<filename>src/plot.py
import matplotlib.pyplot as plt


def plot(training_loss, validation_loss):
    plt.plot(training_loss)
    plt.plot(validation_loss)
    plt.title('Model MSE Loss')
    plt.ylabel('MSE Loss')
    plt.xlabel('EPOCH')
    plt.legend(['Training Set', 'Validation Set'], loc='upper right')
    plt.show()


# Just beach data with simple augmentation (flip and 3 cameras) and 12 epochs:

BEACH_TRAIN_LOSS = [0.29203601591729927, 0.11234304048808122, 0.076648723714189473, 0.065679013539236331, 0.060916535692157006, 0.058213301249240075, 0.056471760158238092, 0.055370922375445379, 0.054493466558385495, 0.05400875930439334, 0.053513852454605153, 0.053154040236029626]
BEACH_VAL_LOSS = [0.15082927886992376, 0.085503880609708552, 0.066843185923310483, 0.060497500907891848, 0.057085498873549481, 0.054939668673251486, 0.053687357429121098, 0.052954358467732922, 0.052188152717406559, 0.051678977036940008, 0.051174986781850298, 0.050799090232549193]

plot(BEACH_TRAIN_LOSS, BEACH_VAL_LOSS)

# Just mountain data with simple augmentation (flip and 3 cameras) and 12 epochs:

MOUNTAIN_TRAIN_LOSS = [0.5155500997697412, 0.3386540177715352, 0.26283437491145994, 0.22547522255903746, 0.20492127603818014, 0.19182595843424427, 0.18231127090795787, 0.1753811079097766, 0.1702928043917798, 0.16628352887273534, 0.16297390458626065, 0.16019888354994288]
MOUNTAIN_VAL_LOSS = [0.39929537955945293, 0.28780499062599085, 0.23753366783594551, 0.20977584270242686, 0.19284737183929632, 0.18201297731064378, 0.17492826192523725, 0.17033340593877311, 0.16635574245700441, 0.16292580907432416, 0.1599685984630935, 0.15754663591948562]

plot(MOUNTAIN_TRAIN_LOSS, MOUNTAIN_VAL_LOSS)

# Both tracks' data with simple augmentation (flip and 3 cameras) and 24 epochs:

BOTH_TRAIN_LOSS = [0.32635890172943577, 0.15972857741962843, 0.13075219050635664, 0.12095765079702954, 0.11559663201699476, 0.11188800751188817, 0.10935720800471102, 0.10719660859976771, 0.1058991095187934, 0.1046858898322978, 0.10380601525449643, 0.10292200840681381, 0.10272391134019798, 0.10183104963365223, 0.10130798398357807, 0.10080074775065788, 0.10050625802330543, 0.10009296250201793, 0.099600166053705766, 0.099272353619192372, 0.098920674318706242, 0.098471667487412512, 0.098244880989620975, 0.097957743729730734]
BOTH_VAL_LOSS = [0.19274231474555426, 0.13799693351783554, 0.12234490573730925, 0.11624409406071362, 0.11207198077033674, 0.10974012007155953, 0.10823982771554867, 0.10682073218923561, 0.10546859074604562, 0.10449770738139981, 0.10381611263135043, 0.10317486511346628, 0.10271511148716081, 0.1020338031468178, 0.10154211933912964, 0.10110835984088075, 0.10068573937885912, 0.10019680735187923, 0.10012146291794623, 0.099697940236849567, 0.099728864238675538, 0.099376484167584309, 0.099351950099207331, 0.099025428049841629]

plot(BOTH_TRAIN_LOSS, BOTH_VAL_LOSS)

<filename>src/plot.py
import matplotlib.pyplot as plt


def plot(training_loss, validation_loss):
    plt.plot(training_loss)
    plt.plot(validation_loss)
    plt.title('Model MSE Loss')
    plt.ylabel('MSE Loss')
    plt.xlabel('EPOCH')
    plt.legend(['Training Set', 'Validation Set'], loc='upper right')
    plt.show()


# Just beach data with simple augmentation (flip and 3 cameras) and 12 epochs:

BEACH_TRAIN_LOSS = [0.29203601591729927, 0.11234304048808122, 0.076648723714189473, 0.065679013539236331, 0.060916535692157006, 0.058213301249240075, 0.056471760158238092, 0.055370922375445379, 0.054493466558385495, 0.05400875930439334, 0.053513852454605153, 0.053154040236029626]
BEACH_VAL_LOSS = [0.15082927886992376, 0.085503880609708552, 0.066843185923310483, 0.060497500907891848, 0.057085498873549481, 0.054939668673251486, 0.053687357429121098, 0.052954358467732922, 0.052188152717406559, 0.051678977036940008, 0.051174986781850298, 0.050799090232549193]

plot(BEACH_TRAIN_LOSS, BEACH_VAL_LOSS)

# Just mountain data with simple augmentation (flip and 3 cameras) and 12 epochs:

MOUNTAIN_TRAIN_LOSS = [0.5155500997697412, 0.3386540177715352, 0.26283437491145994, 0.22547522255903746, 0.20492127603818014, 0.19182595843424427, 0.18231127090795787, 0.1753811079097766, 0.1702928043917798, 0.16628352887273534, 0.16297390458626065, 0.16019888354994288]
MOUNTAIN_VAL_LOSS = [0.39929537955945293, 0.28780499062599085, 0.23753366783594551, 0.20977584270242686, 0.19284737183929632, 0.18201297731064378, 0.17492826192523725, 0.17033340593877311, 0.16635574245700441, 0.16292580907432416, 0.1599685984630935, 0.15754663591948562]

plot(MOUNTAIN_TRAIN_LOSS, MOUNTAIN_VAL_LOSS)

# Both tracks' data with simple augmentation (flip and 3 cameras) and 24 epochs:

BOTH_TRAIN_LOSS = [0.32635890172943577, 0.15972857741962843, 0.13075219050635664, 0.12095765079702954, 0.11559663201699476, 0.11188800751188817, 0.10935720800471102, 0.10719660859976771, 0.1058991095187934, 0.1046858898322978, 0.10380601525449643, 0.10292200840681381, 0.10272391134019798, 0.10183104963365223, 0.10130798398357807, 0.10080074775065788, 0.10050625802330543, 0.10009296250201793, 0.099600166053705766, 0.099272353619192372, 0.098920674318706242, 0.098471667487412512, 0.098244880989620975, 0.097957743729730734]
BOTH_VAL_LOSS = [0.19274231474555426, 0.13799693351783554, 0.12234490573730925, 0.11624409406071362, 0.11207198077033674, 0.10974012007155953, 0.10823982771554867, 0.10682073218923561, 0.10546859074604562, 0.10449770738139981, 0.10381611263135043, 0.10317486511346628, 0.10271511148716081, 0.1020338031468178, 0.10154211933912964, 0.10110835984088075, 0.10068573937885912, 0.10019680735187923, 0.10012146291794623, 0.099697940236849567, 0.099728864238675538, 0.099376484167584309, 0.099351950099207331, 0.099025428049841629]

plot(BOTH_TRAIN_LOSS, BOTH_VAL_LOSS)

# Just beach data with simple augmentation (flip and 3 cameras) and 12 epochs: # Just mountain data with simple augmentation (flip and 3 cameras) and 12 epochs: # Both tracks' data with simple augmentation (flip and 3 cameras) and 24 epochs: