From c801ffeb750b55aa0a95c121f14e8edf4a9e235d Mon Sep 17 00:00:00 2001
From: "michael.divia" <michael.divia@etu.hesge.ch>
Date: Wed, 2 Apr 2025 10:09:17 +0200
Subject: [PATCH] Added Freezing and Fine Tuning for EfficientNetV2M
---
Python/pokedex_EfficientNetV2M.py | 62 +++++++++++++++++++------------
Python/pokedex_ResNet50.py | 2 -
2 files changed, 38 insertions(+), 26 deletions(-)
diff --git a/Python/pokedex_EfficientNetV2M.py b/Python/pokedex_EfficientNetV2M.py
index d6207c1..87d8274 100644
--- a/Python/pokedex_EfficientNetV2M.py
+++ b/Python/pokedex_EfficientNetV2M.py
@@ -1,10 +1,12 @@
import os
-import gc
import keras
import tensorflow as tf
from keras import layers
from tensorflow import data as tf_data
+# --- Silence TensorFlow logs ---
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
# --- GPU Strategy ---
strategy = tf.distribute.MirroredStrategy()
print("Number of GPUs:", strategy.num_replicas_in_sync)
@@ -14,9 +16,8 @@ data_dir = "/home/users/d/divia/scratch/Combined_Dataset"
image_size = (240, 240)
num_classes = 151
base_batch_size = 32
-base_lr = 1e-3
-
global_batch_size = 32
+base_lr = 1e-3
scaled_lr = min(base_lr * (global_batch_size / base_batch_size), 1e-3)
# --- Load Dataset ---
@@ -54,10 +55,13 @@ def preprocess_val(img, label):
train_ds = train_ds.map(preprocess_train, num_parallel_calls=tf_data.AUTOTUNE)
val_ds = val_ds.map(preprocess_val, num_parallel_calls=tf_data.AUTOTUNE)
-train_ds = train_ds.prefetch(buffer_size=tf_data.AUTOTUNE)
-val_ds = val_ds.prefetch(buffer_size=tf_data.AUTOTUNE)
+# Add auto-shard options to suppress Grappler warning
+options = tf.data.Options()
+options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
+train_ds = train_ds.with_options(options).prefetch(buffer_size=tf_data.AUTOTUNE)
+val_ds = val_ds.with_options(options).prefetch(buffer_size=tf_data.AUTOTUNE)
-# --- Build & Compile Model ---
+# --- Build model ---
with strategy.scope():
base_model = tf.keras.applications.EfficientNetV2M(
include_top=False,
@@ -65,31 +69,41 @@ with strategy.scope():
input_shape=(240, 240, 3)
)
- model = keras.Sequential([
- base_model,
- layers.GlobalAveragePooling2D(),
- layers.Dense(256, activation='relu'),
- layers.Dropout(0.5),
- layers.Dense(num_classes, activation='softmax')
- ])
+ x = layers.GlobalAveragePooling2D()(base_model.output)
+ x = layers.Dense(256, activation='relu')(x)
+ x = layers.Dropout(0.5)(x)
+ predictions = layers.Dense(num_classes, activation='softmax')(x)
+
+ model = keras.Model(inputs=base_model.input, outputs=predictions)
- optimizer = tf.keras.optimizers.Adam(learning_rate=scaled_lr)
+ # PHASE 1: Freeze the base model
+ base_model.trainable = False
model.compile(
- optimizer=optimizer,
- loss='categorical_crossentropy',
- metrics=['accuracy']
+ optimizer=tf.keras.optimizers.Adam(learning_rate=scaled_lr),
+ loss="categorical_crossentropy",
+ metrics=["accuracy"]
)
-# --- Train ---
+# --- Callbacks ---
callbacks = [
keras.callbacks.ModelCheckpoint("/home/users/d/divia/EfficientNetV2M/save_at_{epoch}.keras"),
keras.callbacks.EarlyStopping(monitor="val_loss", patience=3, restore_best_weights=True)
]
-model.fit(
- train_ds,
- validation_data=val_ds,
- epochs=10,
- callbacks=callbacks
-)
\ No newline at end of file
+# --- Train head only ---
+model.fit(train_ds, validation_data=val_ds, epochs=5, callbacks=callbacks)
+
+# PHASE 2: Fine-tune top of the base model
+# Unfreeze the whole base:
+base_model.trainable = True
+
+# Recompile with lower LR for fine-tuning
+model.compile(
+ optimizer=tf.keras.optimizers.SGD(learning_rate=1e-5, momentum=0.9),
+ loss="categorical_crossentropy",
+ metrics=["accuracy"]
+)
+
+# Fine-tune the full model
+model.fit(train_ds, validation_data=val_ds, epochs=5, callbacks=callbacks)
\ No newline at end of file
diff --git a/Python/pokedex_ResNet50.py b/Python/pokedex_ResNet50.py
index 9659d76..925618a 100644
--- a/Python/pokedex_ResNet50.py
+++ b/Python/pokedex_ResNet50.py
@@ -1,5 +1,3 @@
-import os
-import gc
import keras
import tensorflow as tf
from keras import layers
--
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