“So you’re a traveler from another world, with strange powers, here to help us out?” The commander of the unit summarized as the rest of the unit approached the still super train, preparing to salvage it.
“Yup,” I said nervously.
“Okay,” he replied after a moment.
“That’s it?” I asked, surprised.
“Good an explanation as any,” he shrugged, “considering you took down that war train and aren’t objecting to us taking it, I’m prepared to give you at your word.”
“What are you scavenging it for anyways?” I asked, turning to look at the massive vehicle, “you that hard up for metal?”
“There’s plenty besides metal,” the commander explained, “for one the parts in this thing are manufactured to far more precision than the human hand could manage. But even past that, these things carry vast stocks of coal and water, both of which can be hard to come across.”
“Not to mention oil for lubrication,” his second in command added.
“Finally there’s weapons and ammo, without access to factories of our own this is our best source of munitions.”
“Makes sense,” I nodded, “what’s the world like? Humanity still out there?”
“I don’t know about the world at large, but there’s a decent number of us hiding out within the machine’s territory,” sighed the commander, waving to a group of what appeared to be steam powered cars to pull up, each of them pulling a large cart for supplies, “to give you the short history of the war, a dozen years back the machine came online, with orders to expand. So it did, and hasn’t stopped yet. Everywhere it goes it turns into wastelands like this,” he motioned to the endless expanse of flat desert around us, “mountains, forests, lakes, doesn’t matter, it bulldozes it flat and builds factories or mines.”
“Why flatten everything?” I ask.
“Defense,” the Harmony answered, the commander nodding, “it relies on sonar for detection so fewer obstacles the better.”
“That was our guess too,” agreed the army man, “there are a few places it wasn’t able to pave, where we’ve been able to survive. In our case we live in a volcano, the damned thing tried to dig it out before the magma forced it to give up.”
“What about other nations? Surely not all of humanity has fallen.”
“On that front I admit to having less information,” he shrugged, “a few years ago we heard rumors that people outside managed to stall it, though I’m not sure how.”
“It seems to understand some natural phenomena can’t be overcome,” the Harmony commented, “perhaps they found a way to trigger that response along the entire front?”
“Good a theory as any?”
“I’m curious as to who would build such a thing,” I said, motioning to the mega-train, “I can’t think of much of a use for this kind of device.”
The commander looked around, nodding to his second in command who turned and left to coordinate the salvaging operation.
“Come with me,” he said, leading me away from the tracks. For several minutes we walked back towards where his men had dug in to assault the train. They were busy packing up the large guns that I recognized as being the main weapon of the train, only modified to rest on a foldable base and operate without clockwork. More steam-cars were being used to pull the guns once they were folded away, a half dozen tents of various sizes had been set up, the largest of which was white with a large red cross. It was also easily the busiest, with nurses and wounded constantly moving in and out at a frantic pace.
The commander led me into one of the side tents that appeared to be a makeshift command post, tables covered with maps filled the space, leaving little room to walk.
“Since you helped us out I’ll tell you what we know about the origins of the damned machine, but this is sensitive information, so don’t go sharing it around,” he warned me with a long glare before continuing, “the official story is the machine went rogue, some fault caused it to refuse to shut down. From then it’s simply kept expanding, seeing humanity as a danger.”
“That was our theory,” the Harmony replied.
“Unfortunately its not true,” he sighed, “the creator deliberately ensured his machines wouldn’t shut down. The flaw was intentional, exactly the same fault is found in every one of the base plans the machines use.”
“Why would he do that?” I asked.
“The man was brilliant, but in the end he seemed to go crazy. We don’t have much information about the last years or exactly what pushed him over the edge, but he seemed to think this was the only way to, as he put it, ‘defeat eternity.’ We think something drove him insane and he thought the universe itself was out to get him.”
“That’s,” the harmony started, but didn’t continue. We’d encountered multiple people who’d mentioned eternity by now, the most memorable was the man from the unchanging world. But the Conductor and a few others had brought it up by now.
“Ya, crazy,” the commander shrugged, seeming to take the Harmony’s comment another way.
“If true it’ll make fixing this thing much harder,” the Harmony said, “ordering a simple repair of damaged parts is one thing, but fixing a design flaw purposefully built into the blueprints of the entire mechanism?”
“Yup,” he sighed, “honestly our best bet is to just keep taking out trains where we can and hope to eventually out last them.”
“There might be another way,” the Harmony said slowly, “I noticed the machines only use a single frequence of sonar. Is that true across all their units?”
“Yes, near as I can remember,” the commander said after a moment.
“I should be able to design a few noise canceling patterns that work on that frequency it should allow you to approach their factories without being noticed.”
“Wait, you can make us invisible?” he asked, leaning forward.
“At least until the machine figures out the trick and varies up the sonar devices,” the Harmony warned, “but if you use it strategically it could net you large wins.”
“Will it work on moving objects?” I asked, “most passive noise cancelling only works on static objects, like in rooms.”
“It should, only because the machine uses a single frequency for its sonar,” the Harmony replied, sending me a series of calculations it wanted me to run through our datalink, “and it won’t be perfect, anyone using it will have to be exceptionally careful to not make too much noise or bump anything that could tip off the machine.”
“If its easier to hide immobile objects, then could you hide a derailing device?” the commander asked, starting to look excited, “if we could reliably derail the trains without them knowing why we might be able to starve out a section.”
“Stationary things are easier,” it agreed, looking over the results of the calculations I sent back. Its image projection crystal flickered on, displaying blueprints for a couple devices on the table, “I don’t know if a standard train derailer is enough to force a train of that size off the tracks, but you should be able to double the size of the one here without impacting its effectiveness.”
The commander’s eyes grew wide as the image appeared, rushing to the entrance of the tent and shouting for someone. In a minute a half dozen people were present, copying the diagrams along with a few variations the Harmony added.
“Hard to believe a few odd angles can disrupt sonar so easily,” one of the techs remarked, looking over one of the blueprints.
“It’s only possible because the Harmony is an entity basically made of sound,” I replied, “I doubt I could come up with something like this.”
“And I have to stress, this will only work until the machines change their sonar frequency,” the Harmony added, “as soon as they catch on these devices will become ineffective.”
“But could you design new patterns for whatever frequency they switch to?” the tech asked.
“No, if the machine is smart, and it seems smart enough, it’ll start using a few different frequencies. A single pattern can only cover a single frequency effectively.”
“Still, making a change like that across the machine’s hundreds of facilities and thousands of trains is a slow process,” the commander said, “even if it catches on, we could have years before all of it is altered to counter this.”
“Very true,” the Harmony agreed, its crystals even bobbing as if mimicking a nod, “it’s not a permanent fix, but it’ll help.”
“Speaking of, we encountered something odd,” one of the technicians spoke up, “seems like you understand the machine better than most, perhaps you can help us figure it out?”
“Sure,” I shrug, motioning for him to lead the way. Ten minutes later I’m in the mega-train once more, looking at a bank of gears arranged in a chaotic, but clearly intentional pattern.
“Right there, see those main gear trains?” the man asked, pointing at a cluster of mechanical bits that stood out against the rest, “that appears to be the main protocol mechanical computing, they’re what tell it what is or isn’t part of the protocols, if something violates them, and so on.”
“Right,” I nod, only to scowl, “one set of gears per protocol?”
“Yup,” he nodded.
“Then why are there four sets?”
“That’s what confused us,” he replied, “this place is directly above the engine room so we’ve never captured it intact before, so we’ve never noticed the extra-protocol set.”
“A back up?” the Harmony offered, “or error correction?”
“I don’t think so, it doesn’t match any of the other stacks, and error correction is over there,” he pointed to another set of gears, “you seemed to be well informed, maybe you could shine some light on this?”
“Seems pretty obvious to me,” I said, looking up to see both the tech and the Harmony staring at me, “what? Clearly there’s a fourth, hidden, protocol.”
“There’s only three protocols though,” the tech replied, “expand, defend, seek approval, we’ve known that for years.”
“Seems like the machine has a fourth,” I countered, pointing at the extra gear stack, “perhaps something secret the designer put in without anyone knowing?”
“Or the machine is evolving,” the Harmony replied softly, earning an alarmed look from me, “it seems quite rigid but, like you said, it’s been operating for years. If it can alter the design of its trains, then why not its own computation systems?”
“that’s… worrying,” the tech remarked, “honestly I hope its like the Traveler said, that it’s a hidden protocol.”
“Either case shouldn’t matter much,” I said, “there’s a limit to how fast mechanical systems can process data. It should hit a limit to how far it can evolve if that’s what’s happening.”
“I disagree,” the Harmony countered, “I’m living proof that multiple overlapping systems can produce intelligence far beyond what individual parts could come up with. Sound can only carry so much information at a time, but I can operate far beyond that limit due to my nature. It could stumble upon something similar for mechanical computers.”
“Seems unlikely,” I replied, and the Harmony didn’t disagree, but the thought was worrying regardless.
***** Discord - Patreon *****

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

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

I have a lot of well-respected white papers about C# & programming in general. You're probably familiar with my more notable publications: "Obfuscationeering: The Mathart of Obfuscationology" & "Use Dynamic Instead of Var". I haven't published anything industry-shifting for a couple of years, but I think it's time to return to revolutionerizing the discipline of the home computer sinuses.
Let me explain: You know how when you take fistfuls of bath salts & huff an entire tank or two of butane, you're able to hyperfocus on activities such as repeatedly drop-kicking Ronald McDonald statues, or fighting 6 cops after running through plate glass?
That was sorta me last weekend, but instead of just screaming that I can taste colors & attacking people with a spoon, I also exercised my brain and randisomoly invented a new programming paradigmogy:
Exception-Driven Eventoring.
I plan to write a guide explaining at readers how one would throw events, how clients could subscribe to receive any & all exceptions you raise, re-re-throwing events, and types such as ExceptionEvent, ExceptionEventHandler delegamanators, ExceptionEventArghs, and of course ExceptionEventException exceptions. I'll even show an example using LeftMouseButtonClickClickExceptionEvent (obviously the event that is thrown when a user click clicks the anterior mouse button).
The performance implications at the prototype stage are pretty good so far. I eyeball all of my benchmarks.
I'll skip over explaining general exceptioneering concepts, assuming most readers are familiar with catching objects of the Exception base class and doing nothing with them (you shouldn't handle exceptions if they are not your fault).
I believe we can all agree about how groundbreakening this is for the .NET community as a whole. The problem is I need somebody else listed as the author. I don't want people to see my name and think "OH, THAT'S THE 5th GANG OF FOUR GUY ". I want the article to hold up on its own.
What do you say? Are you ready to commit academic fraud with me for the good of the industry? There's a hot Canadian bacon & peanut butter on rye in it for any of you who are serious about doing this with me. DM for more details.

Hi,
I recently resigned from a horrible experience grad program in a hospital, what are my chances of securing another grad program, or another job as a nurse? I am a little worried… what would u guys do?

Hi I just came across a novel, chosen by the moon novel by izabella W. Its on pay by chapter websites, I've opened and read a few chapters but I can't seem to find any free version or chapter version anywhere. Please help. If anyone has read it I'm willing to take spoilers. Here's the advert I found below of it on Facebook.
Lycanthrope species is a disgusting race. And I, Delan Riley, am nothing more than a human scum in their eyes never expected those species would turn my world upside down. Since when the lycans managed to penetrate our town, like in the early 1900's we have a hierarchy, upper class = the lycans, middle class = mated humans, and lower class = the normal humans, who were basically considered scum. I endured their torment day after day, vowing to run away from them one day, until that day came and everything changed.
Dylan POV "Humans," I scowled at the principal's words from tannoy. "The Alpha twins will be celebrating their birthday tomorrow, as such, festivities are in order." Oh great, the Alphas twin children. Adrian and Arya are the worst lycans alive. I swear just because they are the alphas kids they literally get away with everything. If their birthday is tomorrow, then the wolves are going to be worse than ever. "All students will be present to greet them, two lines will be made, with humans on the left and the lycanthrope on the right. Any mated human will be at the front of the line for their year, you will all also be in order of your school year. That is all." Chat broke out the minute the tannoy was finished. "We haven't had a school gathering since the alpha king visited three years ago, before his sons coronation." Nick was right, the last time we all gathered like that was for the king and queens visit, when he decided to let the world know that he was to renounce his title to his only child, son Josh. "That sick bestard, he wants to make sure everyone is there so those idiot twins can find their mates." Yes I was mad, my fists connected with the table in front of me once more as I thought about how disgusting the situation was. You see the twins will be turning 17, so it's very possible someone in our school could be their mate, finding a mate is sacred to a wolf, the minute they say that one word your fate is sealed. They will turn your mind, morph you into being a lover of their kind, and then you'll give in.

That won't happen to me, I'm growing old to see the world as it once was, and I'm going to choose who I'll be with. No one will take that dream away from me.

Once dinner was finished, I just wanted to sleep. I'd had a very long tiring day, I quickly sat down on a small stool my mother kept in the storage closet and removed my shirt while my brother Freddy sat at the table to do his simple homework. It wasn't long before my mother came in with a large bowl of warm salt water and some cotton, this was going to sting I just knew it. She was here to help me with the wounds caused by wolves yesterday. She slowly began to unwrap the bandage from around my torso and slowed down drastically when it came to the final layer, I felt it peel off every wound and my fists clenched in pain. "Jesus!" I heard my mom exclaim once the dressing was completely removed. The air on my back was nice though and I sighed as my arm covered my once again exposed brests. "This is more than 15!" I began to hear sniffles coming from her and sighed turning round to look at her face, only to notice tears streaming down it. "Mom I'm fine, it's alright." She shook her head. "It's not alright, I'm your mother I shouldn't let these things happen. I'm so sorry. Your father would have..." here she goes again. Every single time something happened she'd always bring up dad, it really annoyed me because no matter how much we all wish he was here, he just isn't. My father was kiled by THEIR kind, almost 5 years ago when they actually managed to take over. When the lycans managed to penetrate our town my father rose up with some people from the neighborhood, to defend our livelihood, it was futile to say the least. We lost many people and I watched as my dad was ripped apart by two fully shifted wolves, I ended up shoting him to stop his suffering before they dragged me to the courtyard, i was the person to receive the first lashing of the town when I was 12! The wolves have been pretty strict with me since that day. "Stop being stvpid!" Was I harsh? Definitely! Did she need to hear it again, absolutely. "Dad is dead, we don't know what he'd do because he never knew this life. He never knew this world." I know what he'd have done, most likely attacked the guy who held the whip and got himself kiled in the process. "The best thing you can do for me, is stop crying and help me, next time don't insist on helping if you can't handle it." She began to wash my open wounds with the warm salt water causing loud winces to leave me, I knew it was necessary to prevent infection, but my god it hurt like a betch. "Some of these are really deep Dylan!" She sniffed again and my eyes rolled in my head. "I told you, I'm fine, just wrap me back up so I can get to bed." My mom was obviously more impacted by my injuries than I was, I suppose that always the case though. When it's happening to you, you've just got to get through it but when it's happening to someone you love, you just want to take their pain away. She quickly placed a fresh bandage around my waist and chest and wrapped it tightly for compression. The bowl of water that was used was now red in color, I guess from the blood my back was dripping with. "Can you keep your head down please? At least just this week. You can't take any more lashings." I simply nodded before standing up away from the stool, I walked over to Freddie and ruffled his hair in affection. "Good night squirt." He giggled and fixed his hair slightly. "Night Dilly." I smiled walking upstairs to my little bedroom, as soon as I was inside i shut the door and flopped down on to my bed on my stomach and I took a minute to cry to myself at the pain in my back, what my mom did was important but it hurt, not that I'd ever tell her. My hand covered my mouth quickly to muffle any noise I might be making. I couldn't tell anyone, I had to be strong because more and more people were crumpling these days, and my mom would break if she knew how much I was suffering. Sleep followed me shortly after, she was right though about me needing to keep my head down for the time being, I could not take another lashing! After a long night and an even longer morning, we were all finally stood in the hallway at school waiting for the twins to arrive. "Mine!" Everyone that was stood in the hallway tensed up, as we were seniors, me and Nick were stood towards the very back of the human line. All the mated people were situated directly opposite their wolf mates in their years. We stayed silent and still as Arya walked down the hall and stopped directly in front of Nick. His eyes widened in fear, unsure of wether to look up or keep his head lowered. "Look me in the eye, mate." He glanced at me slightly as if asking what he should do. "I said, look me in the eye." He slowly moved his eye line up to look at her face. I took a glance myself to see her eyes pitch black with lust. "I... can't... I mean... erm." Before he was able to mutter anything else, two wolves from opposite, grabbed him out of the line and dragged him behind Arya. "Hey!" My head shot up before I could stop myself. My mouth also forgot its place as I jumped out of line. Everyone's head shot to me as my eyes widened in realization at what I'd done. Adrian, the other twin, walked up to me before punching me right in the stomach, I doubled over instantly. Feeling the sting in my slightly healed back. "I know you... You were publicly flogged only two days ago." God I hate this guy. "I also have it on good authority, that you openly spoke out against our rules and regulations in yesterday's class." My head shot down the line slightly to see Erin, looking a little frightened, her mate, the beta to be was looking at her, nodding his head in reassurance. "You traitor, you grassed on your own kind?" I yelled at her before feeling a fist connect with my cheek. My head whipped to the side from the force, while my class members gasped. I'm so done with this treatment, right then, I wasn't in charge of my actions. My fists curled up and my stance became a lot more defensive. My head snapped up to the alpha to be, and I looked him in the eye. "You don't know the meaning of the word disrespect." I suddenly hurled my fist towards his head, which he easily dodged, but my foot came up and kicked him instead. He stumbled backwards from the force with wide eyes. "You... you Actually hit me!" He didn't even sound annoyed, more shocked. Everyone in the hallway was watching, waiting for the alpha to do something but instead he simply stood up straight, regaining his composure. "I think everyone should get back to class." He began to walk away, following his sister when I called him back. "What about Nick?!" "Simple, He's my sisters mate. He now belongs to her." Argh, he's not an object. "He's not her property." A chuckle left his mouth, before turning his back to me again. "All humans are property." A short while later everyone made it to science class, our teacher Mrs Mathews is mated to the lycans pack doctor, she also now has a four and two year old with him. She was one of the first humans to be cohered into a false relationship. "What were you thinking young lady?" I rolled my head at her before looking at the empty seat next to mine. Nick was with that stvpid wolf girl right now. Being changed, I'm so angry it's ridiculous. "I was thinking, this guy is being a prick. Did you hear him? 