Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66shttps://github.com/tensorflow/addons/issues/2807https://github.com/tensorflow/addons 

Help would be appreciated because im slowly losing my mind :(,
Jonas

Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66shttps://github.com/tensorflow/addons/issues/2807https://github.com/tensorflow/addons 

Help would be appreciated because im slowly losing my mind :(,
Jonas

Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66shttps://github.com/tensorflow/addons/issues/2807https://github.com/tensorflow/addons 

Help would be appreciated because im slowly losing my mind :(,
Jonas

Introduction
In the world of online learning and selling, you need a platform that helps you sell both digital and physical products easily. A good Learning Management System can make this process simple, save you money, and improve customer experience. In this article I wrote what features to look for in an LMS and introduced a great option, EzyCourse.
Key Features of a Good LMS for Selling Products
Easy to Use
An LMS should be simple to use. This helps you create and manage courses and products without any trouble. A clear layout helps users find what they need quickly.
E-commerce Features
To sell both digital and physical products, an LMS should have:

• Product Listings: Easily create and manage product listings with details, pictures, and prices.
• Payment Options: Support different payment methods for easy buying.
• Inventory Management: Track stock levels and manage orders for physical products.

Simple Checkout
A simple and secure checkout process helps reduce the number of abandoned carts. Customers should be able to buy products easily.
Marketing Tools
Marketing tools can help boost sales. Features like discount codes, promotions, and email marketing keep customers interested.
Reports and Insights
Good reporting and insights help you understand sales and customer behavior. This includes details about product sales, revenue, and customer demographics.
Selling Digital Products with an LMS
Digital products like eBooks, software, and online courses are popular because they are cheap to make and can be delivered instantly. A good LMS for digital products should have:

• Instant Delivery: Automatically deliver digital products after purchase.
• Download Management: Securely manage product downloads to prevent unauthorized access.
• Content Protection: Protect your content with features like watermarking and access controls.

Selling Physical Products with an LMS
For selling physical products, an LMS should provide:

• Shipping Integration: Real-time shipping rates and tracking with shipping carriers.
• Order Management: Tools to manage orders, print shipping labels, and handle returns.
• Customer Notifications: Automated notifications for order updates and delivery.

EzyCourse: A Great LMS Solution
EzyCourse is great for selling both digital and physical products.
Easy to Use
EzyCourse is designed to be simple. Its dashboard is easy to navigate, making it easy to create and manage courses and products.
E-commerce Features
EzyCourse has strong e-commerce features. It allows you to list products easily and manage them well. It supports different payment gateways for secure transactions.
Simple Checkout
EzyCourse offers a smooth and secure checkout process, which helps reduce abandoned carts.
Marketing Tools
EzyCourse provides marketing tools like discount codes and email marketing, helping you promote products and engage customers.
Reports and Insights
EzyCourse offers detailed reports and insights, helping you understand sales and customer behavior.
Selling Digital Products
EzyCourse is great for managing digital products. It ensures instant delivery, secure downloads, and content protection.
Selling Physical Products
EzyCourse integrates with shipping carriers for real-time shipping rates and tracking. It also offers tools to manage orders, print shipping labels, and handle returns. Automated customer notifications keep buyers informed.
Conclusion
In the world of online learning and selling, an LMS that supports both digital and physical products is important. Platforms like EzyCourse offer great features and are easy to use, making them ideal for businesses that want to streamline their operations and improve customer experience. If you can choose the right LMS, you can manage your products efficiently, save money, and achieve success.

Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: https://github.com/tensorflow/addons/issues/2807 warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: https://github.com/tensorflow/addons warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66s 

Help would be appreciated because im slowly losing my mind :(,
Jonas

Hello everyone!
I recently completed my first Expert run and started a fresh Master mode run with a new character. With this run I thought it would be nice to play with some QoL mods such as boss checklist and the likes of it, so i downloaded tModloader.
Everything was working fine for the first 2-3 hours, but then suddenly the gameplay started feeling very choppy. If you watch the video I added, it looks like the game is freezing for a very short time every second or so. It makes static objects like trees and houses look like they constantly wiggle back and forth while moving, which makes the gameplay feel laggy and me dizzy.
Link to gameplay with wiggling objects

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

Apply here: https://discord.gg/vPqyu9dPJ7
(Applications are rarely denied unless there is a lack of effort!)
INFORMATION
TotallyNotSuspicious is an SMP established in 2018, focused on creating an environment where interactions between members are highly valued - the primary goal is for the community to be inclusive of all people.
Every member is welcome to join with open arms; no one is judged here.
Why should you join us?

• Servers hosted in North America, with constant 20 TPS
• Discord to Minecraft chat
• Simple Voice Chat integration
• Suggestion forums
• Ticket systems, answered immediately
• Diamond economy, no shop plugins
• Organized Dragon Fights and other various events.

DISCORD
Our server uses Discord for communication, for events/updates. Joining the Discord is required in order to be whitelisted on the server, but applying is simple and easy, with the process being completed within the Discord Server.
Alternatively, the role of Discord Member is available - if you would just like to chat and/or join the server without playing.
MINI SEASONS
In Between seasons, we host custom modded mini seasons, lasting around a month - usually starting in December and June! These can range from snowy adventures with magic, to high tech summer modpacks. The current ongoing miniseason started on the 15th of December!
If you’d also like to see a few images of spawn from previous seasons;
https://imgur.com/gallery/0PEiH5n

