import numpy as np
from math import ceil
from sklearn.model_selection import train_test_split
from commonFunctions_v12 import get_lines_logfile
from commonFunctions_v12 import generator
from commonFunctions_v12 import batch_len
from commonFunctions_v12 import print_info
from commonFunctions_v12 import visualize_loss_history
from commonFunctions_v12 import get_log_pathSampleData
path_last_hard_turn_data = "./simulationData/002_hardLeftTurn20201208_0220/"
# Reading CSV file FROM SAMPLE DATA, extracting lines.
samples = get_lines_logfile(get_log_pathSampleData())
# Reading CSV file from Last Hard Turn, extracting lines
# add them to samples lines.
samples.extend(get_lines_logfile(path_last_hard_turn_data))
train_samples, validation_samples = train_test_split(samples, test_size=0.2)
# Set our batch size (*3 due to image center + left + right ....), then *2 due to flip of each images
batch_size=batch_len*3*2 #6*3*2 = 36 ....
# compile and train the model using the generator function
train_generator = generator(train_samples, batch_size=batch_size)
validation_generator = generator(validation_samples, batch_size=batch_size)
print_info('Import Keras Models. Please wait ...')
from keras.models import Sequential
from commonFunctions_v12 import get_lines_logfile from commonFunctions_v12 import generator from commonFunctions_v12 import batch_len from commonFunctions_v12 import print_info from commonFunctions_v12 import visualize_loss_history from commonFunctions_v12 import get_log_pathSampleData path_last_hard_turn_data = "./simulationData/002_hardLeftTurn20201208_0220/" path_003_OwnRecordingOneLapAntiClockwise = "./simulationData/003_OwnRecordingOneLapAntiClockwise/" path_004_ownRecordOneLapClockwise = "./simulationData/004_ownRecordOneLapClockwise/" path_005_ownRecordOneLapAntiClockwise = "./simulationData/005_ownRecordOneLapAntiClockwise/" path_006_OwnRecord2LapsRecoverSidesAntiClockwise = "./simulationData/006_OwnRecord2LapsRecoverSidesAntiClockwise/" # Reading CSV file FROM SAMPLE DATA, extracting lines. samples = get_lines_logfile(get_log_pathSampleData()) # Reading CSV file from Last Hard Turn, extracting lines # add them to samples lines. #samples.extend(get_lines_logfile(path_last_hard_turn_data)) # Reading CSV file from 003_OwnRecordingOneLapAntiClockwise, extracting lines # add them to samples lines. #samples.extend(get_lines_logfile(path_003_OwnRecordingOneLapAntiClockwise)) # Reading CSV file from 004_ownRecordOneLapClockwise, extracting lines # add them to samples lines. #samples.extend(get_lines_logfile(path_004_ownRecordOneLapClockwise)) #samples.extend(get_lines_logfile(path_005_ownRecordOneLapAntiClockwise)) samples.extend(get_lines_logfile(path_006_OwnRecord2LapsRecoverSidesAntiClockwise)) train_samples, validation_samples = train_test_split(samples, test_size=0.2) # Set our batch size (*3 due to image center + left + right ....), then *2 due to flip of each images
from sklearn.model_selection import train_test_split from commonFunctions_v12 import get_lines_logfile from commonFunctions_v12 import generator from commonFunctions_v12 import batch_len from commonFunctions_v12 import print_info from commonFunctions_v12 import visualize_loss_history from commonFunctions_v12 import get_log_pathSampleData path_last_hard_turn_data = "./simulationData/002_hardLeftTurn20201208_0220/" path_003_OwnRecordingOneLapAntiClockwise = "./simulationData/003_OwnRecordingOneLapAntiClockwise/" path_004_ownRecordOneLapClockwise = "./simulationData/004_ownRecordOneLapClockwise/" path_005_ownRecordOneLapClockwise = "./simulationData/005_ownRecordOneLapAntiClockwise/" # Reading CSV file FROM SAMPLE DATA, extracting lines. samples = get_lines_logfile(get_log_pathSampleData()) # Reading CSV file from Last Hard Turn, extracting lines # add them to samples lines. samples.extend(get_lines_logfile(path_last_hard_turn_data)) # Reading CSV file from 003_OwnRecordingOneLapAntiClockwise, extracting lines # add them to samples lines. samples.extend(get_lines_logfile(path_003_OwnRecordingOneLapAntiClockwise)) # Reading CSV file from 004_ownRecordOneLapClockwise, extracting lines # add them to samples lines. samples.extend(get_lines_logfile(path_004_ownRecordOneLapClockwise)) samples.extend(get_lines_logfile(path_005_ownRecordOneLapClockwise)) train_samples, validation_samples = train_test_split(samples, test_size=0.2) # Set our batch size (*3 due to image center + left + right ....), then *2 due to flip of each images batch_size = batch_len * 3 * 2 #6*3*2 = 36 ....
from commonFunctions_v12 import generator from commonFunctions_v12 import batch_len from commonFunctions_v12 import print_info from commonFunctions_v12 import visualize_loss_history from commonFunctions_v12 import get_log_pathSampleData path_last_hard_turn_data = "./simulationData/002_hardLeftTurn20201208_0220/" path_003_OwnRecordingOneLapAntiClockwise = "./simulationData/003_OwnRecordingOneLapAntiClockwise/" path_004_ownRecordOneLapClockwise = "./simulationData/004_ownRecordOneLapClockwise/" path_005_ownRecordOneLapAntiClockwise = "./simulationData/005_ownRecordOneLapAntiClockwise/" path_006_OwnRecord2LapsRecoverSidesAntiClockwise = "./simulationData/006_OwnRecord2LapsRecoverSidesAntiClockwise/" path_007_trainHardTurnSeveralTimes = "./simulationData/007_trainHardTurnSeveralTimes/" # Reading CSV file FROM SAMPLE DATA, extracting lines. samples = get_lines_logfile(get_log_pathSampleData()) # Reading CSV file from Last Hard Turn, extracting lines # add them to samples lines. samples.extend(get_lines_logfile(path_last_hard_turn_data)) # Reading CSV file from 003_OwnRecordingOneLapAntiClockwise, extracting lines # add them to samples lines. samples.extend(get_lines_logfile(path_003_OwnRecordingOneLapAntiClockwise)) # Reading CSV file from 004_ownRecordOneLapClockwise, extracting lines # add them to samples lines. samples.extend(get_lines_logfile(path_004_ownRecordOneLapClockwise)) #samples.extend(get_lines_logfile(path_005_ownRecordOneLapAntiClockwise)) samples.extend(get_lines_logfile(path_006_OwnRecord2LapsRecoverSidesAntiClockwise)) samples.extend(get_lines_logfile(path_007_trainHardTurnSeveralTimes)) train_samples, validation_samples = train_test_split(samples, test_size=0.2)