'All humans are property.' It's bull shet." I looked up and the whole class looked at me like I had three heads. Talking shet about wolves is one thing, but talking about an alpha is punishable by death, attacking an alpha is an even worse offense. There was then a knock at the door and in walked Erin and her band of mated bestards. "Sorry we're late Mrs." "Erin, how are things between you and bata Monroe?" She blushed, the traitor actually blushed at the mention of his name. "He spoke to me last night about trying for a baby. We need a good strong boy to take over as beta." I scoffed looking at her as she took her seat. "You guys are actually pathetic, why can't it be a girl? Those mutts are basically Neanderthals" I voiced my opinion and saw all the shocked faces around me. Calling the lycans mutts, is the same as them calling us scum. After lesson had ended the entire school was called into the hall for assembly. This is where any human who has been found to have broken the rules were punished, usually 10 lashings were goven out or something similar. "Welcome to the school assembly, congratulations to the alpha twins for finding both your mates. Now on to the business at hand, as the 5 year anniversary of the new world is coming up, we have been informed that the alpha king will be visiting our district next week, this is very exciting news. We want you all to look your absolute best, she wolves and mated females will wear exemplary dresses made by seamstress. Male wolves and mated men will wear tailored suits. Anyone who doesn't comply will be reprimanded." The Alpha King?! No one has met him yet, he took over the throne three years ago when he turned 18. He really didn't make any appearances though, great, this month is going to be a nightmare. "As for the humans, you will be given a new uniform to wear for the visit, these are to be neatly ironed and worn to the highest standard. As for the following humans, based on your attitude this past week, you will be coming to the front and facing punishment. Tony summerset?!" Tony's head shot up as he looked around, he was in the year below but he shared my views when it came to the lycans. He slowly walked up to the front of assembly, almost instantly his top was t0rn in two and he received 10 lashings. A girl named Kara was next and she too received 10 lashings. A few more people went up slowly accepting their fate then suddenly my name was called. "Dylan Riley." Inside I was terrified but I simply shrugged my shoulders, I guess I did kind of expect this. Although I'm not sure if my back can take any more damage. "You attacked an alpha, correct!" His eyes bored into mine as I bowed my head submitting to his authority. "Technically, no." Everyone in the school gym looked on in fear, as my head moved to the front row of the wolf side. Adrian sat, with a werewolf girl in the year below, her name was Jana, I guess he found his mate. Nick and Arya were no where to be seen though. Adrian gave me a shrug as if to say he didn't tell, before smirking at my comment. "He hasn't officially taken the alpha title yet, so he's just..." i looked at the principle and noticed his eyes black and his claws out, he was in what lycans call a half shift, triggered when the subject has become angered. He turned to two security wolves and gave them a nod, Almost immediately i was forced onto my knees, my arm was slammed on a table and held in place by one wolf, while my body was held in place by the other. "Ok, I don't think this is needed, I have alpha blood, a stvpid human girl can't hurt me." My head snapped to Adrian who had stood up in front of the school to stop what was happening. "Nevertheless, humans need to know their place." With that the pressure on my arm increased as our principals hand pulled my sleeve up before a long claw punctured my skin. The searing pain shoting from the fresh wound had my eyes scrunched and my fist clenched, I bit the inside of my cheek hard instantly tasting blood, however no sound left my mouth. He continued to write, using my skin as a canvas and his claws as a marker, it went on forever, my vision blurred slightly at one point as I turned my head away. After minutes of torture, he was done and the pressure on my arm eased, instantly I snatched my arm away, hissing through my teeth at the pain. I was about to scurry off stage, when I was roughly grabbed yet again, my arm being held in the air by the principal while my feet were inches off the floor, blood dripped from the wound and the pattern he had made was on show for everyone to see. Loads of people gasped, even the wolves looked slightly horrified at what had happened. "This is what happens when a human decides to speak out. I can promise, anyone who so much as says one word about our way of life, will have the same punishment." My arm was starting to seriously ache from being held in the air for so long, and the lack of blood flow to my suspended arm was causing me pins and needles, still I refused to make a sound. I held the tears back and I bit my cheek harder causing more blood to fill my mouth. "That's enough Bradley!" Adrian growled, he was still stood up and looking at the scene in front of him. His eyes hard as he stared at the principal a low warning growl erupted from his chest which had the head teacher gulping, he quickly let go of my arm causing me to crash to the floor. A small cry left my mouth as I hit the hard floor. Immediately I scrambled away, my foot just missed the high step leading to the stage and I fell, waiting for the impact of the ground, but it never came. Two strong arms wrapped around me catching my weak body causing me to look up, my eyes widened as I noticed Adrian had caught my falling form. "This isn't part of the human punishment program!" Adrian growled causing me to tense in his grip, I pushed him away from me before fixing my uniform top. The room was deadly silent, taking in the scene in front of them, while I stole a glance at my forearm. Carved into my skin by his devastating claws were two words, words that would most definitely scar my body for life. 'Human scum' "Lessons must be learned, she received lashing merely two days ago, and clearly it had no effect on her." Another growl left Adrian's chest as he stepped on to the stage, I wasn't bothered though, you would think I'd be ashamed but I simply smiled slightly. I fixed my sleeve a little so it wouldn't rub on the fresh wound before speaking. "It doesn't matter," the whole room looked at me shocked by my attitude. "I would rather be labeled human scum, than have any resemblance to your kind. I'm proud of what I am, how many of you can say that?" After my amazing little speech, I walked right down the middle between the humans and lycans and out the door. No more compliance, I'm going to get away with as much as I can without getting into too much bother. There will come a day when the lycans power will fizzle out. When it does I'll be ready, I'll be waiting for the day we take our world back. As for the best part about my plan...

