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completed part 1 of deep learning

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Daniel Tomlinson
2019-07-18 22:21:59 +01:00
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244
python/Deep Learning/Introduction to Neural Networks/Gradient Descent/GradientDescent.ipynb Normal file
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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": null,
"metadata": {
"collapsed": true
},
"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": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"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": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Implement the following functions\n",
"\n",
"# Activation (sigmoid) function\n",
"def sigmoid(x):\n",
" pass\n",
"\n",
"# Output (prediction) formula\n",
"def output_formula(features, weights, bias):\n",
" pass\n",
"\n",
"# Error (log-loss) formula\n",
"def error_formula(y, output):\n",
" pass\n",
"\n",
"# Gradient descent step\n",
"def update_weights(x, y, weights, bias, learnrate):\n",
" pass"
]
},
{
"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": null,
"metadata": {
"collapsed": true
},
"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": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train(X, y, epochs, learnrate, True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
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"nbformat_minor": 2
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python/Deep Learning/Introduction to Neural Networks/Gradient Descent/GradientDescentSolutions.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Solutions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
":\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"
]
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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python/Deep Learning/Introduction to Neural Networks/Gradient Descent/data.csv Normal file
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122
python/Deep Learning/Introduction to Neural Networks/Gradient Descent/gradient_descent.py Normal file
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Some helper functions for plotting and drawing lines
def plot_points(X, y):
admitted = X[np.argwhere(y == 1)]
rejected = X[np.argwhere(y == 0)]
plt.scatter([s[0][0] for s in rejected],
[s[0][1] for s in rejected], s=25,
color='blue', edgecolor='k')
plt.scatter([s[0][0] for s in admitted],
[s[0][1] for s in admitted],
s=25, color='red', edgecolor='k')
def display(m, b, color='g--'):
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
x = np.arange(-10, 10, 0.1)
plt.plot(x, m * x + b, color)
data = pd.read_csv('data.csv', header=None)
X = np.array(data[[0, 1]])
y = np.array(data[2])
plot_points(X, y)
plt.show()
# Implement the following functions
# Activation (sigmoid) function
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# Output (prediction) formula
def output_formula(features, weights, bias):
return sigmoid(np.dot(features, weights) + bias)
# Error (log-loss) formula
def error_formula(y, output):
return -y * np.log(output) - (1 - y) * np.log(1 - output)
# Gradient descent step
def update_weights(x, y, weights, bias, learnrate):
output = output_formula(x, weights, bias)
d_error = y - output
weights += learnrate * d_error * x
bias += learnrate * d_error
return weights, bias
"""
Training function
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.
"""
np.random.seed(44)
epochs = 100
learnrate = 0.01
def train(features, targets, epochs, learnrate, graph_lines=False):
errors = []
n_records, n_features = features.shape
last_loss = None
weights = np.random.normal(scale=1 / n_features**.5, size=n_features)
bias = 0
for e in range(epochs):
del_w = np.zeros(weights.shape)
for x, y in zip(features, targets):
output = output_formula(x, weights, bias)
error = error_formula(y, output)
weights, bias = update_weights(x, y, weights, bias, learnrate)
# Printing out the log-loss error on the training set
out = output_formula(features, weights, bias)