I'll be honest, I'm that guy that picked it up for $20 because of the TV show. I know the game came out ages ago but when it came out my kids were young and my video game time was nil.
I wanted to share my thoughts, if anyone cares. Maybe no one does, but I'm guessing everyone here has played through a bunch of times, has a bunch of mods downloaded, etc. so maybe you will.
1) Dogmeat is annoying, I made Codsworth my follower out of frustration.
Literally shot that dog dozens of times since he would jump between my gun and the enemy trying to be helpful. Then had to stimpack him out of guilt.
2) The Sad Couple and Minuteman Mission Giver made me avoid Sanctuary.
I know it's supposed to be home, but I actually made my home the Drive in Theater instead where I didn't have to listen to the couple complain or risk being given another Minuteman mission that I didn't want. I made a really cool base (at least I loved it). Building was tricky, but I basically made tiers of society up to the top of the screen. Wasted a ton of time but had a lot of fun.
I wished I could have reserved the top bed for myself, but I basically had the King's Quarters on the highest level, my citizens the next level down, shops below that, then my workshops, and surrounded on the bottom level with turrets.
And then... My citizens kept getting trapped under the ramshackle palace, which was annoying. But otherwise building was fun.
3) I loved exploring the world overall.
The little sidequest stories found on various terminals were really cool. Especially the vault ones, but also finding little email arguments and whatnot. I always read what they said. Getting the special power armor chest from that lab was also cool, they should have had more stumble upon things like that.
4) I found all 3 factions distasteful, but went with the Institute as my choice.
The Institute was the least bad option, and it had my family loyalty. I also loved the backstory of how it was founded by survivors living in tunnels and that they created a paradise, and were still expanding underground.
I didn't like how the story tried to drive me into the arms of the railroad. Sorry not sorry: AI isn't people. Building a machine for a purpose and using it isn't "slavery."
Meanwhile the Brotherhood wanting to destroy everything and rule through military might just drives the world into further dystopia.
Did I agree with everything the Institute was doing? No. Replacing people with Synths and experimenting on people is obviously bad, and is too "Vault" type thinking, but it felt like the game wanted me to find out the evil part of the seemingly perfect society in order to push me into the arms of the railroad but didn't make the evil part all that shocking.
Sorry not sorry, but attempting to murder and destroy people over "enslaving" technology that they make themselves wasn't my cup of tea. So the Institute was the least bad of 3 immoral options. At least society isn't going to be a brutal fascist Brotherhood or run by people that want to stop using technology to improve humanity.
5) After the main quest line was over everything was meaningless.
It felt like a real lost bit of potential not to have a "part 2" to the game where you rebuild society. Fishing the main quest should have been the halfway point. I would have loved post-main quests based on your faction of choice. Like reestablishing the CIT. Cleaning it up and using the Minuteman network to recruit townships and create a functioning society.
I've glanced at mods and I don't mean "download a mod that makes this town look like it did before the fall". I mean like recruiting townsfolk to produce wall panels and machinery in one of the factories. Establishing local school houses. Using the Institute to invent some Environmental Rad Away Processors to slowly purify the lake water. That sort of thing.
Part of the hollow feeling was that the world didn't change. We destroyed the Railroad and Brotherhood, but then... It's just raiders and super mutants. No where to progress. Not even a cheesy montage with a time lapse of society improving over the course of decades and a credits scene. No closure. Just back to killing NPCs that are trying to invade Greentop again... Overall I enjoyed the game, just felt like it was unfinished.
I was only level 41 when I finished the main quest line too. Just did Minuteman quests for a while. I put off finishing it until I unlocked the final level of mods for weapons and armor, but probably would have been fine to finish it 10 levels earlier.
6) I also didn't love that I was required to unlock hacking and lockpicking to finish the game.
I was going to anyway, but the game sets up for customizing you're character and then forces you to build a certain way. I felt like I was required to have a high Charisma guy that could pick every lock and hack every terminal. If I wanted to be an ignorant brute just kicking down doors that option wasn't open to me. That master lock can't be picked? BS, I'm wearing power armor and can punch a giant monster to death, pretty sure I can rip a wooden door off it's hinges!
7) The fact that Deacon was supposed to be their idea of "cool" showed the writers grew up watching 80s movies.
It was laughable how big of a dork he actually was.

The demand for online education has skyrocketed in a society going more and more digital. This is an incredible opportunity for educators and content developers.
In the world of online education, websites like Udemy, LearnWorlds, and Kajabi are well-known.
Every platform has a distinct combination of features that it offers, from powerful tools for creating courses and analytics to marketing and community building capacities.
Whether you’re an academic institution, a creative worker, or an enterprise, the range of platforms caters to varied needs. It is crucial that instructors are knowledgeable about the advantages and disadvantages of each platform.

Best Place To Sell an Online Course

• Learnworlds – Best Place To Sell an Online Course, the default choice
• Udemy – Suitable for large companies
• Kajabi – Suitable for designers

This post seeks to give you a thorough overview of the most well-liked online course selling platforms, illuminating their features, costs, and course kinds that you may offer so that you can choose the best option for your needs and experience.

Learnworlds

LearnWorlds is a feature-rich online course platform that enables instructors to design, market, and sell their courses. The platform has many features out of the box, like customisable certificates, an integrated social network, and interactive video.
In addition, it has several third-party tool integrations, the capacity to generate assignments and quizzes, and the ability to engage students with interactive ebooks.
LearnWorlds provides distinctive features, such as integrated social networking and interactive video, for course developers who prioritize interactive and captivating content. Student happiness and engagement can be greatly increased in this way, which is important for course success.
Course producers may offer a more comprehensive and organized learning experience with its configurable assignments, quizzes, and certificates.
Flexibility in organizing and promoting courses is also made possible by the platform’s broad third-party interfaces.
Important features include tasks, integrations, certificates, interactive films, and quizzes.
Get today Learnworlds

Advantages include

• incredibly engaging course material
• an integrated social network for fostering community.

Cons

• Basic plans have a higher learning curve for new users.
• Monthly and transaction fees.

Selling points include business, technology, health, lifestyle, and education.

Cost

• $299 for the Learning Center,
• $299 for the Pro Trainer, and $29 for the Starter.
• A 30-day free trial is what a free demo trial offers.

In summary, LearnWorlds is a great platform for developers seeking community development and involvement.
Knowing something is not only powerful in the digital era, but it may also lead to profitable commercial ventures. People all across the world are ready to learn, and your knowledge could serve as a lighthouse for countless others. Never allow hesitancy to stop you.
Take the step to create a course and you and the many interested minds waiting for your insights will discover a world of possibilities.
It’s time to spread knowledge, educate, and thrive!
Simply said, you’re in good hands with LearnWorlds.
Get today Learnworlds

Udemy

Advantages of Udemy

• planned, prescriptive process for creating courses
• innate audience on Udemy
• Not much design or editing is required.

Drawbacks of Udemy

• steep revenue-sharing schedule
• Video is necessary.
• To provide paid courses, one must apply and be accepted.