No one can stop me.

"Ouch, not so hard." I seethed as the school nurse cleaned my new wound with antiseptic. "If you had of just kept your mouth shut, this wouldn't have happened." I turned to my right looking out the window at the few clouds that were floating in the blue sky. "Like I said, I'm proud to be human, and now everyone knows what I am." I clenched my fist together as the nurse began wrapping a bandage around my forearm. It had been a good few hours since the incident in the hall, and I had been forced to come to the nurces office after I had tried to clean my wound by splashing it with water from the tap, it also refused to stop bleeding. "You are impossible. Can you please just try and stay out of trouble? For one day, that's all I ask." Our school nurse is a wolf, she's one of them. However she hates the way they treat us mere humans, she thinks we should all just live in peace with equal rights. Like that would ever happen. "All I've done is stay out of trouble, but you are just going to humiliate me anyway, so what's the actual point?" "The pack were discussing a public execution, Dylan. You need to walk on egg shells from now on, not just for you but for your family as well." No ones been publicly executed in over 4 months, I'm flattered they're considering it. They only execute people who they believe are the biggest problems to society. "Well then... I'm flattered." I chuckled, before looking at the patch job. 'Huh, not too shabby.' I quickly stood up from the human nursing station and pulled the sleeve of my shirt down covering the evidence of ever being hurt. "This is serious!" I just gave her a blank look before leaving the room. On the way out I heard her call back to me. "Please just think about it." I gave a clipped nod as I walked away wondering how I'm going to tell my mom about this. Later in the evening... "Dilly why you say that?" Freddie looked up at me with a mouth full of bread. "Don't speak with your mouthful!" My mom scolded him as a bashful blush made its way to his cheeks. "Sowwy mommy." His reply was muffled as he swallowed the last chunk of food. "I said it Freddie, because it's the truth. The wolf race are a pathetic excuse for..." my mom cut me off with an extremely stern look. "Dylan! They have ears everywhere, one more word out of you and it's your room." I scowled, my hatred for the Lycan kind growing stronger as each day passes. "What more can they do to me, lash me? Beat me? Brand me? They've ran out of options." I stated slamming my hands down, then severely regretting it as sharp pain shot though my wound. "What was that?" My head shot to regard my mothers worried expression. Her eyebrows were raised and her eyes were dull and judging as she looked at me. "Nothing, it was nothing." I quickly took my plate in my hand and began to walk to the kitchen. "I'm not really hungry, and I have homework to do!" My mom caught hold of my forearm causing me to drop my plate suddenly, I watched it slowly fall before shattering on the floor. I retracted my arm quickly and turned to Freddie. "Stay there and don't move until it's cleaned up ok sport?" He just nodded with wide eyes, I turned back to my mom and noticed her curious stare on my arm. Her grip shifted to the other side as she turned it around before pulling my sleeve up. The bandage was showing and a bit of blood was seeping though after the wound had been disturbed. "What the hel happened?" My moms eyes widened as she began to fumble with the bandage. Before she could unravel any of it I snatched my arm away. "I had an accident at school. No big." I began to gather the large pieces of the broken plate up ready to put them in the bin. "What did you do Dylan?" She looked at me with pure worry and only then did I realize what the wound must look like to someone who didn't know. "For gods sake! I didn't do it to myself! I got publicly punished at the assembly alright? It's no big deal." Her face dropped instantly and she stepped towards me, causing me to step backwards. "Mom, I'm ok. So back off will you." "What did you do? I've never known them to cut someone's arm as a punishment." Her shock and accusation was evident in her voice and I sighed heavily. "I spoke against the alphas son." I may have hit him too, but I wasn't going to divulge that part to her. "It's not one big cut, mom, it's a brand, 'human scum' carved onto my arm." "They've branded you now too?!" My eyes rolled at her hurt tone as I went to get the dustpan and brush. "You're so much like your father." A sigh left her mouth as she spoke, running a hand through her hair, while I quickly swept up the little pieces of the broken plate. "You've had a new uniform delivered. It's laid out on your bed. Dylan, Please just try and stay respectful in the future, I don't want my daughter to be completely mutilated. Although you're not far off." "Gee, Thanks." I then walked over to my little brother Freddy before blowing a kiss into his neck and hearing him giggle. "So sport, how's school going?" "It's ok." He shrugged before going back to coloring a dinosaur picture in. "Well that's good, stay out of trouble, ok little man?" Heading upstairs and into my room, my thoughts wandered to the permanent graffiti scar very slowly healing on my arm. Disgusting beasts. Think they own the world because they're faster, stronger and can shift. Pah. If you ask me they are not all that.

The second I walked into my room my mouth dropped open. On my bed was some grey pants laid out neatly, which wasn't the surprising part, no, what shocked me was the grey high neck no sleeved button down shirt, every single set of uniform had sleeves except this one. They've done this on purpose those, mutts. They want the world to see my arm and know what a disgusting creature I am. They want the world to know that I, Dylan Riley, am nothing more than 'human scum'.

During the last week, I've been horrible, in class I've been loud in voicing my views, I've insulted at least everyone to some degree, I didn't care about the consequences, and I certainly didn't think about them. I haven't seen Nick at all since he was claimed, and to make matters worse today was the royal visit. Oh yes, werewolves and mated humans alike were spending every waking minute preparing themselves to meet his royal majesty, king of the wolves. Unclaimed Humans however would rather stick pins in their eyes. "Dylan, get down now... you're going to be late." She was right, I was dawdling this morning, I really couldn't be bothered today, I gave myself one last look in the small mirror and sighed when my eyes met my newly uncovered brand. It had bad bruising around the letters, and was still extremely tender to touch, it was definitely healing now though. I made my way down the stairs and came face to face with my mother who was seeing to Freddie, she was helping my brother get his coat on when she turned to me. "You ready sport?" Freddie nodded his little head at me and smiled while I quickly slid my shoes on. "Just Remember, the alpha is bad enough, Dylan, please, please don't do anything to anger the king." My mother stopped us from walking out the door to tell me something she had been telling me continuously for the last couple of days, it was almost as if the entire human population of our district was expecting me to do something stvpid. "Try and have a good day." I rolled my eyes but nodded, even I know not to push the king, he could kil me in the hallway like it was nothing. In fact I plan on staying out of his way for the entirety of the day. "We will see you tonight mom." I stated before me and my brother began our walk to school, his little hand clutched my own tightly as we went. Usually Nick would be with us, as he lives next door, well he used to, now he's residing in the main pack house. I quickly dropped Freddie off at his school and watched him get the wolfsbane neutralizer before walking into him building giving me a small wave before he went in. With my new scar on complete show, and my figure being complimented by the skin tight shirt I was wearing, I sauntered down the street to school, I gave my name and year in and took the wolf's bane neutralizer injection with no problems at all. It was finally getting into school that the problem occurred. Walking through the halls I was met by many looks, some of pity some of disgust. You see every single non mated human in the school was wearing a long sleeved version of the uniform I was given. All the Wolves and mated couples were scattered around in fancy floor length dresses or tailored suits. As I turned the corner I noticed a couple, now this couple happened to catch my eye the most out of all of them because it consisted of Arya and Nick, eating each other's faces off. "What the hel!" Nicks head shot to me as his eyes widened. He too was dressed in a tailored suit, a navy blue tie hung on his neck to match Aryas dress. Why was this happening all the time? It's always my friends that get completely brain washed. I shook my head in disbelief before turning my back on him. I heard his fast footsteps behind me as I rounded the corner. "Dylan?!" He ran right in front of me, stopping me in my tracks, making me drop my bag off my shoulder and almost causing me to bump into him. "Let me just explain..." "Has she marked you?" I mean you could almost see it in his eyes, she had marked him, and knowing the way life goes he's probably even mated with her. "Actually... Don't even answer that." I aggressively picked my bag up off of the floor and stormed off down the hall. "Dylan, just listen to me, Erin was right, it's so hard to resist your soulmate, and Arya is actually ok once you get to know her." I just kept walking, he caught up to me walking beside me but it didn't matter, I completely ignored everything and everyone. 'I'm so not in the mood today' getting into class was good though, I said hello to Mr Foley and took my usual seat. Nick sighed then took his bag off ready to sit next to me, but I snapped before he had the chance. "Traitors and mated idiots sit on that side of the room." I didn't look him in the eye as I pointed to a seat right at the front of the classroom on the opposite side. His eyes widened as he turned his attention back to me. "You can't be serious Dylan." I gave him a blank look before grabbing my book out of my backpack, I placed it on the desk then began to write the date on the top line. "I've sat in this seat for as long as I can remember." I ignored him, his voice sounded sad and shocked. "Dylan? Wait! What is that?!" Before I could react Nick had grabbed hold of my branded arm and turned it to see the letters. "Oh my God! What happened?" I snatched my arm away from him and shrugged as I continued to write in my book before grabbing my water bottle out of my bag. "The principal happened, it was my punishment for speaking out against Adrian and Arya. I wear it with pride." He just held a complete look of disbelief. "You spoke out against them?" I shrugged, what did he think I'd do. "It's no secret that I despise this stvpid new world and the mutts that control it. You were my friend, I wasn't going to let them just take you without saying something, although that is exactly what you seem to have done. Enjoy the view from your new seat!" "Don't be like that, Dylan, I'm your best friend, I'm sorry about your arm, but..." my eyes rolled inside my head at my friends words. "Anything with the word 'but' in, isn't an apology, it's a rationalization." I took a drink of water from my bottle and kept my eyes facing forward, ignoring his every attempt to try and talk to me. "Dylan?.. Dylan?... Do you know what? Erin is right, if you push us all away you won't have any friends left." He huffed before walking over to the empty seat and sitting down, I could feel him glancing up at me every now and again but I didn't respond. "Good morning class, please settle down." He looked at me then at Nick and frowned, we've never sat apart, we were friends before the new world even began. I just shook my head telling him to forget it. "So... as you know the king will be arriving in a short while, but until then lessons will go on as normal." Its funny seeing teachers in the same uniform your wearing, mr Foley and his wife are the coolest. Human teachers and doctors only have slightly more respect than we do. Because of Mr Foley's status him and his wife have better access to food and drink, Mrs Foley is cool, sometimes she even makes sure mr Foley brings some in for me. Ya know, coz I'm their favorite student. It's not in a weird way, it's just they were friends of the family before the new wold took effect. Mr Foley and my dad were buddies from high school, so it goes without sayin really. "All the mated humans will be at the front of each years line again, after that you will all be placed in status, Nick, as your mated to Alpha Arya, you'll be at the front of your line. Dylan as you have been branded..." his voice trailed off as he looked at me. "Yeah yeah, I'll be at the back of the line behind everyone. I get it." I huffed, moving my sight towards the window once more. "I am sorry." I turned to face Mr Foley again, he looked genuinely upset and that look of pity wasn't something I wanted to see. I gave him a clipped nod then turned away again. "Anyway, on to the subject matter, 'Of Mice and Men, page 64, Nick why don't you start us off with the reading."

