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Implementing gradient descent
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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 | |
0 | 740 | 3.34 | 4 | |
0 | 580 | 3.02 | 2 | |
0 | 460 | 2.87 | 2 | |
0 | 640 | 3.38 | 3 | |
1 | 600 | 3.56 | 2 | |
1 | 660 | 2.91 | 3 | |
0 | 340 | 2.9 | 1 | |
1 | 460 | 3.64 | 1 | |
0 | 460 | 2.98 | 1 | |
1 | 560 | 3.59 | 2 | |
0 | 540 | 3.28 | 3 | |
0 | 680 | 3.99 | 3 | |
1 | 480 | 3.02 | 1 | |
0 | 800 | 3.47 | 3 | |
0 | 800 | 2.9 | 2 | |
1 | 720 | 3.5 | 3 | |
0 | 620 | 3.58 | 2 | |
0 | 540 | 3.02 | 4 | |
0 | 480 | 3.43 | 2 | |
1 | 720 | 3.42 | 2 | |
0 | 580 | 3.29 | 4 | |
0 | 600 | 3.28 | 3 | |
0 | 380 | 3.38 | 2 | |
0 | 420 | 2.67 | 3 | |
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 | |
0 | 620 | 3.22 | 2 | |
0 | 540 | 3.04 | 1 | |
0 | 800 | 3.91 | 3 | |
0 | 680 | 3.34 | 2 | |
0 | 440 | 3.17 | 2 | |
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 | |
0 | 660 | 3.45 | 4 | |
0 | 520 | 3.1 | 4 | |
1 | 440 | 3.39 | 2 | |
0 | 600 | 3.31 | 4 | |
1 | 800 | 3.22 | 1 | |
1 | 660 | 3.7 | 4 | |
0 | 800 | 3.15 | 4 | |
0 | 420 | 2.26 | 4 | |
1 | 620 | 3.45 | 2 | |
0 | 800 | 2.78 | 2 | |
0 | 680 | 3.7 | 2 | |
0 | 800 | 3.97 | 1 | |
0 | 480 | 2.55 | 1 | |
0 | 520 | 3.25 | 3 | |
0 | 560 | 3.16 | 1 | |
0 | 460 | 3.07 | 2 | |
0 | 540 | 3.5 | 2 | |
0 | 720 | 3.4 | 3 | |
0 | 640 | 3.3 | 2 | |
1 | 660 | 3.6 | 3 | |
1 | 400 | 3.15 | 2 | |
1 | 680 | 3.98 | 2 | |
0 | 220 | 2.83 | 3 | |
0 | 580 | 3.46 | 4 | |
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 | |
0 | 540 | 3.28 | 1 | |
0 | 460 | 3.14 | 3 | |
0 | 460 | 3.58 | 2 | |
0 | 500 | 3.01 | 4 | |
0 | 420 | 2.69 | 2 | |
0 | 520 | 2.7 | 3 | |
0 | 680 | 3.9 | 1 | |
0 | 680 | 3.31 | 2 | |
1 | 560 | 3.48 | 2 | |
0 | 580 | 3.34 | 2 | |
0 | 500 | 2.93 | 4 | |
0 | 740 | 4 | 3 | |
0 | 660 | 3.59 | 3 | |
0 | 420 | 2.96 | 1 | |
0 | 560 | 3.43 | 3 | |
1 | 460 | 3.64 | 3 | |
1 | 620 | 3.71 | 1 | |
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 | |
0 | 600 | 3.35 | 2 | |
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 | |
0 | 460 | 3.44 | 2 | |
1 | 560 | 3.36 | 1 | |
0 | 480 | 2.78 | 3 | |
0 | 460 | 2.93 | 3 | |
0 | 620 | 3.63 | 3 | |
0 | 580 | 4 | 1 | |
0 | 800 | 3.89 | 2 | |
1 | 540 | 3.77 | 2 | |
1 | 680 | 3.76 | 3 | |
1 | 680 | 2.42 | 1 | |
1 | 620 | 3.37 | 1 | |
0 | 560 | 3.78 | 2 | |
0 | 560 | 3.49 | 4 | |
0 | 620 | 3.63 | 2 | |
1 | 800 | 4 | 2 | |
0 | 640 | 3.12 | 3 | |
0 | 540 | 2.7 | 2 | |
0 | 700 | 3.65 | 2 | |
1 | 540 | 3.49 | 2 | |
0 | 540 | 3.51 | 2 | |
0 | 660 | 4 | 1 | |
1 | 480 | 2.62 | 2 | |
0 | 420 | 3.02 | 1 | |
1 | 740 | 3.86 | 2 | |
0 | 580 | 3.36 | 2 | |
0 | 640 | 3.17 | 2 | |
0 | 640 | 3.51 | 2 | |
1 | 800 | 3.05 | 2 | |
1 | 660 | 3.88 | 2 | |
1 | 600 | 3.38 | 3 | |
1 | 620 | 3.75 | 2 | |
1 | 460 | 3.99 | 3 | |
0 | 620 | 4 | 2 | |
0 | 560 | 3.04 | 3 | |
0 | 460 | 2.63 | 2 | |
0 | 700 | 3.65 | 2 | |
0 | 600 | 3.89 | 3 |
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import numpy as np | |
import pandas as pd | |
admissions = pd.read_csv('binary.csv') | |
# Make dummy variables for rank | |
data = pd.concat([admissions, pd.get_dummies(admissions['rank'], prefix='rank')], axis=1) | |
data = data.drop('rank', axis=1) | |
# Standarize features | |
for field in ['gre', 'gpa']: | |
mean, std = data[field].mean(), data[field].std() | |
data.loc[:,field] = (data[field]-mean)/std | |
# Split off random 10% of the data for testing | |
np.random.seed(42) | |
