This is a python implementation of a simple neural network using vanila numpy. The neural network contains three layers: an input layer, a hidden layer, and an output layer.The data used for the network comes from a bike sharing company. It contains bike sharing counts aggregated on hourly basis. Records: 17379 hours. This neural network can be used to predict the bike sharing counts in the future given relevant feature inputs.
You can check the data structure of each stage of data processing right from command line:
>>> from DataProcessor import DataProcessor as dp
>>> data_processor = dp('Bike-Sharing-Dataset/hour.csv')
>>> data_processor.show_data()
instant dteday season yr mnth hr holiday weekday workingday \
0 1 2011-01-01 1 0 1 0 0 6 0
1 2 2011-01-01 1 0 1 1 0 6 0
2 3 2011-01-01 1 0 1 2 0 6 0
3 4 2011-01-01 1 0 1 3 0 6 0
4 5 2011-01-01 1 0 1 4 0 6 0
weathersit temp atemp hum windspeed casual registered cnt
0 1 0.24 0.2879 0.81 0.0 3 13 16
1 1 0.22 0.2727 0.80 0.0 8 32 40
2 1 0.22 0.2727 0.80 0.0 5 27 32
3 1 0.24 0.2879 0.75 0.0 3 10 13
4 1 0.24 0.2879 0.75 0.0 0 1 1
>>> data_processor.virtualize()
>>> data_processor.show_data()
yr holiday temp hum windspeed casual registered cnt season_1 \
0 0 0 0.24 0.81 0.0 3 13 16 1
1 0 0 0.22 0.80 0.0 8 32 40 1
2 0 0 0.22 0.80 0.0 5 27 32 1
3 0 0 0.24 0.75 0.0 3 10 13 1
4 0 0 0.24 0.75 0.0 0 1 1 1
season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \
0 0 ... 0 0 0 0 0 0
1 0 ... 0 0 0 0 0 0
2 0 ... 0 0 0 0 0 0
3 0 ... 0 0 0 0 0 0
4 0 ... 0 0 0 0 0 0
weekday_3 weekday_4 weekday_5 weekday_6
0 0 0 0 1
1 0 0 0 1
2 0 0 0 1
3 0 0 0 1
4 0 0 0 1
[5 rows x 59 columns]
>>> data_processor.normalize()
>>> data_processor.show_data()
yr holiday temp hum windspeed casual registered cnt \
0 0 0 -1.334609 0.947345 -1.553844 -0.662736 -0.930162 -0.956312
1 0 0 -1.438475 0.895513 -1.553844 -0.561326 -0.804632 -0.823998
2 0 0 -1.438475 0.895513 -1.553844 -0.622172 -0.837666 -0.868103
3 0 0 -1.334609 0.636351 -1.553844 -0.662736 -0.949983 -0.972851
4 0 0 -1.334609 0.636351 -1.553844 -0.723582 -1.009445 -1.039008
season_1 season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 \
0 1 0 ... 0 0 0 0 0
1 1 0 ... 0 0 0 0 0
2 1 0 ... 0 0 0 0 0
3 1 0 ... 0 0 0 0 0
4 1 0 ... 0 0 0 0 0
weekday_2 weekday_3 weekday_4 weekday_5 weekday_6
0 0 0 0 0 1
1 0 0 0 0 1
2 0 0 0 0 1
3 0 0 0 0 1
4 0 0 0 0 1
[5 rows x 59 columns]
You can train the network by running:
python Train.py
But before that, you may want to edit some parameters for the network. This can be done by editing networkConfig.json :
{
"iterations": 10000,
"learning_rate": 0.1,
"hidden_nodes": 7,
"output_nodes": 1
}
You can see the losses (the lower, the better) of the trained network during training and how they change over time. After training, you will have:
- a figure output showing losses over iteration
After training, the network can be run to predict bike sharing counts:
python Run.py
