Every time I try to upload the Mac Catalyst build of my App to App Store Connect, I get the following error: Asset validation failed This bundle is invalid. The value for key CFBundleVersion [3] in the Info.plist file must contain a higher version than that of the previously uploaded version [8]. Please find more information about CFBundleVersion at https://developer.apple.com/documentation/bundleresources/information_property_list/cfbundleversion (ID: 60d6b17f-ea3e-4e82-a6e6-21c18e6fb9ef) I have tried updating the version and build numbers in Xcode - still getting the same error. The Info.plist also looks fine - it just contains $(CURRENT_PROJECT_VERSION) next to Bundle version. The kicker is that I'm having no issues with uploading iOS builds - only Mac Catalyst builds. Any help would be appreciated - I've been stuck on this problem for months! I'm running Xcode 14 on macOS 12.5.1

Hi I'm trying to train a MobileNetV3Small model on a custom image classification pipeline on my M1 MacBook Pro using tensorflow-metal. While the code runs without error, the model doesn't seem to train at all - it predicts the same class for any input after training. I have already experimented with similar training on the same dataset with torchvision mobilenetv2 (on a GPU cluster) where I got over 60% accuracy (on 1098 image classes) after 2 epochs. I've included my code below, where even evaluating on the training set after training leads to poor performance. Any ideas what I could be doing wrong? import tensorflow as tf EPOCHS = 1 BATCH_SIZE = 128 LEARNING_RATE = 0.003 SEED=1220 if __name__ == '__main__': # Load train and validation data train_ds = tf.keras.preprocessing.image_dataset_from_directory( '/Volumes/detext/drawings/', color_mode="grayscale", seed=SEED, batch_size=BATCH_SIZE, labels='inferred', label_mode='int', image_size=(200,300)) # Get the class names class_names = train_ds.class_names num_classes = len(class_names) # Create model model = tf.keras.applications.MobileNetV3Small( input_shape=(200,300,1), alpha=1.0, minimalistic=False, include_top=True, weights=None, input_tensor=None, classes=num_classes, pooling=None, classifier_activation="softmax", include_preprocessing=True) # Compile model model.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE), loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]) # Training model.fit(x=train_ds, epochs=EPOCHS) # Testing hist = model.evaluate(x=train_ds) print(hist) model.save('./saved_model3/')