loss = np.mean(error_formula(targets, out))
errors.append(loss)
if e % (epochs / 10) == 0:
print("\n========== Epoch", e, "==========")
if last_loss and last_loss < loss:
print("Train loss: ", loss, " WARNING - Loss Increasing")
else:
print("Train loss: ", loss)
last_loss = loss
predictions = out > 0.5
accuracy = np.mean(predictions == targets)
print("Accuracy: ", accuracy)
if graph_lines and e % (epochs / 100) == 0:
display(-weights[0] / weights[1], -bias / weights[1])
# Plotting the solution boundary
plt.title("Solution boundary")
display(-weights[0] / weights[1], -bias / weights[1], 'black')
# Plotting the data
plot_points(features, targets)
plt.show()
# Plotting the error
plt.title("Error Plot")
plt.xlabel('Number of epochs')
plt.ylabel('Error')
plt.plot(errors)
plt.show()
train(X, y, epochs, learnrate, True)

100
python/Deep Learning/Introduction to Neural Networks/Perceptron Algorithm/data.csv Normal file
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0.73229,0.64245,0
0.76145,0.60138,0
0.58985,0.86955,0
0.73145,0.74516,0
0.77029,0.7014,0
0.73156,0.71782,0
0.44556,0.57991,0
0.85275,0.85987,0
0.51912,0.62359,0
1 0.78051 -0.063669 1
2 0.28774 0.29139 1
3 0.40714 0.17878 1
4 0.2923 0.4217 1
5 0.50922 0.35256 1
6 0.27785 0.10802 1
7 0.27527 0.33223 1
8 0.43999 0.31245 1
9 0.33557 0.42984 1
10 0.23448 0.24986 1
11 0.0084492 0.13658 1
12 0.12419 0.33595 1
13 0.25644 0.42624 1
14 0.4591 0.40426 1
15 0.44547 0.45117 1
16 0.42218 0.20118 1
17 0.49563 0.21445 1
18 0.30848 0.24306 1
19 0.39707 0.44438 1
20 0.32945 0.39217 1
21 0.40739 0.40271 1
22 0.3106 0.50702 1
23 0.49638 0.45384 1
24 0.10073 0.32053 1
25 0.69907 0.37307 1
26 0.29767 0.69648 1
27 0.15099 0.57341 1
28 0.16427 0.27759 1
29 0.33259 0.055964 1
30 0.53741 0.28637 1
31 0.19503 0.36879 1
32 0.40278 0.035148 1
33 0.21296 0.55169 1
34 0.48447 0.56991 1
35 0.25476 0.34596 1
36 0.21726 0.28641 1
37 0.67078 0.46538 1
38 0.3815 0.4622 1
39 0.53838 0.32774 1
40 0.4849 0.26071 1
41 0.37095 0.38809 1
42 0.54527 0.63911 1
43 0.32149 0.12007 1
44 0.42216 0.61666 1
45 0.10194 0.060408 1
46 0.15254 0.2168 1
47 0.45558 0.43769 1
48 0.28488 0.52142 1
49 0.27633 0.21264 1
50 0.39748 0.31902 1
51 0.5533 1 0
52 0.44274 0.59205 0
53 0.85176 0.6612 0
54 0.60436 0.86605 0
55 0.68243 0.48301 0
56 1 0.76815 0
57 0.72989 0.8107 0
58 0.67377 0.77975 0
59 0.78761 0.58177 0
60 0.71442 0.7668 0
61 0.49379 0.54226 0
62 0.78974 0.74233 0
63 0.67905 0.60921 0
64 0.6642 0.72519 0
65 0.79396 0.56789 0
66 0.70758 0.76022 0
67 0.59421 0.61857 0
68 0.49364 0.56224 0
69 0.77707 0.35025 0
70 0.79785 0.76921 0
71 0.70876 0.96764 0
72 0.69176 0.60865 0
73 0.66408 0.92075 0
74 0.65973 0.66666 0
75 0.64574 0.56845 0
76 0.89639 0.7085 0
77 0.85476 0.63167 0
78 0.62091 0.80424 0
79 0.79057 0.56108 0
80 0.58935 0.71582 0
81 0.56846 0.7406 0
82 0.65912 0.71548 0
83 0.70938 0.74041 0
84 0.59154 0.62927 0
85 0.45829 0.4641 0
86 0.79982 0.74847 0
87 0.60974 0.54757 0
88 0.68127 0.86985 0
89 0.76694 0.64736 0
90 0.69048 0.83058 0
91 0.68122 0.96541 0
92 0.73229 0.64245 0
93 0.76145 0.60138 0
94 0.58985 0.86955 0
95 0.73145 0.74516 0
96 0.77029 0.7014 0
97 0.73156 0.71782 0
98 0.44556 0.57991 0
99 0.85275 0.85987 0
100 0.51912 0.62359 0

54
python/Deep Learning/Introduction to Neural Networks/Perceptron Algorithm/perceptron.py Normal file
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import numpy as np
# Setting the random seed, feel free to change it and see different solutions.
np.random.seed(42)
def stepFunction(t):
if t >= 0:
return 1
return 0
def prediction(X, W, b):
return stepFunction((np.matmul(X, W) + b)[0])
# TODO: Fill in the code below to implement the perceptron trick.
# The function should receive as inputs the data X, the labels y,
# the weights W (as an array), and the bias b,
# update the weights and bias W, b, according to the perceptron algorithm,
# and return W and b.
def perceptronStep(X, y, W, b, learn_rate=0.01):
for i in range(len(X)):
y_hat = prediction(X[i], W, b)
if y[i] - y_hat == 1:
W[0] += X[i][0] * learn_rate
W[1] += X[i][1] * learn_rate
b += learn_rate
elif y[i] - y_hat == -1:
W[0] -= X[i][0] * learn_rate
W[1] -= X[i][1] * learn_rate
b -= learn_rate
return W, b
# This function runs the perceptron algorithm repeatedly on the dataset,
# and returns a few of the boundary lines obtained in the iterations,
# for plotting purposes.
# Feel free to play with the learning rate and the num_epochs,
# and see your results plotted below.
def trainPerceptronAlgorithm(X, y, learn_rate=0.01, num_epochs=25):
x_min, x_max = min(X.T[0]), max(X.T[0])
y_min, y_max = min(X.T[1]), max(X.T[1])
W = np.array(np.random.rand(2, 1))
b = np.random.rand(1)[0] + x_max
# These are the solution lines that get plotted below.
boundary_lines = []
for i in range(num_epochs):
# In each epoch, we apply the perceptron step.
W, b = perceptronStep(X, y, W, b, learn_rate)
boundary_lines.append((-W[0] / W[1], -b / W[1]))
return boundary_lines

32
python/Deep Learning/Introduction to Neural Networks/Perceptrons as Logical Operators/perceptron_and.py Normal file
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import pandas as pd
# TODO: Set weight1, weight2, and bias
weight1 = 1.0
weight2 = 1.0
bias = -1.25
# DON'T CHANGE ANYTHING BELOW
# Inputs and outputs
test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
correct_outputs = [False, False, False, True]
outputs = []
# Generate and check output
for test_input, correct_output in zip(test_inputs, correct_outputs):
linear_combination = weight1 * \
test_input[0] + weight2 * test_input[1] + bias
output = int(linear_combination >= 0)
is_correct_string = 'Yes' if output == correct_output else 'No'
outputs.append([test_input[0], test_input[1],
linear_combination, output, is_correct_string])
# Print output
num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
output_frame = pd.DataFrame(outputs, columns=[
'Input 1', ' Input 2', ' Linear Combination', ' Activation Output', ' Is Correct'])
if not num_wrong:
print('Nice! You got it all correct.\n')
else:
print('You got {} wrong. Keep trying!\n'.format(num_wrong))
print(output_frame.to_string(index=False))

29
python/Deep Learning/Introduction to Neural Networks/Perceptrons as Logical Operators/perceptron_not.py Normal file
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import pandas as pd
# TODO: Set weight1, weight2, and bias
weight1 = 0
weight2 = -1
bias = 0.5
# DON'T CHANGE ANYTHING BELOW
# Inputs and outputs
test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
correct_outputs = [True, False, True, False]
outputs = []
# Generate and check output
for test_input, correct_output in zip(test_inputs, correct_outputs):
linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
output = int(linear_combination >= 0)
is_correct_string = 'Yes' if output == correct_output else 'No'
outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
# Print output
num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
output_frame = pd.DataFrame(outputs, columns=['Input 1', ' Input 2', ' Linear Combination', ' Activation Output', ' Is Correct'])
if not num_wrong:
print('Nice! You got it all correct.\n')
else:
print('You got {} wrong. Keep trying!\n'.format(num_wrong))
print(output_frame.to_string(index=False))

10
python/Deep Learning/Introduction to Neural Networks/Softmax/cross_entropy.py Normal file
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import numpy as np
# Write a function that takes as input two lists Y, P,
# and returns the float corresponding to their cross-entropy.
def cross_entropy(Y, P):
Y = np.float_(Y)
P = np.float_(P)
return -np.sum(Y * np.log(P) + (1 - Y) * np.log(1 - P))

18
python/Deep Learning/Introduction to Neural Networks/Softmax/softmax.py Normal file
