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Python/pokedex_Xception.py

+import tensorflow as tf
+from tensorflow.keras import layers, models, Input
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+from sklearn.utils.class_weight import compute_class_weight
+import numpy as np
+import json
+import os
+import argparse
+
+# --- GPU Strategy ---
+strategy = tf.distribute.MirroredStrategy()
+print("Number of GPUs:", strategy.num_replicas_in_sync)
+
+# --- Paths ---
+parser = argparse.ArgumentParser(description="WHERE ?!")
+parser.add_argument("--hpc", choices=["yes", "no"], default="no",
+                    help="Use HPC paths if 'yes', otherwise local paths.")
+args = parser.parse_args()
+
+if args.hpc == "yes":
+    dataset_path = "/home/users/d/divia/scratch/Combined_Dataset"
+    model_output_path = "/home/users/d/divia/pokedex/models/Xception"
+else:
+    dataset_path = "/home/padi/Git/pokedex/Combined_Dataset"
+    model_output_path = "/home/padi/Git/pokedex/models/Xception"
+
+os.makedirs(model_output_path, exist_ok=True)
+
+# --- Custom Xception-like model ---
+def simple_xception(input_shape, num_classes):
+    inputs = Input(shape=input_shape)
+
+    x = layers.Rescaling(1.0 / 255)(inputs)
+    x = layers.Conv2D(128, 3, strides=2, padding="same")(x)
+    x = layers.BatchNormalization()(x)
+    x = layers.Activation("relu")(x)
+
+    previous_block_activation = x
+
+    for size in [256, 512, 728]:
+        x = layers.Activation("relu")(x)
+        x = layers.SeparableConv2D(size, 3, padding="same")(x)
+        x = layers.BatchNormalization()(x)
+
+        x = layers.Activation("relu")(x)
+        x = layers.SeparableConv2D(size, 3, padding="same")(x)
+        x = layers.BatchNormalization()(x)
+
+        x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
+        residual = layers.Conv2D(size, 1, strides=2, padding="same")(previous_block_activation)
+        x = layers.add([x, residual])
+        previous_block_activation = x
+
+    x = layers.SeparableConv2D(1024, 3, padding="same")(x)
+    x = layers.BatchNormalization()(x)
+    x = layers.Activation("relu")(x)
+
+    x = layers.GlobalAveragePooling2D()(x)
+    x = layers.Dropout(0.25)(x)
+    outputs = layers.Dense(num_classes, activation='softmax')(x)
+
+    return models.Model(inputs, outputs)
+
+# --- Image settings ---
+img_size = (256, 256)
+batch_size = 32
+
+# --- Data Augmentation ---
+data_augmentation = tf.keras.Sequential([
+    layers.RandomFlip("horizontal"),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+    layers.RandomContrast(0.1),
+])
+
+# --- Load datasets ---
+raw_train_ds = image_dataset_from_directory(
+    dataset_path,
+    image_size=img_size,
+    batch_size=batch_size,
+    validation_split=0.2,
+    subset="training",
+    seed=123,
+)
+
+raw_val_ds = image_dataset_from_directory(
+    dataset_path,
+    image_size=img_size,
+    batch_size=batch_size,
+    validation_split=0.2,
+    subset="validation",
+    seed=123,
+)
+
+# Save class names
+class_names = raw_train_ds.class_names
+with open(os.path.join(model_output_path, "class_names.json"), "w") as f:
+    json.dump(class_names, f)
+print(f"Detected {len(class_names)} Pokémon classes.")
+
+# --- Compute class weights ---
+print("Computing class weights...")
+all_labels = [label.numpy() for _, label in raw_train_ds.unbatch()]
+class_weights = compute_class_weight(
+    class_weight="balanced",
+    classes=np.unique(all_labels),
+    y=np.array(all_labels)
+)
+class_weight_dict = dict(enumerate(class_weights))
+print("Class weights ready.")
+
+# --- Performance improvements ---
+AUTOTUNE = tf.data.AUTOTUNE
+train_ds = raw_train_ds.map(lambda x, y: (data_augmentation(x), y)).prefetch(AUTOTUNE)
+val_ds = raw_val_ds.prefetch(buffer_size=AUTOTUNE)
+
+# --- Build and compile model ---
+with strategy.scope():
+    model = simple_xception((*img_size, 3), num_classes=len(class_names))
+    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# --- Callbacks ---
+callbacks = [
+    EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
+]
+
+# --- Train the model ---
+model.fit(
+    train_ds,
+    validation_data=val_ds,
+    epochs=20,
+    callbacks=callbacks,
+    class_weight=class_weight_dict
+)
+
+# --- Save the model ---
+model_h5_path = os.path.join(model_output_path, "pokemon_xception.h5")
+model.save(model_h5_path)
+print(f"Model saved to {model_h5_path}")
+
+# --- Save as TensorFlow SavedModel ---
+saved_model_path = os.path.join(model_output_path, "saved_model")
+tf.saved_model.save(model, saved_model_path)
+print(f"SavedModel exported to {saved_model_path}")