nNwOPrDjmEeCjVvI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"text/plain": [
"<matplotlib.figure.Figure at 0x10ffcb5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = pd.read_csv('data.csv', header=None)\n",
"X = np.array(data[[0,1]])\n",
"y = np.array(data[2])\n",
"plot_points(X,y)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TODO: Implementing the basic functions\n",
"Here is your turn to shine. Implement the following formulas, as explained in the text.\n",
"- Sigmoid activation function\n",
"\n",
"$$\\sigma(x) = \\frac{1}{1+e^{-x}}$$\n",
"\n",
"- Output (prediction) formula\n",
"\n",
"$$\\hat{y} = \\sigma(w_1 x_1 + w_2 x_2 + b)$$\n",
"\n",
"- Error function\n",
"\n",
"$$Error(y, \\hat{y}) = - y \\log(\\hat{y}) - (1-y) \\log(1-\\hat{y})$$\n",
"\n",
"- The function that updates the weights\n",
"\n",
"$$ w_i \\longrightarrow w_i + \\alpha (y - \\hat{y}) x_i$$\n",
"\n",
"$$ b \\longrightarrow b + \\alpha (y - \\hat{y})$$"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"# Implement the following functions\n",
"\n",
"# Activation (sigmoid) function\n",
"def sigmoid(x):\n",
" return 1 / (1 + np.exp(-x))\n",
"\n",
"def output_formula(features, weights, bias):\n",
" return sigmoid(np.dot(features, weights) + bias)\n",
"\n",
"def error_formula(y, output):\n",
" return - y*np.log(output) - (1 - y) * np.log(1-output)\n",
"\n",
"def update_weights(x, y, weights, bias, learnrate):\n",
" output = output_formula(x, weights, bias)\n",
" d_error = -(y - output)\n",
" weights -= learnrate * d_error * x\n",
" bias -= learnrate * d_error\n",
" return weights, bias"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training function\n",
"This function will help us iterate the gradient descent algorithm through all the data, for a number of epochs. It will also plot the data, and some of the boundary lines obtained as we run the algorithm."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(44)\n",
"\n",
"epochs = 100\n",
"learnrate = 0.01\n",
"\n",
"def train(features, targets, epochs, learnrate, graph_lines=False):\n",
" \n",
" errors = []\n",
" n_records, n_features = features.shape\n",
" last_loss = None\n",
" weights = np.random.normal(scale=1 / n_features**.5, size=n_features)\n",
" bias = 0\n",
" for e in range(epochs):\n",
" del_w = np.zeros(weights.shape)\n",
" for x, y in zip(features, targets):\n",
" output = output_formula(x, weights, bias)\n",
" error = error_formula(y, output)\n",
" weights, bias = update_weights(x, y, weights, bias, learnrate)\n",
" \n",
" # Printing out the log-loss error on the training set\n",
" out = output_formula(features, weights, bias)\n",
" loss = np.mean(error_formula(targets, out))\n",
" errors.append(loss)\n",
" if e % (epochs / 10) == 0:\n",
" print(\"\\n========== Epoch\", e,\"==========\")\n",
" if last_loss and last_loss < loss:\n",
" print(\"Train loss: \", loss, \" WARNING - Loss Increasing\")\n",
" else:\n",
" print(\"Train loss: \", loss)\n",
" last_loss = loss\n",
" predictions = out > 0.5\n",
" accuracy = np.mean(predictions == targets)\n",
" print(\"Accuracy: \", accuracy)\n",
" if graph_lines and e % (epochs / 100) == 0:\n",
" display(-weights[0]/weights[1], -bias/weights[1])\n",
" \n",
"\n",
" # Plotting the solution boundary\n",
" plt.title(\"Solution boundary\")\n",
" display(-weights[0]/weights[1], -bias/weights[1], 'black')\n",
"\n",
" # Plotting the data\n",
" plot_points(features, targets)\n",
" plt.show()\n",
"\n",
" # Plotting the error\n",
" plt.title(\"Error Plot\")\n",
" plt.xlabel('Number of epochs')\n",
" plt.ylabel('Error')\n",
" plt.plot(errors)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time to train the algorithm!\n",
"When we run the function, we'll obtain the following:\n",
"- 10 updates with the current training loss and accuracy\n",
"- A plot of the data and some of the boundary lines obtained. The final one is in black. Notice how the lines get closer and closer to the best fit, as we go through more epochs.\n",
"- A plot of the error function. Notice how it decreases as we go through more epochs."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"========== Epoch 0 ==========\n",
"Train loss: 0.713584519538\n",
"Accuracy: 0.4\n",
"\n",
"========== Epoch 10 ==========\n",
"Train loss: 0.622583521045\n",
"Accuracy: 0.59\n",
"\n",
"========== Epoch 20 ==========\n",
"Train loss: 0.554874408367\n",
"Accuracy: 0.74\n",
"\n",
"========== Epoch 30 ==========\n",
"Train loss: 0.501606141872\n",
"Accuracy: 0.84\n",
"\n",
"========== Epoch 40 ==========\n",
"Train loss: 0.459333464186\n",
"Accuracy: 0.86\n",
"\n",
"========== Epoch 50 ==========\n",
"Train loss: 0.425255434335\n",
"Accuracy: 0.93\n",
"\n",
"========== Epoch 60 ==========\n",
"Train loss: 0.397346157167\n",
"Accuracy: 0.93\n",
"\n",
"========== Epoch 70 ==========\n",
"Train loss: 0.374146976524\n",
"Accuracy: 0.93\n",
"\n",
"========== Epoch 80 ==========\n",
"Train loss: 0.354599733682\n",
"Accuracy: 0.94\n",
"\n",
"========== Epoch 90 ==========\n",
"Train loss: 0.337927365888\n",
"Accuracy: 0.94\n"
]
},
{
"data": {
"image/png": 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wJywhDFsLW8XPXacEMUUmPopODYkJQavXGjxlrMytOD/hPO94v2OY+/bD24zY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\nT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s01U5s6QjbFb0ohG2pM7gAaZZT3q+6jVCr54NvVWC3piFEJ6h6u7D1yhH4B/VjYeyEO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\ndyL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"text/plain": [
"<matplotlib.figure.Figure at 0x10ffefbe0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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pGfP9DninvMuKVFdmRm52Y3KzG7N+x1dM+HQtk/M28OqCzfTOashNg3IYfmZz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fb42400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train(X, y, epochs, learnrate, True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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