README.md

Behavioral Cloning

Rubric Points

---

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 containing the script to create and train the model
• drive.py for driving the car in autonomous mode
• model.h5 containing a trained convolution neural network
• writeup_report.md or writeup_report.pdf summarizing the results
• and a video

2. Submission includes functional code

Using the Udacity provided simulator and my drive.py file, the car can be driven autonomously around the track by executing

python ./drive.py 2017_07_17_18_43.model

3. Submission code is usable and readable

The GeneratorBehavioralCloning.ipynb file contains the code for training and saving the convolution neural network. The file shows the pipeline I used for training and validating the model, and since the code was covered in detail in the class, it contains only a few comments to explain how the code works.

Model Architecture and Training Strategy

1. An appropriate model architecture has been employed

My model is the nvidia neural network model mentioned in class and consists of 5 convolution layers followed by 3 dense layers

# nvidia model
#model.add(Convolution2D(24, 5, 5, subsample=(2,2)))
model.add(Convolution2D(24, 5, 5))
model.add(MaxPooling2D())
model.add(Activation('relu'))

#model.add(Convolution2D(36, 5, 5, subsample=(2,2)))
model.add(Convolution2D(36, 5, 5))
model.add(MaxPooling2D())
model.add(Activation('relu'))

model.add(Convolution2D(48, 5, 5, subsample=(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(Dropout(.2))

model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(10))

##
model.add(Dense(1))

The model includes RELU layers to introduce nonlinearity, and the data is normalized in the model using a Keras lambda layer:

model = Sequential()
model.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=XtrainInputShape))

2. Attempts to reduce overfitting in the model

Before being replaced by the nvidia model, the LeNet model was used and it contained dropout layers in order to reduce overfitting:

# add lenet
model.add(Convolution2D(10, 5, 5, input_shape=XtrainInputShape, activation="relu"))
model.add(MaxPooling2D())
model.add(Convolution2D(6, 5, 5, activation="relu"))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dropout(.25))
model.add(Dense(120))
model.add(Dropout(.25))
model.add(Dense(84))

I was repimanded for not including regularization in the nvidia model, so it now also includes both MaxPooling2D and Dropout.

Using the validation_split parameter, the model was trained on 80% of the data and validated on 20% of the data to reduce overfitting:

adamOptimizer=keras.optimizers.Adam(lr=0.0001)
model.compile(optimizer=adamOptimizer, loss='mse', metrics=['accuracy'])
model.fit(X_train, y_train, validation_split=0.2, shuffle=True, nb_epoch=4)

The model was very sensitive to overfitting and reducing the number of epochs seemed to be the primary way to stop the training when the model started overfitting.

The model was tested by running it through the simulator and ensuring that the vehicle could stay on the track.

3. Model parameter tuning

The model used an adam optimizer and used transfer learning. In the several training passes, the learning rate was initially set to 0.001 using the lr (LR) parameter and reduced to 0.0001 in later passes. The same number of epochs was used in each pass: 5.

4. Appropriate training data

Training data was chosen to keep the vehicle driving on the road. I used a combination of center lane driving in both directions around the entire track. The training images set had a high bias for zero steering angle. Rather than remove the bias, I used transfer learning. I trained on the biased data and then used a smaller image set of center driving in both directions from just past the bridge.

For details about how I created the training data, see the next section.

Model Architecture and Training Strategy

1. Solution Design Approach

The overall strategy for deriving a model architecture was to follow the class lessons.

My first step was to use a LeNet convolution neural network model.

X_train.shape: (32511, 160, 320, 3) y_train.shape: (32511,)
X_train[0].shape: (160, 320, 3)

model.add(Convolution2D(10, 5, 5, input_shape=XtrainInputShape, activation="relu"))
model.add(MaxPooling2D())
model.add(Convolution2D(6, 5, 5, activation="relu"))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dropout(.25))
model.add(Dense(120))
model.add(Dropout(.25))
model.add(Dense(84))

In order to gauge how well the model was working, I split my image and steering angle data into a training and validation set.

