From e15bb05ca685215a7625be4b9f05cdc519bf1988 Mon Sep 17 00:00:00 2001
From: "michael.divia" <michael.divia@etu.hesge.ch>
Date: Wed, 9 Apr 2025 18:16:38 +0200
Subject: [PATCH] Migrated from SoftMax to Logits
---
python/pokedex_ResNet50.py | 25 ++++++++++++-------------
1 file changed, 12 insertions(+), 13 deletions(-)
diff --git a/python/pokedex_ResNet50.py b/python/pokedex_ResNet50.py
index dad6c96..971ac69 100644
--- a/python/pokedex_ResNet50.py
+++ b/python/pokedex_ResNet50.py
@@ -69,15 +69,11 @@ print(f"Detected {len(class_names)} Pokémon classes.")
# --- Compute class weights ---
print("Computing class weights...")
-all_labels = []
-for _, labels in raw_train_ds.unbatch():
- all_labels.append(labels.numpy())
-
-all_labels = np.array(all_labels)
+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=all_labels
+ y=np.array(all_labels)
)
class_weight_dict = dict(enumerate(class_weights))
print("Class weights ready.")
@@ -92,26 +88,29 @@ with strategy.scope():
base_model = ResNet50(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
- outputs = Dense(len(class_names), activation="softmax")(x)
+ outputs = Dense(len(class_names), activation=None)(x)
model = Model(inputs=base_model.input, outputs=outputs)
- # Freeze some layers
for layer in base_model.layers[:100]:
layer.trainable = False
- model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+ model.compile(
+ optimizer='adam',
+ loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+ metrics=['accuracy']
+ )
# --- Callbacks ---
callbacks = [
EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
]
-# --- Train the model with class weights ---
+# --- Train the model ---
model.fit(
train_ds,
validation_data=val_ds,
- epochs=1,
+ epochs=20,
callbacks=callbacks,
class_weight=class_weight_dict
)
@@ -121,7 +120,7 @@ model_h5_path = os.path.join(model_output_path, "pokedex_ResNet50.h5")
model.save(model_h5_path)
print(f"Model saved to {model_h5_path}")
-# --- Save as TensorFlow SavedModel (for ONNX export) ---
+# --- 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}")
+print(f"SavedModel exported to {saved_model_path}")
\ No newline at end of file
--
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