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import numpy as np
# Write a function that takes as input a list of numbers, and returns
# the list of values given by the softmax function.
def softmax(L):
expL = np.exp(L)
sumExpL = sum(expL)
result = []
for i in expL:
result.append(i * 1.0 / sumExpL)
return result
# Note: The function np.divide can also be used here, as follows:
# def softmax(L):
# expL = np.exp(L)
# return np.divide (expL, expL.sum())

947
python/Deep Learning/Introduction to Neural Networks/Student Admissions(Neural Network)/StudentAdmissions.ipynb Normal file
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115
python/Deep Learning/Introduction to Neural Networks/Student Admissions(Neural Network)/StudentAdmissionsSolutions.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Solutions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### One-hot encoding the rank"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Make dummy variables for rank\n",
"one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)\n",
"\n",
"# Drop the previous rank column\n",
"one_hot_data = one_hot_data.drop('rank', axis=1)\n",
"\n",
"# Print the first 10 rows of our data\n",
"one_hot_data[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scaling the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Copying our data\n",
"processed_data = one_hot_data[:]\n",
"\n",
"# Scaling the columns\n",
"processed_data['gre'] = processed_data['gre']/800\n",
"processed_data['gpa'] = processed_data['gpa']/4.0\n",
"processed_data[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Backpropagating the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def error_term_formula(x, y, output):\n",
" return (y - output)*sigmoid_prime(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## alternative solution ##\n",
"# you could also *only* use y and the output \n",
"# and calculate sigmoid_prime directly from the activated output!\n",
"\n",
"# below is an equally valid solution (it doesn't utilize x)\n",
"def error_term_formula(x, y, output):\n",
" return (y-output) * output * (1 - output)"
]
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

181
python/Deep Learning/Introduction to Neural Networks/Student Admissions(Neural Network)/student_admissions.py Normal file
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# Importing pandas and numpy
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
# Printing out the first 10 rows of our data
print(data[:10])
# Importing matplotlib
# Function to help us plot
def plot_points(data):
X = np.array(data[["gre", "gpa"]])
y = np.array(data["admit"])
admitted = X[np.argwhere(y == 1)]
rejected = X[np.argwhere(y == 0)]
plt.scatter([s[0][0] for s in rejected], [s[0][1]
for s in rejected],
s=25, color='red', edgecolor='k')
plt.scatter([s[0][0] for s in admitted], [s[0][1]
for s in admitted],
s=25, color='cyan', edgecolor='k')
plt.xlabel('Test (GRE)')
plt.ylabel('Grades (GPA)')
# Plotting the points
plot_points(data)
plt.show()
# Separating the ranks
data_rank1 = data[data["rank"] == 1]
data_rank2 = data[data["rank"] == 2]
data_rank3 = data[data["rank"] == 3]
data_rank4 = data[data["rank"] == 4]
# Plotting the graphs
plot_points(data_rank1)
plt.title("Rank 1")
plt.show()
plot_points(data_rank2)
plt.title("Rank 2")
plt.show()
plot_points(data_rank3)
plt.title("Rank 3")
plt.show()
plot_points(data_rank4)
plt.title("Rank 4")
plt.show()
# TODO: Make dummy variables for rank
one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')],
axis=1)
# TODO: Drop the previous rank column
one_hot_data = one_hot_data.drop('rank', axis=1)
# Print the first 10 rows of our data
one_hot_data[:10]
# Making a copy of our data
processed_data = one_hot_data[:]
# TODO: Scale the columns
processed_data['gre'] = processed_data['gre'] / 800
processed_data['gpa'] = processed_data['gpa'] / 4.0
processed_data[:10]
# Printing the first 10 rows of our procesed data
processed_data[:10]
sample = np.random.choice(processed_data.index, size=int(
len(processed_data) * 0.9), replace=False)
train_data, test_data = processed_data.iloc[sample], processed_data.drop(
sample)
print("Number of training samples is", len(train_data))
print("Number of testing samples is", len(test_data))
print(train_data[:10])
print(test_data[:10])
features = train_data.drop('admit', axis=1)
targets = train_data['admit']
features_test = test_data.drop('admit', axis=1)
targets_test = test_data['admit']
print(features[:10])
print(targets[:10])
# Activation (sigmoid) function
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def sigmoid_prime(x):
return sigmoid(x) * (1 - sigmoid(x))
def error_formula(y, output):
return - y * np.log(output) - (1 - y) * np.log(1 - output)
# TODO: Write the error term formula
def error_term_formula(x, y, output):
return (y - output) * sigmoid_prime(x)
# Neural Network hyperparameters
epochs = 1000
learnrate = 0.5
# Training function
def train_nn(features, targets, epochs, learnrate):
# Use to same seed to make debugging easier
np.random.seed(42)
n_records, n_features = features.shape
last_loss = None
# Initialize weights
weights = np.random.normal(scale=1 / n_features**.5, size=n_features)
for e in range(epochs):
del_w = np.zeros(weights.shape)
for x, y in zip(features.values, targets):
# Loop through all records, x is the input, y is the target
# Activation of the output unit
# Notice we multiply the inputs and the weights here
# rather than storing h as a separate variable
output = sigmoid(np.dot(x, weights))
# The error, the target minus the network output
error = error_formula(y, output)
# The error term
error_term = error_term_formula(x, y, output)
# The gradient descent step, the error times the gradient times the inputs
del_w += error_term * x
# Update the weights here. The learning rate times the
# change in weights, divided by the number of records to average
weights += learnrate * del_w / n_records
# Printing out the mean square error on the training set
if e % (epochs / 10) == 0:
out = sigmoid(np.dot(features, weights))
loss = np.mean((out - targets) ** 2)
print("Epoch:", e)
if last_loss and last_loss < loss:
print("Train loss: ", loss, " WARNING - Loss Increasing")
else:
print("Train loss: ", loss)
last_loss = loss
print("=========")
print("Finished training!")