"Of course sir." Nick began reading the book but I switched off, today is going to be a long day. After almost an hour and a half of reading comprehension, the bell chimed signaling lunch. I shot up and out of the classroom before anyone could say anything. Today, I was avoiding drama like the plague.

I wandered the corridors straight to the lunch hall. All the people I would normally hang out with we're all mated so I grabbed my lunch quickly, and sat down at the end of the human table. Let me lay the lunch hall out for you. On one side of the room you have two long rows of tables, with simple benches that make it look like prison, on the other side of the room you have multiple round tables with fancy chairs. Yup you get it. The humans sit at the prison tables and the wolves and traitors sit on the fancy tables, they get fancy food, fancy drink and most importantly they get pudding. what I would give to have some pudding. "Dylan can we just talk?" Nick quickly took the spot next to me as he set his lunch tray down. I looked at his food which had been placed on a ceramic, circular white plate. God that looked good. I sighed knowing he was going to talk anyway. "Fine, you have two minutes." I used my fork to take a bit of pasta off his plate and shoved it into my mouth. God that was good. "After I left school, I was taken to the pack house with Arya, and I really got to know her. It took a few days for me to finally accept being with her, but ever since life has been ok, and the sax... well that's a whole other story." Eww, I didn't need that mental image in my head. "I'm glad your happy." I stated before deciding I had no appetite. His face held shock before he sighed in relief. "That means a lot Dylan, I mean you know that your opinion matters to me." I cut him off before he could say anything else. "I said I was glad your happy. I didn't say I approved of what you've done. You've basically turned into one of THEM, I can't ever forgive you for that." He looked hurt, but I couldn't care less about his feelings. He placed his hand gently on my arm and went to open his mouth when a growl sounded out. All heads whipped to where it came from, Arya was stood holding a glass of soda and a plate, she was looking right at me and Nick and I would totally be dead if looks could kil. Nick quickly retracted his hand, his whole face fell and you could see sorrow flood his irises. "You sit with me now, get away from that, that... scum!" Wow, Nick was such a lucky guy. NOT. "You heard her. Get away from me, go sit with your new friends. I'm happy for you, and I understand where your coming from, but don't come up to me again and pretend you didn't betray your own kind. Don't pretend you didn't betray me." I shoved a little bit of food into my mouth before standing up and walking out of the cafeteria, leaving my tray on the table. I was walking through the hallway to the classroom, you see I decided to spend lunch with Mr Foley in his room, when I happened to hear voices in the corridor. "Is it wise for her to actually be present when the king arrives? Surely she could be placed in the dungeons, it might actually teach her some respect?" My principal was speaking to the alpha of our district, huh, if I stayed and listened do you think they'd notice, maybe they could smell me?! "Everyone is to be present, if the Riley girl does one thing out of line she will be dealt with severely, child or not. That girl has been a blight to the district since day one, she's dangerous, if she puts one hair out of place I will personally break her into submission." Oh shet, they were talking about me specifically, and they mentioned the dungeon, that's not been used in months. Normally I would have listened in more but something about the entire situation didn't sit right with me, all of a sudden, I was on edge, and simply wasn't interested in the slightest in hearing how my misery was to be enhanced. I backed up slightly before turning around and bumping head first into one of the hottest man I had ever seen. I lost my balance immediately and fell straight on to the floor letting out a small grumble in the process. His eyebrows knitted together quickly and his breath hitched in his throat as he looked upon my fallen state and gasped. "Mate!" He whispered, his eyes fixated on mine. Now, I had seen and heard that many times to know what that means, I gasped before taking a step back. 'No, no, no, no, no. This can not be happening.' He growled slightly before stepping towards me. Oh Shet!

