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Why Follow Premiership Northern Ireland?
The Premiership Northern Ireland is not just a football league; it's a community. It brings together fans from diverse backgrounds, united by their love for the game. Here's why it deserves your attention:
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Betting Strategies
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Famous Players to Watch
The Premiership Northern Ireland has been home to some incredible talent. Here are a few players who have made significant impacts:
- Liam Boyce: Known for his striking prowess and goal-scoring ability.
- Craig Cathcart: A versatile defender with leadership qualities.
- Kane Hemmings: A promising young midfielder with exceptional skills.
- Niall McGinn: Renowned for his speed and attacking flair on the wings.
The Role of Technology in Football
In today's digital age, technology plays a crucial role in enhancing the football experience. From VAR (Video Assistant Referee) systems ensuring fair play to advanced analytics used by teams to strategize, technology is transforming the game. Here's how it impacts the Premiership Northern Ireland:
- VAR Implementation: Ensures accurate decision-making during matches.
- Data Analytics: Teams use data to improve performance and tactics.
- Social Media Engagement:: Teams and players connect with fans globally through digital platforms.
- Betting Platforms:: Technology enables secure and convenient online betting experiences.
Cultural Impact of Football in Northern Ireland
SarangMahajan/SDC-ND-P3<|file_sep|>/model.py
from keras.models import Sequential
from keras.layers import Flatten,Dense,Lambda,Cropping2D
from keras.layers.convolutional import Convolution2D
def nvidia_model():
# Model adapted from NVIDIA self driving car paper https://arxiv.org/pdf/1604.07316v1.pdf
# Normalizing data using lambda layer
# Cropping layer used for removing top part (sky) and bottom part (dashboards) of image
# Convolutional layers used for feature extraction
# Dense layers used for prediction
# Dropout layers added at end to avoid overfitting
# Relu activation used throughout
# Softmax activation used at last layer as we have multiple classes
# Adam optimizer used
# Nvidia model architecture
# model = Sequential()
# model.add(Lambda(lambda x: x/127.5 -1.,input_shape=(160,320,3)))
# model.add(Cropping2D(cropping=((70,25),(0,0))))
# model.add(Convolution2D(24,(5,5),strides=(2,2),activation='relu'))
# model.add(Convolution2D(36,(5,5),strides=(2,2),activation='relu'))
# model.add(Convolution2D(48,(5,5),strides=(2,2),activation='relu'))
# model.add(Convolution2D(64,(3,3),activation='relu'))
# model.add(Convolution2D(64,(3,3),activation='relu'))
# model.add(Flatten())
# model.add(Dense(100))
# model.add(Dense(50))
# model.add(Dense(10))
# model.add(Dense(1))
# adam = Adam(lr=1e-4)
# model.compile(loss='mse',optimizer=adam)
# return model
def nvidia_model_dropout():
#Nvidia Model Architecture with Dropout layers added at end
#Adam optimizer with low learning rate
#Relu activation function used throughout
#Softmax activation function used at last layer as we have multiple classes
#Data normalized using Lambda layer
#Cropping layer used for removing top part (sky) and bottom part (dashboards) of image
#Convolutional layers used for feature extraction
#Dense layers used for prediction
model = Sequential()
model.add(Lambda(lambda x: x/127.5 -1.,input_shape=(160,320,3)))
model.add(Cropping2D(cropping=((70,25),(0,0))))
model.add(Convolution2D(24,(5,5),strides=(2,2),activation='relu'))
model.add(Convolution2D(36,(5,5),strides=(2,2),activation='relu'))
model.add(Convolution2D(48,(5,5),strides=(2,2),activation='relu'))
model.add(Convolution2D(64,(3,3),activation='relu'))
model.add(Convolution2D(64,(3,3),activation='relu'))
model.add(Flatten())
model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(10))
model.add(Dropout(.6))
model.add(Dense(1))
adam = Adam(lr=1e-4)
model.compile(loss='mse',optimizer=adam)
return model<|repo_name|>SarangMahajan/SDC-ND-P3<|file_sep|>/README.md
**Behavioral Cloning**
The goals / steps of this project are the following:
* Use the simulator to collect data of good driving behavior
* Build a convolution neural network in Keras that predicts steering angles from images
* Train and validate the model with a training and validation set
* Test that the model successfully drives around track one without leaving the road
* Summarize the results with a written report
[//]: # (Image References)
[image1]: ./images/original_data.jpg "Original Data"
[image4]: ./images/cropped_data.jpg "Cropped Data"
[image6]: ./images/flipped_data.jpg "Flipped Data"
[image7]: ./images/center_left_right.jpg "Center Left Right"
[image8]: ./images/recovery.jpg "Recovery"
[image9]: ./images/lane_center.jpg "Lane Center"
[image10]: ./images/lane_recover.jpg "Lane Recover"
[image11]: ./images/lane_undistort.jpg "Lane Undistort"
## Rubric Points
### Here I will consider the [rubric points](https://review.udacity.com/#!/rubrics/432/view) individually and describe how I addressed each point in my implementation.
---
### Files Submitted & Code Quality
####1. Submission includes all required files and can be used to run the simulator in autonomous mode
My project includes the following files:
* [model.py](https://github.com/SarangMahajan/SDC-ND-P3/blob/master/model.py): The script containing code for training our convolution neural network.
* [drive.py](https://github.com/SarangMahajan/SDC-ND-P3/blob/master/drive.py): The script that uses our trained convolution neural network to drive autonomously around track one.
* [model.h5](https://github.com/SarangMahajan/SDC-ND-P3/blob/master/model.h5): The trained convolution neural network.
* [writeup_report.md](https://github.com/SarangMahajan/SDC-ND-P3/blob/master/writeup_report.md): This report describes my project.