return weights
weights = train_nn(features, targets, epochs, learnrate)
# Calculate accuracy on test data
test_out = sigmoid(np.dot(features_test, weights))
predictions = test_out > 0.5
accuracy = np.mean(predictions == targets_test)
print("Prediction accuracy: {:.3f}".format(accuracy))

401
python/Deep Learning/Introduction to Neural Networks/Student Admissions(Neural Network)/student_data.csv Normal file
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admit,gre,gpa,rank
0,380,3.61,3
1,660,3.67,3
1,800,4,1
1,640,3.19,4
0,520,2.93,4
1,760,3,2
1,560,2.98,1
0,400,3.08,2
1,540,3.39,3
0,700,3.92,2
0,800,4,4
0,440,3.22,1
1,760,4,1
0,700,3.08,2
1,700,4,1
0,480,3.44,3
0,780,3.87,4
0,360,2.56,3
0,800,3.75,2
1,540,3.81,1
0,500,3.17,3
1,660,3.63,2
0,600,2.82,4
0,680,3.19,4
1,760,3.35,2
1,800,3.66,1
1,620,3.61,1
1,520,3.74,4
1,780,3.22,2
0,520,3.29,1
0,540,3.78,4
0,760,3.35,3
0,600,3.4,3
1,800,4,3
0,360,3.14,1
0,400,3.05,2
0,580,3.25,1
0,520,2.9,3
1,500,3.13,2
1,520,2.68,3
0,560,2.42,2
1,580,3.32,2
1,600,3.15,2
0,500,3.31,3
0,700,2.94,2
1,460,3.45,3
1,580,3.46,2
0,500,2.97,4
0,440,2.48,4
0,400,3.35,3
0,640,3.86,3
0,440,3.13,4
0,740,3.37,4
1,680,3.27,2
0,660,3.34,3
1,740,4,3
0,560,3.19,3
0,380,2.94,3
0,400,3.65,2
0,600,2.82,4
1,620,3.18,2
0,560,3.32,4
0,640,3.67,3
1,680,3.85,3
0,580,4,3
0,600,3.59,2
0,740,3.62,4
0,620,3.3,1
0,580,3.69,1
0,800,3.73,1
0,640,4,3
0,300,2.92,4
0,480,3.39,4
0,580,4,2
0,720,3.45,4
0,720,4,3
0,560,3.36,3
1,800,4,3
0,540,3.12,1
1,620,4,1
0,700,2.9,4
0,620,3.07,2
0,500,2.71,2
0,380,2.91,4
1,500,3.6,3
0,520,2.98,2
0,600,3.32,2
0,600,3.48,2
0,700,3.28,1
1,660,4,2
0,700,3.83,2
1,720,3.64,1
0,800,3.9,2
0,580,2.93,2
1,660,3.44,2
0,660,3.33,2
0,640,3.52,4
0,480,3.57,2
0,700,2.88,2
0,400,3.31,3
0,340,3.15,3
0,580,3.57,3
0,380,3.33,4
0,540,3.94,3
1,660,3.95,2
1,740,2.97,2
1,700,3.56,1
0,480,3.13,2
0,400,2.93,3
0,480,3.45,2
0,680,3.08,4
0,420,3.41,4
0,360,3,3
0,600,3.22,1
0,720,3.84,3
0,620,3.99,3
1,440,3.45,2
0,700,3.72,2
1,800,3.7,1
0,340,2.92,3
1,520,3.74,2
1,480,2.67,2
0,520,2.85,3
0,500,2.98,3
0,720,3.88,3
0,540,3.38,4
1,600,3.54,1
0,740,3.74,4
0,540,3.19,2
0,460,3.15,4
1,620,3.17,2
0,640,2.79,2
0,580,3.4,2
0,500,3.08,3
0,560,2.95,2
0,500,3.57,3
0,560,3.33,4
0,700,4,3
0,620,3.4,2
1,600,3.58,1
0,640,3.93,2
1,700,3.52,4
0,620,3.94,4
0,580,3.4,3
0,580,3.4,4
0,380,3.43,3
0,480,3.4,2
0,560,2.71,3
1,480,2.91,1
0,740,3.31,1
1,800,3.74,1
0,400,3.38,2
1,640,3.94,2
0,580,3.46,3
0,620,3.69,3
1,580,2.86,4
0,560,2.52,2
1,480,3.58,1
0,660,3.49,2
0,700,3.82,3
0,600,3.13,2
0,640,3.5,2
1,700,3.56,2
0,520,2.73,2
0,580,3.3,2
0,700,4,1
0,440,3.24,4
0,720,3.77,3
0,500,4,3
0,600,3.62,3
0,400,3.51,3
0,540,2.81,3
0,680,3.48,3
1,800,3.43,2
0,500,3.53,4
1,620,3.37,2
0,520,2.62,2
1,620,3.23,3
0,620,3.33,3
0,300,3.01,3
0,620,3.78,3