XtrainInputShape: (160, 320, 3)
Train on 26008 samples, validate on 6503 samples
Epoch 1/10
26008/26008 [==============================] - 122s - loss: 0.0447 - acc: 0.2373 - val_loss: 0.0262 - val_acc: 0.2488
Epoch 2/10
26008/26008 [==============================] - 118s - loss: 0.0168 - acc: 0.2387 - val_loss: 0.0257 - val_acc: 0.2488
Epoch 3/10
26008/26008 [==============================] - 118s - loss: 0.0149 - acc: 0.2387 - val_loss: 0.0266 - val_acc: 0.2488
Epoch 4/10
26008/26008 [==============================] - 119s - loss: 0.0138 - acc: 0.2388 - val_loss: 0.0281 - val_acc: 0.2488
Epoch 5/10
26008/26008 [==============================] - 119s - loss: 0.0134 - acc: 0.2388 - val_loss: 0.0272 - val_acc: 0.2488
Epoch 6/10
26008/26008 [==============================] - 119s - loss: 0.0132 - acc: 0.2390 - val_loss: 0.0278 - val_acc: 0.2485
Epoch 7/10
26008/26008 [==============================] - 119s - loss: 0.0129 - acc: 0.2389 - val_loss: 0.0276 - val_acc: 0.2487
Epoch 8/10
26008/26008 [==============================] - 119s - loss: 0.0126 - acc: 0.2390 - val_loss: 0.0250 - val_acc: 0.2488
Epoch 9/10
26008/26008 [==============================] - 119s - loss: 0.0124 - acc: 0.2390 - val_loss: 0.0299 - val_acc: 0.2483
Epoch 10/10
26008/26008 [==============================] - 119s - loss: 0.0117 - acc: 0.2390 - val_loss: 0.0269 - val_acc: 0.2488
saving model as: 2017_07_10_20_52.model

I found that my first model had a decreasing mean squared error on the training set but a constant or increasing squared error on the validation set. This implied that the model was overfitting. In addition, the car decide to hump the right hand side of the bridge as it went across.

To combat the overfitting, I modified the model to reduce the number of epochs.

XtrainInputShape: (160, 320, 3)
Train on 26008 samples, validate on 6503 samples
Epoch 1/3
26008/26008 [==============================] - 120s - loss: 0.1064 - acc: 0.2365 - val_loss: 0.0261 - val_acc: 0.2488
Epoch 2/3
26008/26008 [==============================] - 119s - loss: 0.0177 - acc: 0.2387 - val_loss: 0.0265 - val_acc: 0.2488
Epoch 3/3
26008/26008 [==============================] - 119s - loss: 0.0157 - acc: 0.2387 - val_loss: 0.0258 - val_acc: 0.2488
saving model as: 2017_07_10_21_11.model

This time the car humped the left hand side of the bridge. To increase the number of image samples, I followed the class lesson and both flipped the images and added the left and right camera with a steering correction:

STEERINGMEASUEMENT=3
CAMERAS=3
STERRINGADJUSTMENT=[0, .25, -.25]
...
for line in lines:
    for camera in range(CAMERAS):
    ...
    steeringMeasurements.append(float(line[STEERINGMEASUEMENT])+STERRINGADJUSTMENT[camera])
    
    images.append(cv2.flip(image,1))
    steeringMeasurements.append(steering*-1)
    

XtrainInputShape: (160, 320, 3)
Train on 26008 samples, validate on 6503 samples
Epoch 1/5
26008/26008 [==============================] - 121s - loss: 0.6141 - acc: 0.2304 - val_loss: 0.0276 - val_acc: 0.2483
Epoch 2/5
26008/26008 [==============================] - 118s - loss: 0.0234 - acc: 0.2364 - val_loss: 0.0248 - val_acc: 0.2483
Epoch 3/5
26008/26008 [==============================] - 118s - loss: 0.0205 - acc: 0.2365 - val_loss: 0.0247 - val_acc: 0.2483
Epoch 4/5
26008/26008 [==============================] - 119s - loss: 0.0192 - acc: 0.2365 - val_loss: 0.0246 - val_acc: 0.2483
Epoch 5/5
26008/26008 [==============================] - 119s - loss: 0.0174 - acc: 0.2365 - val_loss: 0.0259 - val_acc: 0.2483
saving model as: 2017_07_10_21_32.model

Continuing to follow the class lesson I cropped the images in the pipeline:

IMAGECROP=((50,20), (0,0))
model.add(Cropping2D(cropping=IMAGECROP))

After inspecting random images, I did not crop the images as much as much as the lessons suggested.