#### How I trained my convolution neural network
##### Data Preprocessing
I first loaded all images from center,left,right cameras along with steering angles into numpy arrays using csv module.
![alt text][image7]
Then I did some preprocessing before feeding them into my network.
###### Cropping
Firstly I cropped top part (sky) and bottom part (dashboards) from images using Cropping Layer so that my network focuses only on road.
![alt text][image4]
###### Normalization
I normalized data using Lambda layer which also helped in converging faster.
##### Data Augmentation
I augmented my data by flipping each image horizontally so that my network learns both left turn as well right turn.
![alt text][image6]
I also adjusted steering angles by +/- .15 when using left/right camera images so that network learns how much correction is needed when deviating from center.
I also collected recovery data which was useful when vehicle went off track.
![alt text][image8]
##### Model Architecture
My final model consisted of a convolution neural network based off Nvidia architecture given in https://arxiv.org/pdf/1604.07316v1.pdf modified as follows :
I normalized data using lambda layer before feeding it into my network.
I cropped top part (sky) & bottom part(dashboards) from images using Cropping Layer so that my network focuses only on road.
I added Dropout Layers at end before prediction layer so that I could avoid overfitting.
![alt text][image9]
The final fully connected layers looked like this:
| Layer | Description |
|:---------------------:|:---------------------------------------------:|
| Input | Preprocessed Image |
| Convolution | filters:24 kernal_size:(5x5) strides:(1x1) activation:'relu' |
| Convolution | filters:36 kernal_size:(5x5) strides:(1x1) activation:'relu' |
| Convolution | filters:48 kernal_size:(5x5) strides:(1x1) activation:'relu' |
| Convolution | filters:64 kernal_size:(3x3) strides:(1x1) activation:'relu' |
| Convolution | filters:64 kernal_size:(3x3) strides:(1x1) activation:'relu' |
| Flatten | Output Shape:(115200,) |
| Dense | units:100 |
| Dense | units:50 |
| Dense | units:10 |
| Dropout | drop_out_prob:.6 |
| Dense | units:1 |
#### Training Strategy
To train the final model I did two epochs of training.
I saved weights after each epoch so that I could visualize how loss was changing after each epoch.
I got validation loss around .01 which was very low & also checked if my car was able drive around track one without leaving road which showed me that I had good fit.
#### How I ran my simulator in autonomous mode
At first I was getting error while running drive.py file because I was missing OpenCV package so I installed it using sudo apt-get install python-opencv command.
Then I ran drive.py file using command python drive.py path_to_model.h5 & it worked perfectly fine.
###Model Architecture & Training Strategy
#### 1. Solution Design Approach
The overall strategy for deriving a architecture was to keep it simple initially & then keep adding complexity only if required.
In order to gauge how well my algorithm was working,I split my image & steering angle data into a training set & validation set.
I found that my first architecture had low mean squared error but vehicle tended to go off track after certain time period which indicated overfitting.
So I added dropout layers before prediction layer & got better results which was confirmed by lower mean squared error & vehicle being able drive around track one without going off road.
At each iteration I plotted mean squared error vs epoch number so that I could monitor progress.
My first step was to use an existing LeNet architecture as a starting point because it was very simple & fast.
In order to gauge how well the architecture was working,I split my image & steering angle data into a training set & validation set .
I found out that LeNet gave me very high mean squared error which indicated underfitting.So then I decided not use LeNet architecture because it was not able learn enough features from images .
Then instead of going with complex architectures,I went with Nvidia architecture which is very simple yet gives good results.
But when I tried running Nvidia architecture ,my car kept going off track after certain time period which indicated overfitting.
So then instead of adding more complexity,I added dropout layers before prediction layer which helped me get better results .
At the end of training process,I had a solid mean squared error value close enough around .01 which showed me that there was no overfitting or underfitting .
#### 2. Final Model Architecture
The final architecture consists of cropping layer ,normalization layer ,convolutional layers ,dense layers ,dropout layers & prediction layer .
Here is a visualization of the architecture (note: visualizing the architecture is optional according to the project rubric)
![alt text][image10]
####3. Creation of Training Set & Training Process
To capture good driving behavior,I first recorded two laps on track one using center lane driving style.Then after some trial & error,I also recorded recovery data where vehicle drove close to edge so that later it could learn how much correction is needed when going off track .
To augment data ,I flipped each image horizontally so that now it could learn both left turn as well right turn .
After flipping,I adjusted steering angles by +/- .15 when using left/right camera images so now it would know how much correction is needed when deviating from center .
For example if original steering angle is .05 then left camera image would have adjusted angle .20 whereas right camera image would have adjusted angle -.10 .
Here is an example image showing original & augmented data :
![alt text][image11]
To augment data even further,I decided to randomly choose images instead taking all images at once .
This helped me get better generalization .
Then I finally preprocessed all these images by cropping top part (sky) & bottom part(dashboards) from images using Cropping Layer so now my network focuses only on road .
I also normalized all these preprocessed images using Lambda Layer which helped me converge faster .
After collecting all this data ,I finally shuffled them randomly so now there is no correlation between consecutive samples .
Finally,I split these shuffled samples into training set & validation set .
Then I finally ran everything through training process using Adam optimizer with low learning rate because it gives good results .
I chose low learning rate because otherwise there will be too much oscillation around minima .
At each iteration ,I plotted mean squared error vs epoch number so now I could monitor progress .
After finishing all these steps ,my vehicle could now drive autonomously around track one without leaving road .
<|file_sep|># **Behavioral Cloning**
## Writeup Report
---
**Behavioral Cloning Project**
The goals / steps of this project are the following:
* Use the simulator to collect data of good driving behavior
* Build a convolution neural network in Keras that predicts steering angles from images
* Train and validate the model with a training and validation set
* Test that the model successfully