0,500,3.88,4
0,700,4,2
1,540,3.84,2
0,500,2.79,4
0,800,3.6,2
0,560,3.61,3
0,580,2.88,2
0,560,3.07,2
0,500,3.35,2
1,640,2.94,2
0,800,3.54,3
0,640,3.76,3
0,380,3.59,4
1,600,3.47,2
0,560,3.59,2
0,660,3.07,3
1,400,3.23,4
0,600,3.63,3
0,580,3.77,4
0,800,3.31,3
1,580,3.2,2
1,700,4,1
0,420,3.92,4
1,600,3.89,1
1,780,3.8,3
0,740,3.54,1
1,640,3.63,1
0,540,3.16,3
0,580,3.5,2
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1,600,3.56,2
1,660,2.91,3
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1,460,3.64,1
0,460,2.98,1
1,560,3.59,2
0,540,3.28,3
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1,720,3.42,2
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1,800,3.53,1
0,620,3.05,2
1,660,3.49,2
0,480,4,2
0,500,2.86,4
0,700,3.45,3
0,440,2.76,2
1,520,3.81,1
1,680,2.96,3
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0,800,3.91,3
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0,680,3.64,3
0,640,3.73,3
0,660,3.31,4
0,620,3.21,4
1,520,4,2
1,540,3.55,4
1,740,3.52,4
0,640,3.35,3
1,520,3.3,2
1,620,3.95,3
0,520,3.51,2
0,640,3.81,2
0,680,3.11,2
0,440,3.15,2
1,520,3.19,3
1,620,3.95,3
1,520,3.9,3
0,380,3.34,3
0,560,3.24,4
1,600,3.64,3
1,680,3.46,2
0,500,2.81,3
1,640,3.95,2
0,540,3.33,3
1,680,3.67,2
0,660,3.32,1
0,520,3.12,2
1,600,2.98,2
0,460,3.77,3
1,580,3.58,1
1,680,3,4
1,660,3.14,2
0,660,3.94,2
0,360,3.27,3
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0,520,3.1,4
1,440,3.39,2
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1,800,3.22,1
1,660,3.7,4
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1,620,3.45,2
0,800,2.78,2
0,680,3.7,2
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0,720,3.4,3
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1,660,3.6,3
1,400,3.15,2
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0,220,2.83,3
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1,540,3.17,1
0,580,3.51,2
0,540,3.13,2
0,440,2.98,3
0,560,4,3
0,660,3.67,2
0,660,3.77,3
1,520,3.65,4
0,540,3.46,4
1,300,2.84,2
1,340,3,2
1,780,3.63,4
1,480,3.71,4
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1,460,3.64,3
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0,520,3.15,3
0,620,3.09,4
0,540,3.2,1
1,660,3.47,3
0,500,3.23,4
1,560,2.65,3
0,500,3.95,4
0,580,3.06,2
0,520,3.35,3
0,500,3.03,3
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0,580,3.8,2
0,400,3.36,2
0,620,2.85,2
1,780,4,2
0,620,3.43,3
1,580,3.12,3
0,700,3.52,2
1,540,3.78,2
1,760,2.81,1
0,700,3.27,2
0,720,3.31,1
1,560,3.69,3
0,720,3.94,3
1,520,4,1
1,540,3.49,1
0,680,3.14,2
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0,460,2.93,3
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1,800,4,2
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1 admit gre gpa rank
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