XtrainInputShape: (160, 320, 3)
Train on 26008 samples, validate on 6503 samples
Epoch 1/6
26008/26008 [==============================] - 61s - loss: 0.0331 - acc: 0.2360 - val_loss: 0.0552 - val_acc: 0.2483
Epoch 2/6
26008/26008 [==============================] - 61s - loss: 0.0269 - acc: 0.2364 - val_loss: 0.0553 - val_acc: 0.2483
Epoch 3/6
26008/26008 [==============================] - 61s - loss: 0.0254 - acc: 0.2363 - val_loss: 0.0585 - val_acc: 0.2448: 0.0255 - acc:  - ETA: 1s - los
Epoch 4/6
26008/26008 [==============================] - 61s - loss: 0.0241 - acc: 0.2365 - val_loss: 0.0445 - val_acc: 0.2483
Epoch 5/6
26008/26008 [==============================] - 60s - loss: 0.0233 - acc: 0.2365 - val_loss: 0.0548 - val_acc: 0.2477
Epoch 6/6
26008/26008 [==============================] - 61s - loss: 0.0227 - acc: 0.2366 - val_loss: 0.0529 - val_acc: 0.2474
saving model as: 2017_07_10_22_20.model

this made the situation even worse, the car didn't even make it to the bridge. I then followed the next class lesson and switched to the nvidia model:

# nvidia model
#model.add(Convolution2D(24, 5, 5, subsample=(2,2)))
model.add(Convolution2D(24, 5, 5))
model.add(MaxPooling2D())
model.add(Activation('relu'))

#model.add(Convolution2D(36, 5, 5, subsample=(2,2)))
model.add(Convolution2D(36, 5, 5))
model.add(MaxPooling2D())
model.add(Activation('relu'))

model.add(Convolution2D(48, 5, 5, subsample=(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(Dropout(.2))

model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(10))

##
model.add(Dense(1))

Epoch 1/5
1728/1700 [==============================] - 9s - loss: 0.0446 - val_loss: 0.0316
Epoch 2/5
1728/1700 [==============================] - 9s - loss: 0.0306 - val_loss: 0.0326
Epoch 3/5
1728/1700 [==============================] - 9s - loss: 0.0316 - val_loss: 0.0358
Epoch 4/5
1728/1700 [==============================] - 9s - loss: 0.0263 - val_loss: 0.0264
Epoch 5/5
1728/1700 [==============================] - 9s - loss: 0.0359 - val_loss: 0.0340
        
saving model as: 2017_07_17_18_43.model

2. Final Model Architecture

The final model architecture was the nvidia model presented in class which consisted of preprocessing the images with normalization and cropping, and a pipeline that consisted of 5 convolution neural networks followed by 3 dense layers feeding a final dense regression layer.

# nvidia model
#model.add(Convolution2D(24, 5, 5, subsample=(2,2)))
model.add(Convolution2D(24, 5, 5))
model.add(MaxPooling2D())
model.add(Activation('relu'))

#model.add(Convolution2D(36, 5, 5, subsample=(2,2)))
model.add(Convolution2D(36, 5, 5))
model.add(MaxPooling2D())
model.add(Activation('relu'))

model.add(Convolution2D(48, 5, 5, subsample=(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(Dropout(.2))

model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(10))

##
model.add(Dense(1))

Here is a visualization of the architecture (note: visualizing the architecture is optional according to the project rubric)

This tensorboard visualization was obtained by adding the tenosrboard callback in keras:

tensorboard=keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0,  write_graph=True, write_images=True)

history_object=model.fit_generator(train_generator, samples_per_epoch=len(train_samples), validation_data=validation_generator, nb_val_samples=len(validation_samples), nb_epoch=5, callbacks=[stopOnValLossOf_020, tensorboard])

The keras visualization did not seem useful to me.

3. Creation of the Training Set & Training Process

To capture good driving behavior, I first recorded one lap in each direction on track one using center lane driving. Here is an example image of center lane driving:

I then recorded the vehicle recovering from the left side and right sides of the road back to center so that the vehicle would learn to recover when it veered from the center of the road. These images show what a recovery looks like:

Since I initially drove the car in both training directions (clockwise and counter clockwise), I was memory constrained and could not flip the images. The grader strongly suggested that I add a generator for the image. I did and then was able to flip the images, as well.

The full track image set had a high bias for a zero steering angle. Generally, the zero bias caused the car to drive off the bridge and straight onto the dirt path just past the bridge. When 2 learning rates were used (0.001 followed by 0.0001), the car stayed on the track, but tended to veer, especially close to the lane markers.

I collected additional training data from the middle of the bridge to just past the dirt path (in both directions). Then I used transfer learning to tune the model to the additional images. The car was better behaved.

3. Video of the Final Model Driving Car On Track 1 (click to play)

using the highly bias zero steering data

using transfer learning