Cost on Udemy

Udemy charges a revenue share of 63% on all other course sales and 3% on courses made through instructor coupons. It is free to publish free courses that include less than two hours of video. (Note: Mobile platform fees for sales of mobile courses, as well as processing costs from PayPal or Payoneer, are not included in revenue sharing.)
Why are you offering your first course on Udemy? due to the platform’s abundance of resources for developing and promoting courses.
For instance, if you’re not sure whether your video courses are up to par, you may send in a sample for evaluation.
You’ll receive comments on the audio and video, as well as equipment recommendations.
Additionally, Udemy offers a helpful Teaching Center with instructions on topics like organizing a course, shooting your first video, and other related subjects.
Udemy’s customized marketing courses can help if you’re having trouble closing your first few transactions.
Take advantage of site-wide discounts, have your course highlighted in their email blasts, or sign up for Udemy’s affiliate marketing program.
Udemy gets a cut of course sales; all of these programs are optional. The promotional channel you select will determine how much they take.
Because of the scale of the marketplace you’re competing with, it’s critical that you choose the ideal course topic and evaluate how it compares to other courses on the site using Udemy’s marketplace insights.
This function, which is akin to Google Analytics, provides information on a topic’s popularity on Udemy, search volume, and related keywords.
Additionally, you may view the quantity of courses that are currently available for a given subject, the category’s highest-earning courses, the monthly revenue earned, and the most effective channels for promotion (Udemy search, Udemy discovery, external sources, and paid ads).

Kajabi

Expert designers would benefit most from using Kajabi, one of the most upscale online course platforms.
Kajabi might not be for you if you are a novice or a small business owner.
On the other hand, this is the ideal location if you are an experienced designer looking to start an online academy. This platform even has Amy Porterfield’s endorsement!

Kajabi’s features

• Infinite landing sites
• Advertising emails without limit
• 1000 active members and 10,000 contacts
• One website Usable templates
• Automations
• Assessments (tests and questionnaires)

cost

• Basic, which costs $119
• Growth, which costs $159
• Procosts $319 per month (annual memberships are also an option)

The ability to build webinar pipelines is an intriguing feature shared by all of the options.
It also has fantastic marketing options, like expertly branded messaging, because it caters to huge edupreneurs.
The cost is somewhat more than that of the other platforms on this list, but for some users, the attentive, knowledgeable support staff can make up for it.
They could have made the digital offering more varied for the price they ask by including some possibilities, such as vFair’s event gamification tool, but they don’t.
The website may seem overly technical because it was designed with specialists in mind, which may discourage novice course developers from using it.
Overall, it is a costly and comprehensive LMS supplier, according to its reputation.

Like roster sharing? Playbooks too? I did see you’ll be able to download other people’s Team Builders, but do we know for sure if custom Rosters and Playbooks will be available to download too?
I’ll delete this too if this has already been answered a bunch in the sub