Hi i want to know which is the best approach to lean java effectively. I do not know they way that i am doing is right or wrong so i am asking for some opinion and suggestions from you.
The way i am doing is
Currently, i am watching video courses from https://www.udemy.com/course/java-in-depth-become-a-complete-java-enginee but i guess i am lacking some practical exercises. I am not saying the course doesn't have enough exercises. I am saying i have to do more.
I also read some JAVA books like Effective Java(3rd Edition) and some oracle documentations.
Mostly i spend seven or eight hours a day to learn from it. [Morning 3 hours/Afternoon 5 hours].
Only watching videos is not the best approach to learn java and i got really bored.
I have done with some basics like classes, objects, variables, methods, conditions, loops and strings.
Topics i need to work on are recursion, OOPs, DSA and some advance levels like nested classes, generic, enums, functional programing(lambda). multi threading, performance optimizing, frameworks, Spring boots and so on....
So that here are some approaches.
1). Keep following the course until finish.
2). So should i do parallel approach? watch video from udemy course and also do some exercises in Hackerrank and leetcode? In here i would like to mention the following. 2.1) During doing some problem solving, should i use AI or try to solve on my won. I understand using AI all the time is not a good approach. So i try to solve my own first and take some time googling or stackoverflowing. If i cannot think anymore i use chatgpt or discussion to get the answer.
3). Watching only udemy video course is boring and I also want to spend some time and build projects but i do not know how to build and what to build. So should i skip some fundamentals and start doing projects and go back to fundamental when i get stuck?
4). Copy other people projects. I look around some udemy spring boot course https://www.udemy.com/course/spring-hibernate-tutorial/ and code along with the instructor?
It is not only about JAVA but also about every type of programming languages i want to learn.
Thank you so much to everyone who give feedbacks and suggestions for me. i really appreciate your time and ideas. Thank you.

DEMOGRAPHIC: Female, hispanic, first generation student, second generation american, single mother
Planning on civil engineering ACEDEMICS: - for high school I have a 3.3UW3.8W, i’ve only taken honors courses at my high school. -1120 SAT (going optional) -i’m doing a dual enrollment program so when applying i should have 9 classes hopefully around similar UW GPA. (courses going towards a MechE Tech AA)
EC: -i worked freshman, sophomore and junior year locally -last summer i did this volunteer thing too help the class of 27 get comfortable in the hs -i have been talking with a lot of engineers and i’m hoping for a shadowing opportunity this summer. as well as volunteering for the arthritis foundation and BnGC. - also play dnd and planning to dm -smaller hobbies like reading, writing (which i share), and sporadic redesigning of road/buildings
SCHOOLS: i know my chances are pretty dookie with those stats alone but these are the schools i’m wanting to get into (in order)
WPI Umass Amherst Uconn
ESSAY: this is where ima COOK.(i hope) - so i got diagnosed with RA in 9th grade. struggled a lot in school + socially because of it. felt limited (admission should say womp womp) -had realization in end of 10th on how i shouldn’t let it stop me and advocated for a 504 and an alternative plan for junior year which would help me succeed. (admission should say wowww) - after securing reliable rides to dual enrollment classes at my CC, got chance to breathe since the workload was much better for me. made me have another moment and realize i want to dedicate a my career to helping people with disabilities in the best way i can. becoming a civil engineer😎 (admission should be drooling on my essay atp)
Another things i might want to add is i wasn’t able to do a lot of EC since getting transportation was near impossible for me reason i would add: my mom was getting her nursing AA while i was in freshman-sophomore year. and to lighten her load i had to clean the home a good bit. (which made the whole womp womp worse, but if i add that am i chasing pity?)
In conclusion, would my essay save me or am i gonna need to look at more reachable schools? ALSO PLEASE CHANCE ME 🙏🙏

I am an 19 year old from California and made the terrible decision of attending a FAR out of state university as a pre-nursing major. I am now in 10k debt and it is very brutal to move across the country from time to time. Long story short, I fucked up my first year of college by getting mostly Cs & standing at a 3.09 GPA. Even though I have met all the requirements to apply for the program this upcoming fall, I have very high doubts about getting in. It’s very competitive and everyone that I know who got admitted this last round had 3.6+ GPAs. Should I just give up and come back home? Should I just tough it out and apply anyways? I’m so lost. I’m also scared to tell anyone about this including my family. They have all supported and sacrificed so much for me, the thought of failing them crushes me.