sample = np.random.choice(data.index, size=int(len(data)*0.9), replace=False) | |
data, test_data = data.ix[sample], data.drop(sample) | |
# Split into features and targets | |
features, targets = data.drop('admit', axis=1), data['admit'] | |
features_test, targets_test = test_data.drop('admit', axis=1), test_data['admit'] |
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import numpy as np | |
from data_prep import features, targets, features_test, targets_test | |
def sigmoid(x): | |
""" | |
Calculate sigmoid | |
""" | |
return 1 / (1 + np.exp(-x)) | |
# TODO: We haven't provided the sigmoid_prime function like we did in | |
# the previous lesson to encourage you to come up with a more | |
# efficient solution. If you need a hint, check out the comments | |
# in solution.py from the previous lecture. | |
# 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) | |
# Neural Network hyperparameters | |
epochs = 1000 | |
learnrate = 0.5 | |
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 | |
# Note: We haven't included the h variable from the previous | |
# lesson. You can add it if you want, or you can calculate | |
# the h together with the output | |
# TODO: Calculate the output | |
output = None | |
# TODO: Calculate the error | |
error = None | |
# TODO: Calculate the error term | |
error_term = None | |
# TODO: Calculate the change in weights for this sample | |
# and add it to the total weight change | |
del_w += 0 | |
# TODO: Update weights using the learning rate and the average change in weights | |
weights += 0 | |
# 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) | |
if last_loss and last_loss < loss: | |
print("Train loss: ", loss, " WARNING - Loss Increasing") | |
else: | |
print("Train loss: ", loss) | |
last_loss = loss | |
# Calculate accuracy on test data | |
tes_out = sigmoid(np.dot(features_test, weights)) | |
predictions = tes_out > 0.5 | |
accuracy = np.mean(predictions == targets_test) | |
print("Prediction accuracy: {:.3f}".format(accuracy)) |
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import numpy as np | |
from data_prep import features, targets, features_test, targets_test | |
def sigmoid(x): | |
""" | |
Calculate sigmoid | |
""" | |
return 1 / (1 + np.exp(-x)) | |
# TODO: We haven't provided the sigmoid_prime function like we did in | |
# the previous lesson to encourage you to come up with a more | |
# efficient solution. If you need a hint, check out the comments | |
# in solution.py from the previous lecture. | |
# 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) | |
# Neural Network hyperparameters | |
epochs = 1000 | |
learnrate = 0.5 | |
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 = y - output | |
# The error term | |
# Notice we calulate f'(h) here instead of defining a separate | |
# sigmoid_prime function. This just makes it faster because we | |
# can re-use the result of the sigmoid function stored in | |
# the output variable | |
error_term = error * output * (1 - 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) | |
if last_loss and last_loss < loss: | |
print("Train loss: ", loss, " WARNING - Loss Increasing") | |
else: | |
print("Train loss: ", loss) | |
last_loss = loss | |
# Calculate accuracy on test data | |
tes_out = sigmoid(np.dot(features_test, weights)) | |
predictions = tes_out > 0.5 | |
accuracy = np.mean(predictions == targets_test) | |
print("Prediction accuracy: {:.3f}".format(accuracy)) |
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