 ---- Minecraft Crash Report ---- // You should try our sister game, Minceraft! Time: 2024-06-01 06:30:21 Description: mouseClicked event handler java.lang.NoSuchMethodError: 'net.minecraft.class_339 net.minecraft.class_442.method_25411(net.minecraft.class_339)' at net.minecraft.class_442.handler$dom000$gbfabrictools$addConfigScreen(class_442.java:1530) at net.minecraft.class_442.method_25426(class_442.java:156) at net.minecraft.class_437.method_25423(class_437.java:297) at net.minecraft.class_310.method_1507(class_310.java:1080) at net.minecraft.class_8032.method_49296(class_8032.java:98) at net.minecraft.class_8032.method_25419(class_8032.java:90) at net.minecraft.class_8032.method_48639(class_8032.java:74) at net.minecraft.class_4185.method_25306(class_4185.java:94) at net.minecraft.class_4264.method_25348(class_4264.java:56) at net.minecraft.class_339.method_25402(class_339.java:189) at net.minecraft.class_4069.method_25402(class_4069.java:38) at net.minecraft.class_312.method_1611(class_312.java:98) at net.minecraft.class_437.method_25412(class_437.java:409) at net.minecraft.class_312.method_1601(class_312.java:98) at net.minecraft.class_312.method_22686(class_312.java:169) at net.minecraft.class_1255.execute(class_1255.java:102) at net.minecraft.class_312.method_22684(class_312.java:169) at org.lwjgl.glfw.GLFWMouseButtonCallbackI.callback(GLFWMouseButtonCallbackI.java:43) at org.lwjgl.system.JNI.invokeV(Native Method) at org.lwjgl.glfw.GLFW.glfwPollEvents(GLFW.java:3403) at com.mojang.blaze3d.systems.RenderSystem.pollEvents(RenderSystem.java:201) at com.mojang.blaze3d.systems.RenderSystem.flipFrame(RenderSystem.java:219) at net.minecraft.class_1041.method_15998(class_1041.java:288) at net.minecraft.class_310.method_1523(class_310.java:1241) at net.minecraft.class_310.method_1514(class_310.java:802) at net.minecraft.client.main.Main.main(Main.java:250) at net.fabricmc.loader.impl.game.minecraft.MinecraftGameProvider.launch(MinecraftGameProvider.java:470) at net.fabricmc.loader.impl.launch.knot.Knot.launch(Knot.java:74) at net.fabricmc.loader.impl.launch.knot.KnotClient.main(KnotClient.java:23) A detailed walkthrough of the error, its code path and all known details is as follows: --------------------------------------------------------------------------------------- -- Head -- Thread: Render thread Stacktrace: at net.minecraft.class_442.handler$dom000$gbfabrictools$addConfigScreen(class_442.java:1530) at net.minecraft.class_442.method_25426(class_442.java:156) at net.minecraft.class_437.method_25423(class_437.java:297) at net.minecraft.class_310.method_1507(class_310.java:1080) at net.minecraft.class_8032.method_49296(class_8032.java:98) at net.minecraft.class_8032.method_25419(class_8032.java:90) at net.minecraft.class_8032.method_48639(class_8032.java:74) at net.minecraft.class_4185.method_25306(class_4185.java:94) at net.minecraft.class_4264.method_25348(class_4264.java:56) at net.minecraft.class_339.method_25402(class_339.java:189) at net.minecraft.class_4069.method_25402(class_4069.java:38) at net.minecraft.class_312.method_1611(class_312.java:98) at net.minecraft.class_437.method_25412(class_437.java:409) at net.minecraft.class_312.method_1601(class_312.java:98) at net.minecraft.class_312.method_22686(class_312.java:169) at net.minecraft.class_1255.execute(class_1255.java:102) at net.minecraft.class_312.method_22684(class_312.java:169) at org.lwjgl.glfw.GLFWMouseButtonCallbackI.callback(GLFWMouseButtonCallbackI.java:43) at org.lwjgl.system.JNI.invokeV(Native Method) at org.lwjgl.glfw.GLFW.glfwPollEvents(GLFW.java:3403) at com.mojang.blaze3d.systems.RenderSystem.pollEvents(RenderSystem.java:201) at com.mojang.blaze3d.systems.RenderSystem.flipFrame(RenderSystem.java:219) -- Affected screen -- Details: Screen name: net.minecraft.class_8032 Stacktrace: at net.minecraft.class_437.method_25412(class_437.java:409) at net.minecraft.class_312.method_1601(class_312.java:98) at net.minecraft.class_312.method_22686(class_312.java:169) at net.minecraft.class_1255.execute(class_1255.java:102) at net.minecraft.class_312.method_22684(class_312.java:169) at org.lwjgl.glfw.GLFWMouseButtonCallbackI.callback(GLFWMouseButtonCallbackI.java:43) at org.lwjgl.system.JNI.invokeV(Native Method) at org.lwjgl.glfw.GLFW.glfwPollEvents(GLFW.java:3403) at com.mojang.blaze3d.systems.RenderSystem.pollEvents(RenderSystem.java:201) at com.mojang.blaze3d.systems.RenderSystem.flipFrame(RenderSystem.java:219) at net.minecraft.class_1041.method_15998(class_1041.java:288) at net.minecraft.class_310.method_1523(class_310.java:1241) at net.minecraft.class_310.method_1514(class_310.java:802) at net.minecraft.client.main.Main.main(Main.java:250) at net.fabricmc.loader.impl.game.minecraft.MinecraftGameProvider.launch(MinecraftGameProvider.java:470) at net.fabricmc.loader.impl.launch.knot.Knot.launch(Knot.java:74) at net.fabricmc.loader.impl.launch.knot.KnotClient.main(KnotClient.java:23) -- Last reload -- Details: Reload number: 1 Reload reason: initial Finished: Yes Packs: vanilla, fabric, Moonlight Mods Dynamic Assets, Essential Assets, essential Stacktrace: at net.minecraft.class_6360.method_36565(class_6360.java:49) at net.minecraft.class_310.method_1587(class_310.java:2413) at