decent model trained

drive.py

@@ -44,7 +44,7 @@ def update(self, measurement):
 
 
 controller = SimplePIController(0.1, 0.002)
-set_speed = 9
+set_speed = 15
 controller.set_desired(set_speed)
 
 

model.py

@@ -0,0 +1,150 @@
+import csv
+import pickle
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from keras.models import Sequential, Model
+from keras.layers import Flatten, Dense, Lambda, Activation, Dropout, Cropping2D
+from keras.layers.convolutional import Conv2D
+from keras.layers.pooling import MaxPooling2D
+from sklearn.model_selection import train_test_split
+from sklearn.utils import shuffle
+
+
+def generator(samples, batch_size=100):
+	num_samples = len(samples)
+	# batch_size = int(batch_size / 4)
+	while 1:
+		for offset in range(0, num_samples, batch_size):
+			batch_samples = samples[offset:offset + batch_size]
+
+			images = []
+			angles = []
+			for batch_sample in batch_samples:
+				angle = float(batch_sample[3])
+				correction = 0.2
+
+				# CENTER
+				image = get_image(line[0])
+				images.append(image)
+				angles.append(angle)
+
+				# Augmenting images by flipping across y-axis
+				images.append(cv2.flip(image, 1))
+				angles.append(-angle)
+
+				# LEFT
+				images.append(get_image(line[1]))
+				angles.append(angle + correction)
+
+				# RIGHT
+				images.append(get_image(line[2]))
+				angles.append(angle - correction)
+
+			X_train = np.array(images)
+			y_train = np.array(angles)
+			yield shuffle(X_train, y_train)
+
+def read_data_from_file(data_path='data/'):
+	lines = []
+	with open(data_path + 'driving_log.csv') as csvfile:
+		reader = csv.reader(csvfile)
+		for line in reader:
+			lines.append(line)
+
+	images = []
+	angles = []
+	for line in lines:
+		angle = float(line[3])
+		correction = 0.2
+
+		# CENTER
+		image = get_image(line[0], data_path)
+		images.append(image)
+		angles.append(angle)
+
+		# Augmenting images by flipping across y-axis
+		images.append(cv2.flip(image, 1))
+		angles.append(-angle)
+
+		# LEFT
+		images.append(get_image(line[1], data_path))
+		angles.append(angle + correction)
+
+		# RIGHT
+		images.append(get_image(line[2], data_path))
+		angles.append(angle - correction)
+
+	X_train = np.array(images)
+	y_train = np.array(angles)
+
+	return (X_train, y_train)
+
+def store_in_pickle(data, filename='data.p'):
+	pickle.dump(data, open(filename, 'wb'))
+	print("Stored data in {}".format(filename))
+
+def get_image(path, base_path='../data/'):
+	# load image and conver to RGB
+	filename = path.split('/')[-1]
+	path = base_path + 'IMG/' + filename
+	image = cv2.imread(path)
+	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+	return image
+
+
+if __name__ == '__main__':
+	# lines = []
+	# with open('../data/driving_log.csv') as csvfile:
+	# 	reader = csv.reader(csvfile)
+	# 	for line in reader:
+	# 		lines.append(line)
+
+	# train_samples, validation_samples = train_test_split(lines, test_size=0.2, random_state=51)
+
+	# batch_size = 25
+	# train_generator = generator(train_samples, batch_size)
+	# validation_generator = generator(validation_samples, batch_size)
+
+	# load data 
+	X_train, y_train = read_data_from_file('../data/')
+
+	# Defining the model
+	model = Sequential()
+	model.add(Lambda(lambda x: x / 255.0 - 0.5 , input_shape=(160, 320, 3)))
+	model.add(Cropping2D(cropping=((70, 25), (0, 0))))
+	model.add(Conv2D(24, (5, 5), strides=2, activation='relu'))
+	model.add(Conv2D(36, (5, 5), strides=2, activation='relu'))
+	model.add(Conv2D(48, (5, 5), strides=2, activation='relu'))
+	model.add(Conv2D(64, (3, 3), activation='relu'))
+	model.add(Conv2D(64, (3, 3), activation='relu'))
+	model.add(Flatten())
+	model.add(Dense(1164))
+	model.add(Dropout(0.3))
+	model.add(Dense(100))
+	model.add(Dropout(0.3))
+	model.add(Dense(50))
+	model.add(Dense(10))
+	model.add(Dense(1))
+
+	model.compile(loss='mse', optimizer='adam')
+	history_object = model.fit(X_train, y_train, validation_split=0.2, shuffle=True, epochs=5, batch_size=100)
+	# history_object = model.fit_generator(train_generator,
+	# 									 steps_per_epoch=len(train_samples)/batch_size,
+	# 									 validation_data=validation_generator,
+	# 									 validation_steps=len(validation_samples)/batch_size,
+	# 									 epochs=5)
+
+	print(history_object.history.keys())
+
+	# Plot the training and validation loss for each epoch
+	plt.plot(history_object.history['loss'])
+	plt.plot(history_object.history['val_loss'])
+	plt.title('model mean squared error loss')
+	plt.ylabel('mean squared error loss')
+	plt.xlabel('epoch')
+	plt.legend(['training set', 'validation set'], loc='upper right')
+	plt.show()
+
+	# Save model for use by drive
+	model.save('model.h5')