Removed saved checkpoints

train_model.py

@@ -8,7 +8,6 @@ from video_compression_model import VideoCompressionModel
 NUM_CHANNELS = 3     # Number of color channels in the video frames (RGB images have 3 channels)
 BATCH_SIZE = 32       # Batch size used during training
 EPOCHS = 20           # Number of training epochs
-CHECKPOINT_FILEPATH = "models/checkpoint-{epoch:02d}.keras"
 
 # Step 1: Data Preparation
 TRAIN_VIDEO_FILE = 'native_video.mkv'  # The training video file name
@@ -68,21 +67,12 @@ val_targets = val_frames
 # Create the "models" directory if it doesn't exist
 os.makedirs("models", exist_ok=True)
 
-# Create the ModelCheckpoint callback
-model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
-    filepath=CHECKPOINT_FILEPATH,
-    save_weights_only=False,  # Save the entire model (including architecture)
-    monitor='val_loss',       # Metric to monitor for saving the best model (optional)
-    save_best_only=True       # Save only the best model based on the monitored metric (optional)
-)
-
 print("\nTraining the model...")
 model.fit(
     train_frames, [train_targets, tf.zeros_like(train_targets)],
     batch_size=BATCH_SIZE,
     epochs=EPOCHS,
-    validation_data=(val_frames, [val_targets, tf.zeros_like(val_targets)]),
-    callbacks=[model_checkpoint_callback]  # Add the ModelCheckpoint callback
+    validation_data=(val_frames, [val_targets, tf.zeros_like(val_targets)])
 )
 print("\nTraining completed.")