net.minecraft.class_310.method_1514(class_310.java:821) at net.minecraft.client.main.Main.main(Main.java:250) at net.fabricmc.loader.impl.game.minecraft.MinecraftGameProvider.launch(MinecraftGameProvider.java:470) at net.fabricmc.loader.impl.launch.knot.Knot.launch(Knot.java:74) at net.fabricmc.loader.impl.launch.knot.KnotClient.main(KnotClient.java:23) -- System Details -- Details: Minecraft Version: 1.20.1 Minecraft Version ID: 1.20.1 Operating System: Windows 11 (amd64) version 10.0 Java Version: 17.0.8, Microsoft Java VM Version: OpenJDK 64-Bit Server VM (mixed mode), Microsoft Memory: 16734346176 bytes (15959 MiB) / 24092082176 bytes (22976 MiB) up to 40869298176 bytes (38976 MiB) CPUs: 16 Processor Vendor: AuthenticAMD Processor Name: AMD Ryzen 7 5800X 8-Core Processor Identifier: AuthenticAMD Family 25 Model 33 Stepping 2 Microarchitecture: Zen 3 Frequency (GHz): 4.20 Number of physical packages: 1 Number of physical CPUs: 8 Number of logical CPUs: 16 Graphics card #0 name: Virtual Desktop Monitor Graphics card #0 vendor: Virtual Desktop, Inc. Graphics card #0 VRAM (MB): 0.00 Graphics card #0 deviceId: unknown Graphics card #0 versionInfo: DriverVersion=10.54.50.446 Graphics card #1 name: Parsec Virtual Display Adapter Graphics card #1 vendor: Parsec Cloud, Inc. Graphics card #1 VRAM (MB): 0.00 Graphics card #1 deviceId: unknown Graphics card #1 versionInfo: DriverVersion=0.45.0.0 Graphics card #2 name: NVIDIA GeForce RTX 4090 Graphics card #2 vendor: NVIDIA (0x10de) Graphics card #2 VRAM (MB): 24095.00 Graphics card #2 deviceId: 0x2684 Graphics card #2 versionInfo: DriverVersion=32.0.15.5585 Memory slot #0 capacity (MB): 16384.00 Memory slot #0 clockSpeed (GHz): 3.60 Memory slot #0 type: DDR4 Memory slot #1 capacity (MB): 16384.00 Memory slot #1 clockSpeed (GHz): 3.60 Memory slot #1 type: DDR4 Memory slot #2 capacity (MB): 16384.00 Memory slot #2 clockSpeed (GHz): 3.60 Memory slot #2 type: DDR4 Memory slot #3 capacity (MB): 16384.00 Memory slot #3 clockSpeed (GHz): 3.60 Memory slot #3 type: DDR4 Virtual memory max (MB): 85532.29 Virtual memory used (MB): 51955.71 Swap memory total (MB): 20096.00 Swap memory used (MB): 74.20 JVM Flags: 4 total; -XX:HeapDumpPath=MojangTricksIntelDriversForPerformance_javaw.exe_minecraft.exe.heapdump -Xss1M -Xmx38976m -Xms256m Fabric Mods: ad_astra: Ad Astra 1.15.5 ad_astra_giselle_addon: Ad Astra: Giselle Addon 5.6 additionallanterns: Additional Lanterns 1.1.1a advancednetherite: Advanced Netherite 2.1.0-1.20.1 adventurez: AdventureZ 1.4.20 alloy_forgery: Alloy Forgery 2.1.2+1.20 another_furniture: Another Furniture 1.20.1-3.0.1 appleskin: AppleSkin 2.5.1+mc1.20 archers: Archers (RPG Series) 1.2.1+1.20.1 com_github_zsoltmolnarrr_tinyconfig: TinyConfig 2.3.2 structure_pool_api: Structure Pool API 1.0+1.20.1 architectury: Architectury 9.2.14 archon: Archon 0.6.2 cardinal-components-base: Cardinal Components API (base) 5.2.2 cardinal-components-entity: Cardinal Components API (entities) 5.2.2 saflib: SafLib 1.1.0 artifacts: Artifacts 9.5.7 expandability: ExpandAbility 9.0.4 step-height-entity-attribute: Step Height Entity Attribute 1.2.0 attributefix: AttributeFix 21.0.4 azurelibarmor: AzureLib Armor 2.0.3 backpacked: Backpacked 3.0.0-beta.2 mm: Manningham Mills 2.3 bcc: BetterCompatibilityChecker 4.0.8 bclib: BCLib 3.0.14 wunderlib: WunderLib 1.1.5 beaconoverhaul: Beacon Overhaul 1.8.4+1.20 reach-entity-attributes: Reach Entity Attributes 2.4.0 betterdeserttemples: YUNG's Better Desert Temples 1.20-Fabric-3.0.3 org_reflections_reflections: reflections 0.10.2 betterdungeons: YUNG's Better Dungeons 1.20-Fabric-4.0.4 betterend: Better End 4.0.11 betterendisland: YUNG's Better End Island 1.20-Fabric-2.0.6 betterfortresses: YUNG's Better Nether Fortresses 1.20-Fabric-2.0.6 bettermineshafts: YUNG's Better Mineshafts 1.20-Fabric-4.0.4 betternether: Better Nether 9.0.10 betteroceanmonuments: YUNG's Better Ocean Monuments 1.20-Fabric-3.0.4 betterstrongholds: YUNG's Better Strongholds 1.20-Fabric-4.0.3 betterthirdperson: Better Third Person 1.9.0 betterwitchhuts: YUNG's Better Witch Huts 1.20-Fabric-3.0.3 biomemusic: Biome Music Mod 1.20.1-2.3 blur: Blur (Fabric) 3.1.0 midnightlib: MidnightLib 1.4.1 satin: Satin 1.13.0 bookshelf: Bookshelf 20.1.10 bosses_of_mass_destruction: Bosses of Mass Destruction (Beta) 1.7.5-1.20.1 maelstrom_library: Maelstrom Library 1.6.1-1.20 multipart_entities: MultipartEntities 1.5-1.20 botanypots: BotanyPots 13.0.33 botanytrees: BotanyTrees 9.0.11 botarium: Botarium 2.3.3 team_reborn_energy: Energy 3.0.0 bountiful: Bountiful 6.0.3+1.20.1 cardinal-components: Cardinal Components API 5.2.2 cardinal-components-block: Cardinal Components API (blocks) 5.2.2 cardinal-components-chunk: Cardinal Components API (chunks) 5.2.2 cardinal-components-item: Cardinal Components API (items) 5.2.2 cardinal-components-level: Cardinal Components API (world saves) 5.2.2 cardinal-components-scoreboard: Cardinal Components API (scoreboard) 5.2.2 cardinal-components-world: Cardinal Components API (worlds) 5.2.2 charmofundying: Charm of Undying 6.5.0+1.20.1 spectrelib: SpectreLib 0.13.15+1.20.1 chefsdelight: Chefs Delight 1.0.3-fabric-1.20.1 chimes: Chimes 2.0.1 cloth-config: Cloth Config v11 