Feature/precision improvement

tests/test_tools.py

@@ -21,7 +21,7 @@ def test_load_dataset(tmp_path):
  p2 = d2 / f"img{i + 1}.jpg"
  p2.write_text("fake image data")
 
- train_ds, val_ds, class_names = load_dataset(str(d), 2, 32, 32)
+ train_ds, val_ds, class_names = load_dataset(str(d), 2, 32, 32, 123)
 
  assert len(train_ds) == 4
  assert len(val_ds) == 1
@@ -30,10 +30,12 @@ def test_load_dataset(tmp_path):
 
 def test_create_augmentation_layer():
  data_augmentation = create_augmentation_layer(32, 32)
- assert len(data_augmentation.layers) == 3
+ assert len(data_augmentation.layers) == 5
  assert isinstance(data_augmentation.layers[0], tf.keras.layers.RandomFlip)
  assert isinstance(data_augmentation.layers[1], tf.keras.layers.RandomRotation)
  assert isinstance(data_augmentation.layers[2], tf.keras.layers.RandomZoom)
+ assert isinstance(data_augmentation.layers[3], tf.keras.layers.RandomContrast)
+ assert isinstance(data_augmentation.layers[4], tf.keras.layers.GaussianNoise)
 
 
 def test_get_data_path_addon():

training/train.py

@@ -6,10 +6,12 @@
 from keras.applications import EfficientNetV2B1
 from utilities.tools import get_data_path_addon, get_base_path, suppress_tf_warnings, load_dataset, show_augmented_batch, create_augmentation_layer, plot_model_score, show_sample_batch, show_batch_shape
 from utilities.discord_callback import DiscordCallback
-from keras.optimizers import Adam
+from keras.optimizers import AdamW
 from keras.regularizers import l1_l2
 from keras.callbacks import EarlyStopping, ModelCheckpoint
 import os
+import random
+
 import tensorflow as tf
 # Ignore warnings
 import warnings
@@ -30,6 +32,8 @@
 # Set to True to load trained model
 load_model = False
 load_path = "../models/all_model_variants/efficientnet-old-head-model-variants.h5"
+# Set seed for reproducibility
+random_seed = True
 # Config
 base_path = get_base_path()
 path_addon = get_data_path_addon(model_type)
@@ -38,6 +42,7 @@
  "batch_size": 32,
  "img_height": img_height,
  "img_width": img_width,
+ "seed": random.randint(0, 1000) if random_seed else 123
 }
 
 # Load dataset and classes
@@ -52,11 +57,6 @@
 train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
 val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
 
-# Normalize the data
-normalization_layer = layers.Rescaling(1. / 255)
-normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
-image_batch, labels_batch = next(iter(normalized_ds))
-
 # Create data augmentation layer and show augmented batch
 data_augmentation = create_augmentation_layer(img_height, img_width)
 show_augmented_batch(train_ds, data_augmentation)
@@ -87,11 +87,11 @@
 ]) if not load_model else keras.models.load_model(load_path)
 
 # Define optimizer
-optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
+optimizer = AdamW(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, use_ema=True)
 
 # Define learning rate scheduler
 initial_learning_rate = 0.001
-lr_decay_steps = 1000
+lr_decay_steps = 10
 lr_decay_rate = 0.96
 lr_scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
  initial_learning_rate,
@@ -101,7 +101,7 @@
 
 # Compile model
 model.compile(optimizer=optimizer,
- loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+ loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
  metrics=['accuracy'])
 model.summary()
 
@@ -134,4 +134,3 @@
 # Save model
 model.save(f"{save_path}{name}.h5")
 
-# TODO: Different data augmentation (vertical, ..), Augmentation before training

utilities/tools.py

@@ -11,7 +11,7 @@
 import platform
 
 
-def load_dataset(path: str, batch_size: int, img_height: int, img_width: int) -> tuple[tf.data.Dataset, tf.data.Dataset, list]:
+def load_dataset(path: str, batch_size: int, img_height: int, img_width: int, seed: int) -> tuple[tf.data.Dataset, tf.data.Dataset, list]:
  """
  :param path: Path to the Dataset folder
  :param batch_size: Integer which defines how many Images are in one Batch
@@ -20,27 +20,19 @@ def load_dataset(path: str, batch_size: int, img_height: int, img_width: int) ->
  :return: Tuple of train, val Dataset and Class names
  """
  data_dir = pathlib.Path(path)
- # if "more_classes" in path:
- # image_count = len(list(data_dir.glob('*/*/*.jpg')))
- # else:
- # image_count = len(list(data_dir.glob('*/*/*/*.jpg')))
-
- # print("Image count:", image_count)
- # cars = list(data_dir.glob('*/*/*/*.jpg'))
- # PIL.Image.open(str(cars[0]))
  train_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
- seed=123,
+ seed=seed,
  image_size=(img_height, img_width),
  batch_size=batch_size)
 
  val_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
- seed=123,
+ seed=seed,
  image_size=(img_height, img_width),
  batch_size=batch_size)
 
@@ -99,12 +91,6 @@ def load_image_subset(path: str, batch_size: int, img_height: int, img_width: in
  :return: Subset of Dataset
  """
  data_dir = pathlib.Path(path)
- # if "more_classes" in path:
- # image_count = len(list(data_dir.glob('*/*/*.jpg')))
- # else:
- # image_count = len(list(data_dir.glob('*/*/*/*.jpg')))
-
- # print("Image count:", image_count)
 
  data = tf.keras.utils.image_dataset_from_directory(
  data_dir,
@@ -168,12 +154,14 @@ def create_augmentation_layer(img_height: int, img_width: int) -> keras.Sequenti
  """
  return keras.Sequential(
  [
- layers.RandomFlip("horizontal",
+ layers.RandomFlip("vertical",
  input_shape=(img_height,
  img_width,
  3)),
- layers.RandomRotation(0.1),
+ layers.RandomRotation(0.2),
  layers.RandomZoom(0.1),
+ layers.RandomContrast(0.1),
+ layers.GaussianNoise(0.1)
  ]
  )