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Code cleanup

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Sean
2026-03-10 20:27:42 -04:00
parent 1fbb16df15
commit 548143cbfc
1 changed files with 2 additions and 73 deletions
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75
Models/model_sk_convnext_v1.py
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@@ -39,7 +39,7 @@ tensorboard = TensorBoard(log_dir=log_dir)
# Configuration # Configuration
# ----------------------- # -----------------------
shuffle_count=3000 shuffle_count=3000
dataset_path = 'C:\\boneyard\\DeepLearning\\data' dataset_path = '/home/pi/DeepLearning/Data'
image_size = (actualImageDimension, actualImageDimension) image_size = (actualImageDimension, actualImageDimension)
batch_size = 16 # try 16 was 32 batch_size = 16 # try 16 was 32
image_size=(actualImageDimension, actualImageDimension) image_size=(actualImageDimension, actualImageDimension)
@@ -74,22 +74,7 @@ val_ds = tf.keras.preprocessing.image_dataset_from_directory(
# ----------------------- # -----------------------
# Data Augmentation # Data Augmentation
# ----------------------- # -----------------------
# I do this in c#-land
# data_augmentation = tf.keras.Sequential([
# layers.RandomFlip("horizontal"),
# layers.RandomRotation(0.1)
# ])
#data_augmentation = tf.keras.Sequential([
# layers.RandomFlip("horizontal"),
# layers.RandomRotation(0.1),
# layers.RandomRotation(0.1, fill_mode="nearest"),
# layers.RandomZoom(0.1)
#])
# def preprocess_train(x, y):
# x = data_augmentation(x, training=True)
# return x, y
def preprocess_val(x, y): def preprocess_val(x, y):
return x, y return x, y
@@ -106,22 +91,6 @@ train_ds = (
) )
# for images, labels in train_ds.take(1):
# plt.figure(figsize=(10,10))
# for i in range(12):
# ax = plt.subplot(3,4,i+1)
# plt.imshow(images[i].numpy().astype("uint8"))
# plt.title(int(labels[i].numpy()))
# plt.axis("off")
# plt.tight_layout()
# plt.show()
# ----------------------- # -----------------------
# ConvNeXt-Tiny Base Model # ConvNeXt-Tiny Base Model
# ----------------------- # -----------------------
@@ -143,13 +112,6 @@ inputs = tf.keras.Input(shape=(actualImageDimension, actualImageDimension, 3))
x = preprocess_input(inputs) x = preprocess_input(inputs)
x = base_model(x) x = base_model(x)
# Dense Head
# x = layers.GlobalAveragePooling2D()(x)
# x = layers.BatchNormalization()(x)
# x = layers.Dense(512, activation="relu")(x)
# x = layers.Dropout(0.3)(x)
# x = layers.Dense(128, activation="relu")(x)
x = layers.GlobalAveragePooling2D()(x) x = layers.GlobalAveragePooling2D()(x)
x = layers.BatchNormalization()(x) x = layers.BatchNormalization()(x)
x = layers.Dense(256, activation="relu")(x) x = layers.Dense(256, activation="relu")(x)
@@ -228,36 +190,3 @@ history_fine = model.fit(
validation_data=val_ds, validation_data=val_ds,
callbacks=[tensorboard, lr_scheduler, early_stopping, checkpointer] callbacks=[tensorboard, lr_scheduler, early_stopping, checkpointer]
) )
# -----------------------
# Plot Results
# -----------------------
def plot_history(hist, title_prefix=""):
plt.figure()
plt.plot(hist.history['accuracy'], label='Train Accuracy')
plt.plot(hist.history['val_accuracy'], label='Val Accuracy')
plt.title(f'{title_prefix} Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
plt.figure()
plt.plot(hist.history['loss'], label='Train Loss')
plt.plot(hist.history['val_loss'], label='Val Loss')
plt.title(f'{title_prefix} Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
plot_history(history, "Initial Training")
plot_history(history_fine, "Fine-Tuning")
# -----------------------
# Save Final Model
# -----------------------
#model.save(modelname)