11.1.118 cloth-basic-math: cloth-basic-math 0.6.1 clumps: Clumps 12.0.0.4 collective: Collective 7.61 combatroll: Combat Roll 1.3.2+1.20.1 comforts: Comforts 6.3.5+1.20.1 continuity: Continuity 3.0.0-beta.5+1.20.1 coroutil: CoroUtil 1.20.1-1.3.7 cristellib: Cristel Lib 1.1.5 blue_endless_jankson: jankson 1.2.3 croptopia: Croptopia 3.0.3 ctov: ChoiceTheorem's Overhauled Village 3.4.3 culinaryconstruct: Culinary Construct 5.2.1+1.20.1 cupboard: cupboard 1.20.1-2.6 darkpaintings: DarkPaintings 17.0.4 darkutils: DarkUtilities 17.0.3 decorative_blocks: Decorative Blocks 4.1.3 deeperdarker: Deeper and Darker 1.2.6 customportalapi: Custom Portal Api 0.0.1-beta64-1.20 dimdoors: DimensionalDoors 5.3.5 com_flowpowered_flow-math: flow-math 1.0.3 com_github_dimensionaldevelopment_poly2tri_java: poly2tri.java 0.1.1 org_jgrapht_jgrapht-core: jgrapht-core 1.1.0 distanthorizons: Distant Horizons 2.0.4-a-dev doubledoors: Double Doors 5.7 dragonfight: Dragonfight Mod 1.20.1-4.5 dummmmmmy: MmmMmmMmmMmm 1.20-1.8.17b dungeonnowloading: Dungeon Now Loading 1.5 dungeons_arise: When Dungeons Arise 2.1.58 dungeons_arise_seven_seas: When Dungeons Arise: Seven Seas 1.0.2 durabilitytooltip: Durability Tooltip 1.1.5 dynamictrim: DynamicTrim 1.4.1 mixinsquared: MixinSquared 0.1.1 easymagic: Easy Magic 8.0.1 ecologics: Ecologics 2.2.0 elementa: Elementa 647 elytraslot: Elytra Slot 6.3.0+1.20.1 enchantedlib: Enchanted Lib 0.3.1 enchdesc: EnchantmentDescriptions 17.0.15 endrem: End Remastered 5.2.4 ends_delight: End's Delight refabricated-1.20.1-alpha-1.0 epherolib: EpheroLib 1.2.0 essential: Essential 1.3.2.5+ge4fdbcd438 essential-container: essential-container 1.0.0 essential-loader: essential-loader 1.2.3 everycomp: Every Compat 1.20-2.6.56 porting_lib_tags: Porting Lib Tags 3.0 expandeddelight: Expanded Delight 0.3.1 omega-config: OmegaConfig 1.4.0+1.20.1 explorify: Explorify v1.4.0 fabric-api: Fabric API 0.92.0+1.20.1 fabric-api-base: Fabric API Base 0.4.31+1802ada577 fabric-api-lookup-api-v1: Fabric API Lookup API (v1) 1.6.36+1802ada577 fabric-biome-api-v1: Fabric Biome API (v1) 13.0.13+1802ada577 fabric-block-api-v1: Fabric Block API (v1) 1.0.11+1802ada577 fabric-block-view-api-v2: Fabric BlockView API (v2) 1.0.1+1802ada577 fabric-blockrenderlayer-v1: Fabric BlockRenderLayer Registration (v1) 1.1.41+1802ada577 fabric-client-tags-api-v1: Fabric Client Tags 1.1.2+1802ada577 fabric-command-api-v1: Fabric Command API (v1) 1.2.34+f71b366f77 fabric-command-api-v2: Fabric Command API (v2) 2.2.13+1802ada577 fabric-commands-v0: Fabric Commands (v0) 0.2.51+df3654b377 fabric-containers-v0: Fabric Containers (v0) 0.1.64+df3654b377 fabric-content-registries-v0: Fabric Content Registries (v0) 4.0.11+1802ada577 fabric-convention-tags-v1: Fabric Convention Tags 1.5.5+1802ada577 fabric-crash-report-info-v1: Fabric Crash Report Info (v1) 0.2.19+1802ada577 fabric-data-attachment-api-v1: Fabric Data Attachment API (v1) 1.0.0+de0fd6d177 fabric-data-generation-api-v1: Fabric Data Generation API (v1) 12.3.4+1802ada577 fabric-dimensions-v1: Fabric Dimensions API (v1) 2.1.54+1802ada577 fabric-entity-events-v1: Fabric Entity Events (v1) 1.6.0+1c78457f77 fabric-events-interaction-v0: Fabric Events Interaction (v0) 0.6.2+1802ada577 fabric-events-lifecycle-v0: Fabric Events Lifecycle (v0) 0.2.63+df3654b377 fabric-game-rule-api-v1: Fabric Game Rule API (v1) 1.0.40+1802ada577 fabric-item-api-v1: Fabric Item API (v1) 2.1.28+1802ada577 fabric-item-group-api-v1: Fabric Item Group API (v1) 4.0.12+1802ada577 fabric-key-binding-api-v1: Fabric Key Binding API (v1) 1.0.37+1802ada577 fabric-keybindings-v0: Fabric Key Bindings (v0) 0.2.35+df3654b377 fabric-lifecycle-events-v1: Fabric Lifecycle Events (v1) 2.2.22+1802ada577 fabric-loot-api-v2: Fabric Loot API (v2) 1.2.1+1802ada577 fabric-loot-tables-v1: Fabric Loot Tables (v1) 1.1.45+9e7660c677 fabric-message-api-v1: Fabric Message API (v1) 5.1.9+1802ada577 fabric-mining-level-api-v1: Fabric Mining Level API (v1) 2.1.50+1802ada577 fabric-model-loading-api-v1: Fabric Model Loading API (v1) 1.0.3+1802ada577 fabric-models-v0: Fabric Models (v0) 0.4.2+9386d8a777 fabric-networking-api-v1: Fabric Networking API (v1) 1.3.11+1802ada577 fabric-networking-v0: Fabric Networking (v0) 0.3.51+df3654b377 fabric-object-builder-api-v1: Fabric Object Builder API (v1) 11.1.3+1802ada577 fabric-particles-v1: Fabric Particles (v1) 1.1.2+1802ada577 fabric-recipe-api-v1: Fabric Recipe API (v1) 1.0.21+1802ada577 fabric-registry-sync-v0: Fabric Registry Sync (v0) 2.3.3+1802ada577 fabric-renderer-api-v1: Fabric Renderer API (v1) 3.2.1+1802ada577 fabric-renderer-indigo: Fabric Renderer - Indigo 1.5.1+1802ada577 fabric-renderer-registries-v1: Fabric Renderer Registries (v1) 3.2.46+df3654b377 fabric-rendering-data-attachment-v1: Fabric Rendering Data Attachment (v1) 0.3.37+92a0d36777 fabric-rendering-fluids-v1: Fabric Rendering Fluids (v1) 3.0.28+1802ada577 fabric-rendering-v0: Fabric Rendering (v0) 1.1.49+df3654b377 fabric-rendering-v1: Fabric Rendering (v1) 3.0.8+1802ada577 fabric-resource-conditions-api-v1: Fabric Resource Conditions API (v1) 2.3.8+1802ada577 fabric-resource-loader-v0: Fabric Resource Loader (v0) 0.11.10+1802ada577 fabric-screen-api-v1: Fabric Screen API (v1) 2.0.8+1802ada577 fabric-screen-handler-api-v1: Fabric Screen Handler API (v1) 1.3.30+1802ada577 fabric-sound-api-v1: Fabric Sound API (v1) 1.0.13+1802ada577 fabric-transfer-api-v1: Fabric Transfer API (v1) 3.3.4+1802ada577 fabric-transitive-access-wideners-v1: Fabric Transitive Access Wideners (v1) 4.3.1+1802ada577 fabric-language-kotlin: Fabric Language Kotlin 1.11.0+kotlin.2.0.0 org_jetbrains_kotlin_kotlin-reflect: kotlin-reflect 2.0.0 org_jetbrains_kotlin_kotlin-stdlib: kotlin-stdlib 2.0.0 org_jetbrains_kotlin_kotlin-stdlib-jdk7: kotlin-stdlib-jdk7 2.0.0 org_jetbrains_kotlin_kotlin-stdlib-jdk8: kotlin-stdlib-jdk8 2.0.0 org_jetbrains_kotlinx_atomicfu-jvm: atomicfu-jvm 0.24.0 org_jetbrains_kotlinx_kotlinx-coroutines-core-jvm: kotlinx-coroutines-core-jvm 1.8.1 org_jetbrains_kotlinx_kotlinx-coroutines-jdk8: kotlinx-coroutines-jdk8 1.8.1 org_jetbrains_kotlinx_kotlinx-datetime-jvm: kotlinx-datetime-jvm 0.6.0 org_jetbrains_kotlinx_kotlinx-serialization-cbor-jvm: kotlinx-serialization-cbor-jvm 1.6.3 org_jetbrains_kotlinx_kotlinx-serialization-core-jvm: kotlinx-serialization-core-jvm 1.6.3 org_jetbrains_kotlinx_kotlinx-serialization-json-jvm: kotlinx-serialization-json-jvm 1.6.3 fabricloader: Fabric Loader 0.15.11 mixinextras: MixinExtras 0.3.5 fallingleaves: Falling Leaves 1.15.6 fallingtree: FallingTree 4.3.4 farmersdelight: Farmer's Delight 1.20.1-2.1.1+refabricated porting_lib_accessors: Porting Lib Accessors 2.3.4+1.20.1 porting_lib_base: Porting Lib Base 2.3.4+1.20.1 porting_lib_attributes: Porting Lib Attributes 2.3.4+1.20.1 porting_lib_common: Porting Lib Common 2.3.4+1.20.1 porting_lib_entity: Porting Lib Entity 2.3.4+1.20.1 porting_lib_fluids: Porting Lib Fluids 2.3.4+1.20.1 porting_lib_mixin_extensions: Porting Lib Mixin Extensions 2.3.4+1.20.1 porting_lib_transfer: Porting Lib Transfer 2.3.4+1.20.1 porting_lib_utility: Porting Lib Utility 2.3.4+1.20.1 porting_lib_client_events: Porting Lib Client Events 2.3.4+1.20.1 porting_lib_config: Porting Lib Config 2.3.4+1.20.1 porting_lib_extensions: Porting Lib Extensions 2.3.4+1.20.1 porting_lib_lazy_registration: Porting Lib Lazy Register 2.3.4+1.20.1 porting_lib_loot: Porting Lib Loot 2.3.4+1.20.1 porting_lib_networking: Porting Lib Networking 2.3.4+1.20.1 porting_lib_recipe_book_categories: Porting Lib Recipe Book Categories 2.3.4+1.20.1 porting_lib_registries: Porting Lib Registries 2.3.4+1.20.1 porting_lib_tool_actions: Porting Lib Tool Actions 2.3.4+1.20.1 porting_lib_core: Porting Lib Core 2.3.4+1.20.1 forgeconfigapiport: Forge Config API Port 8.0.0 framework: Framework 0.7.6 com_electronwill_night-config_core: core 3.6.6 com_electronwill_night-config_toml: toml 3.6.6 org_javassist_javassist: javassist 3.29.2-GA friendsandfoes: Friends&Foes 2.0.10 geckolib: GeckoLib 4 4.4.2 com_eliotlash_mclib_mclib: mclib 20 geophilic: Geophilic v2.2.0-mc1.20u1.20.2 goblintraders: Goblin Traders 1.9.3 graveyard: The Graveyard 3.0 guardvillagers: GuardVillagers 2.0.9-1.20.1 handcrafted: Handcrafted 3.0.6 hybrid-aquatic: Hybrid Aquatic 1.3.2 iceberg: Iceberg 1.1.18 illagerinvasion: Illager Invasion 8.0.5 extensibleenums: Extensible Enums 7.0.1 immediatelyfast: ImmediatelyFast 1.2.16+1.20.4 net_lenni0451_reflect: Reflect 1.3.3 immersive_aircraft: Immersive Aircraft 1.0.1+1.20.1 org_mariuszgromada_math_mathparser_org-mxparser: MathParser.org-mXparser 5.2.1 immersive_armors: Immersive Armors 1.6.1+1.20.1 incendium: Incendium 5.3.5 indium: Indium 1.0.30+mc1.20.4 inventorysorter: Inventory Sorter 1.9.0-1.20 kyrptconfig: Kyrpt Config 1.5.6-1.20 iris: Iris 1.7.0+mc1.20.1 io_github_douira_glsl-transformer: glsl-transformer 2.0.0-pre13 org_anarres_jcpp: jcpp 1.4.14 org_antlr_antlr4-runtime: antlr4-runtime 4.11.1 itemborders: Item Borders 1.2.2 jade: Jade 11.8.0 jamlib: JamLib 0.6.1+1.20.x java: OpenJDK 64-Bit Server VM 17 jei: Just Enough Items 15.3.0.4 justenoughbreeding: Just Enough Breeding 1.2.1 justenoughprofessions: Just Enough Professions (JEP) 3.0.1 kambrik: Kambrik 6.1.1+1.20.1 kotori316_version_checker: ForgeLikeVersionChecker 2.4.0 kotori_scala: Scalable Cat's Force Fabric 2.2.0 org_scala-lang_scala-library: scala-library 2.13.12 org_scala-lang_scala3-library_3: scala3-library_3 3.3.1 org_typelevel_cats-core_3: cats-core_3 2.10.0-kotori org_typelevel_cats-free_3: cats-free_3 2.10.0-kotori org_typelevel_cats-kernel_3: cats-kernel_3 2.10.0-kotori lavender: Lavender 0.1.9+1.20 lavender-md: lavender-md 0.1.1+1.20 lavender-md-owo-ui: lavender-md-owo-ui 0.1.1+1.20 leavesbegone: Leaves Be Gone 8.0.0 lootintegrations: Loot integration Mod 1.20.1-3.7 lootr: Lootr 0.7.33.81 magistuarmory: Epic Knights Mod 9.8 magnumtorch: Magnum Torch 8.0.2 majruszlibrary: Majrusz Library 7.0.8 majruszsaccessories: Majrusz's Accessories 1.5.3 majruszsdifficulty: Majrusz's Progressive Difficulty 1.9.10 mcdar: MC Dungeons Artifacts 4.0.3 mcdw: MC Dungeons Weapons 9.0.4 mcwfences: Macaw's Fences and Walls 1.1.1 mcwfurnitures: Macaw's Furniture 3.2.2 mcwlights: Macaw's Lights and Lamps 1.0.6 mcwpaintings: Macaw's Paintings 1.0.5 mcwpaths: Macaw's Paths and Pavings 1.0.5 mcwroofs: Macaw's Roofs 2.3.0 mcwtrpdoors: Macaw's Trapdoors 1.1.3 mcwwindows: Macaw's Windows 2.2.1 mes: Moog's End Structures 1.3.1-1.20-fabric minecraft: Minecraft 1.20.1 mobsunscreen: Mob Sunscreen 3.1.0 modelfix: Model Gap Fix 1.15 moonlight: Moonlight 1.20-2.11.30 more_armor_trims: More Armor Trims 1.2.0 moremobvariants: More Mob Variants 1.3.0.1 moretotems: More Totems 2.16.0 mousetweaks: Mouse Tweaks 2.26 mr_dungeons_andtaverns: Dungeons and Taverns 3.0.3.f mutantmonsters: Mutant Monsters 8.0.7 mvs: Moog's Voyager Structures 4.1.2-1.20-fabric naturalist: Naturalist 4.0.3 netherdepthsupgrade: Nether Depths Upgrade fabric-3.1.6-1.20 nyfsspiders: Nyf's Spiders 2.1.1 oceansdelight: Ocean's Delight fdrf-fabric-1.0.2-1.20 org_jetbrains_annotations: annotations 23.0.0 overloadedarmorbar: Overloaded Armor Bar 1.20.1-2 gbfabrictools: GBfabrictools 1.2.2+1.16 owo: oωo 0.11.2+1.20 paraglider: Paragliders 20.1.3 patchouli: Patchouli 1.20.1-84-FABRIC fiber: fiber 0.23.0-2 phantasm: End's Phantasm 0.3 philipsruins: Philip`s Ruins 1.20.1 pigpen: PigPen 15.0.2 player-animator: Player Animator 1.0.2-rc1+1.20 polymorph: Polymorph 0.49.5+1.20.1 prism: Prism 1.0.5 projectile_damage: Projectile Damage Attribute 3.2.3+1.20.1 puzzleslib: Puzzles Lib 8.1.20 puzzlesaccessapi: Puzzles Access Api 8.0.7 quarryplus: QuarryPlus 20.1.1159 ranged_weapon_api: RangedWeaponAPI 1.1.1+1.20.1 rare-ice: Rare Ice 0.6.0 resourcefulconfig: Resourcefulconfig 2.1.2 resourcefullib: Resourceful Lib 2.1.25 com_teamresourceful_bytecodecs: bytecodecs 1.0.2 com_teamresourceful_yabn: yabn 1.0.3 rightclickharvest: Right Click Harvest 3.2.3+1.19.x-1.20.1-fabric runelic: Runelic 18.0.2 runes: Runes 0.9.11+1.20.1 sawmill: Universal Sawmill 1.20-1.4.1 sdrp: Simple Discord Rich Presence 4.0.3-build.40+mc1.20.1 com_github_jagrosh_discordipc: DiscordIPC a8d6631cc9 com_kohlschutter_junixsocket_junixsocket-common: junixsocket-common 2.6.2 com_kohlschutter_junixsocket_junixsocket-native-common: junixsocket-native-common 2.6.2 org_json_json: json 20210307 simplylight: Simply Light 1.20.1-1.4.5 simplyswords: Simply Swords 1.55.0-1.20.1 spruceui: SpruceUI 5.0.0+1.20 skinlayers3d: 3d-Skin-Layers 1.6.5 smallships: Small Ships 2.0.0-b1.2 smarterfarmers: Smarter Farmers 1.20-1.8.2 sodium: Sodium 0.5.8+mc1.20.1 somanyenchantments: So Many Enchantments Mod 0.4.1 soulsweapons: Marium's Soulslike Weaponry 1.1.3-1.20-fabric sound_physics_remastered: Sound Physics Remastered 1.20.1-1.4.2 spell_engine: Spell Engine 0.14.3+1.20.1 spell_power: Spell Power Attribute 0.10.2+1.20.1 starterkit: Starter Kit 6.7 stoneworks: Stoneworks 8.0.0 structory: Structory 1.3.5 structory_towers: Structory: Towers 1.0.7 structureessentials: Structure Essentials Mod 1.20.1-3.3 supermartijn642configlib: SuperMartijn642's Config Lib 1.1.8+a supermartijn642corelib: SuperMartijn642's Core Lib 1.1.17 supplementaries: Supplementaries 1.20-2.8.11 suppsquared: Supplementaries Squared 1.20-1.1.14 t_and_t: Towns and Towers 1.12 terralith: Terralith 2.5.1 things: Things 0.3.3+1.20 totw_additions: Towers of the Wild: Additions 1.3 totw_modded: Towers Of The Wild: Modded fabric-1.20.1-1.0.5 trashcans: Trash Cans 1.0.18 travelersbackpack: Traveler's Backpack fabric-1.20.1-9.1.13 travelerstitles: Traveler's Titles 1.20-Fabric-4.0.2 treeharvester: Tree Harvester 8.7 trimeffects: Trim Effects 1.1.1-fabric trinkets: Trinkets 3.7.2 twigs: Twigs 3.1.0 universalcraft: UniversalCraft 337 veinmining: Vein Mining 1.4.1+1.20.1 vigilance: Vigilance 297 villagernames: Villager Names 7.3 villagersplus: Villagers Plus 3.1 villagesandpillages: Villages&Pillages 1.0.0 villagespawnpoint: Village Spawn Point 4.2 visuality: Visuality 0.7.1+1.20 visualworkbench: Visual Workbench 8.0.0 watut: What Are They Up To 1.20.1-1.1.1 weaponmaster: YDM's Weapon Master 3.0.5 wirelesschargers: Wireless Chargers 1.0.9+a wizards: Wizards (RPG Series) 1.2.0+1.20.1 yeetusexperimentus: Yeetus Experimentus 2.3.1-build.6+mc1.20.1 yet_another_config_lib_v3: YetAnotherConfigLib 3.4.4+1.20.1-fabric com_twelvemonkeys_common_common-image: common-image 3.10.0 com_twelvemonkeys_common_common-io: common-io 3.10.0 com_twelvemonkeys_common_common-lang: common-lang 3.10.0 com_twelvemonkeys_imageio_imageio-core: imageio-core 3.10.0 com_twelvemonkeys_imageio_imageio-metadata: imageio-metadata 3.10.0 com_twelvemonkeys_imageio_imageio-webp: imageio-webp 3.10.0 org_quiltmc_parsers_gson: gson 0.2.1 org_quiltmc_parsers_json: json 0.2.1 yigd: You're in Grave Danger 2.0.0-beta.13 fabric-permissions-api-v0: fabric-permissions-api 0.2-SNAPSHOT libgui: LibGui 8.1.1+1.20.1 jankson: Jankson 6.0.0+j1.2.3 libninepatch: LibNinePatch 1.2.0 yungsapi: YUNG's API 1.20-Fabric-4.0.5 yungsbridges: YUNG's Bridges 1.20-Fabric-4.0.3 yungsextras: YUNG's Extras 1.20-Fabric-4.0.3 Loaded Shaderpack: (off) Launched Version: fabric-loader-0.15.11-1.20.1 Backend library: LWJGL version 3.3.1 SNAPSHOT Backend API: NVIDIA GeForce RTX 4090/PCIe/SSE2 GL version 3.2.0 NVIDIA 555.85, NVIDIA Corporation Window size: 1024x768 GL Caps: Using framebuffer using OpenGL 3.2 GL debug messages: Using VBOs: Yes Is Modded: Definitely; Client brand changed to 'fabric' Type: Client (map_client.txt) Graphics mode: fancy Resource Packs: fabric Current Language: en_us CPU: 16x AMD Ryzen 7 5800X 8-Core Processor 

I normally play on Forge, but I gave Fabric a try and ended up getting back handed with this lmao