This is a sequal to u/uktabi's Yulpa GF one shot, since he seems to LAZY to make a sequel. (that's a joke, god I'm so tired its almost 3am as I write this god fucking dammit why did i do this to myself)
He was originally inspired by u/FrostedScales' art., (God, please make a cover for this I want one so fucking baaaaaaad)
​
I make my way into my house, a small part of me is hoping for relief from the harsh Savannah heat. Only to be brought back to my unpleasant reality of my house being just as hot and dry as the outside.
Ugh, why do I willingly live in this hellish place without AC?
I hear mewing and tapping hooves getting closer, a small blood red calf comes running towards me and runs circles around me. I extend my free hand and she readily forces her head into it, wrapping her tongue, upper and lower lip around it to return the gesture of connection. The barbs irritate my skin, but she's old enough now not to unintentionally draw blood.
I look down at the pleading eye, happy to see me again.
Ah, right. While I'm in hell she's in paradise, I guess I’ll have to suck it up for her.
I give her a closed lip smile and rub her ears.
“Hi honey, how was school?” She inflates her nostrils and begins to talk to me in learned English. It is… unsettling how accurately she can mimic almost anyone with only her nose. I am reminded of that fact as she speaks in my voice.
“Good, bunny lunch was.” I forced down an indignant laugh at the child's broken grammar. It seems Yulpa are able to understand words, but grammar doesn't seem important to them. I remember how off puttingly dense their spoken language is, they can communicate in infra sound over vast distances. Their phrases were spoken in single words, so a single “word” was a bit of a conversation. Like “Over the river” or ”up the tree is food.”
It was insane how dense their language was.
“Do you know if mom is back?” The little head in my hand nodes while still wrapped around me.
“Garden she eating is.”
I rub her head one more time before I make my way out the back door. The child quickly scampered off to do who knows what.
Out in the back I see her, draped in golden jewelry, with a well maintained main, green cloth and jewels to accentuate her natural deep red coat. She was laying down with what looked like roughage in her maw. She was absentmindedly chewing and staring off into space.
I walked over and rested myself against her side, I let her breathing rise and lower me. Being in the presence of such a large person really made me feel how insanely varied our body masses were. Despite being married and … constituting it, we had to sleep in separate beds, lest she roll onto me and I die of suffocation or all my bones breaking.
I absentmindedly picked some grass and twirled it around. “So, what's wrong?” She rolled her eyes towards me.
“Hungry.” She spoke in perfect English, I looked down at the grass I began to weave together.
“You need to get the cure.” She raised her massive head ever so slightly. “No, betrayal, life lived one way. Too late, already sacrificed too many.” She blows out her nose.
I grab more grass and weave it into the mass I was creating. “I thought I was too old, set in my ways to be married when we met. But now look at us.” I point to the child in the window clearly talking to someone on the home computer. “I’m glad you hunted me, forced me into this. If you didn't I'd still be a lonely S.O.B. jacking it to venlil stuff right now.”
She flicks an ear. “I wanted sacrifice YOU.“ She spoke.
I smirked as I continued to weave. “Yeah but ya didn't. Cause I’m just so sexy!!!”
I can feel a ruble as she laughed, I didn't notice the twitching of her neck mussels as she swung her head over to slap my head with her upper lip. “Ow!!!” I screamed at the surprising strength of the dexterous lip.
I will never get used to how she can hold me like a rag-doll with just her lip. Nor do I want to.
As I nursed my wounded pride I placed the straw hat on her head. “Besides, this is a better use for the hay than causing you pain.”
She breathed out sharply and made a sound only a multi-ton mammal can produce.
“Okay, me get cure.”
I smiled and rubbed her ears, she adjusted the hat I made for her and rested her head on the ground once more.
​
​
**\*
​
​
She seemed antsy as she rocked back and forth, she was making a loaf of herself on the ground. But she could still reach up to my face with her lips as I sat down in a chair beside her.
The waiting room of the Xeno walk-in clinic reminded me more of the vets as species of every size and shape sat in chairs meant for humanoids, or sat on the ground or in perches or, rarely, species specific chairs. Though they were a rarity, a luxury whose expense was used for the most populous non-human species in the area.
Most of which were in a separate waiting room. I saw a family of Farsul enter it, opening the door to the KolSul wing of the clinic. Most everyone in the office instantly scowled when the mother and her pups walked through. They got both the separate wing and specialty chairs as they were by far the most populous Xenos on earth. Thanks to resentment building to massive levels all across the S.C. pushing them here.
I ran my hand through her main, careful to not undo any braids or tug any of her excessive adornments.She was still shifting this way and that as we waited to be called on. I spied a venlil with a deep scowl near the door of the separate wing, he seemed to be wearing a coat. Something highly unusual for his species, especially in this climate.
“Hello uh, we are not sure you… should be seeking care here.”
I was jolted out of my observations by a young farsul attendant addressing me.
“What?” She seemed to stammer.
“We, uh, are a xeno clinic, we specialize in ailments for non humans. Since we are on a human majority planet, human specialty clinics are open here. They can give you much better focused care."
I stared at the young farsul for a few moments, I studied her nervous stance. She seemed to resemble a great Pyrenees breed of dog. I continued to stare for a couple of seconds, enough to make the awkward situation even worse before I shook myself out of it.
“Oh, uh, I’m not here to receive care, my wife is.” I run my hand through her main and look down at her. This is the most nervous I've ever seen her in all my years with her. “She recently got the cure injection and is here to test it out in case something goes wrong.”
The farsul then takes on a deeply confused expression, snapping her head between me and her in quick succession. “...You two… are married?”
I smile and straighten my back and respond in the most enthusiastic voice I can muster. “Yep!!!”
She continued her confused expression before resigning herself. “...Alright then, I’ll get her tested… just follow me.” She turns as she reads our file, we were heading to a farm outside of town to test her on some authentic meat. As we exited the office I tapped her shoulder. “By the way, I saw a venlil by the Kolsul section door, I think is planning something bad.”
She took on a look of annoyed apathy, as though it was a daily occurrence. “Oh, him. Don't worry security is on their way to search him, you'd be surprised how many expats from Scalga we get.”
She rubbed the back of her head and I could barely make out something she whispered. “Not nearly as weird as a human yupla couple, Jesus Christ.”
​
​
**\*
​
​
There, the object of my fearless and terrifyingly powerful wife's hesitation. A single skinned chicken leg, sitting on a metal table. The farsul nurse was making superficial vital checks on her as she stared at the drumstick.
Her lips were curled under her chin.
“Common honey. It's not going to bite you.” I say to comfort her. She glances at me with a look of I don't want to do this, why did i let you convince me to do this onmygodi’mgoingtosacrificeyouyousonofabit-
Her simultaneous death stare/ pleading eyes were pulled away as the farmer spoke at us.
“Eat it and get off my property, I got enough animals to take care of. Don't need two more.” He pointed to my wife and the farsul nurse, they both gave him a scowl as he turned back into his house.
She gently unrolls her lips and tentatively brings out her barbed tongue and wraps it around the drumstick. She brings it into her mouth and I hear a series of slow crunches.
The farsul nurse looks over at her medical doodad as she chews.
“Hmm, it seems everything is in order, the cure has taken and she is handling the meat fine. Just call our clinic if she seems to be having-”
My focus was pulled away from the nurse as I saw my wife's eyes light up from the taste. She looked at the ground at the pecking chicken that was so near. I could hear her imitate the clucking of the chicken, I saw it shoot up and looked confused. Before my wife coils it in her tongue and quickly brings it into her maw, I hear crunching again and a loud gulp. She looks around at the pens and she spies a pig.
She stalks towards it and I can hear her imitating the pigs, she steps over the fence and quickly grapples the approaching pig and bites down hard on its head. I can see her tongue quickly strip the skin off as her lips dig in with their own bards to force the corpse up and into her mouth.
I stare in shock at the display, by this time the other pigs notice the smell of blood and my wife devouring one of their companions and they quickly run to the farthest corner of the pen.
Before I know it the corpse is gone, she licks her lips and walks back over to us.
“-mitochondrial flux drive. As long as that looks good over the next week you should be all set!”
“I , uh, she ate a pig! D-did you see that?” I point to my wife cleaning herself of the mud of the pen.
'Yes, you'd be surprised how suddenly ravenous former omnivores get when they get their appetites fulfilled for the first time, heck I remember I ate a guinea pig when I got cured I was so hungry!’
I wave my arms about. “SHE ATE AN ACTUAL PIG AND YOUR NOT ONE BIT IMPRESSED OR SCARED???”
She turned her gaze to my wife, who was currently spying yet another pig in the corner. “I’ve seen yulpa do it before, trust me, get used to her eating vast amounts of meat.”
I looked at the simultaneously nervous and unimpressed Farsul. I was still reeling from my wife's actions, but decided to deal with them another time. “O-okay”
“Good, now I'd suggest you get her to not eat another. The farmer will definitely notice two pigs missing.” She begins to laugh in her throat. “Heh, I guess he’ll have two less animals to take care of after today heh.”
As we rode back to the clinic my wife was fast asleep in a food coma. The way she just… ate that thing so fast… I think I have to worry about being eaten now. It felt… oddly exhilarating, like when she was still trying to convince herself to sacrifice me all over again.
I think I might have a problem.

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First/Previous
“How’d you think that was going to go?” asked a voice from the other side of the door.
I lay on the bunk and stared at the ceiling; my head throbbed. The place where I’d been grazed stung whenever I touched my fingers to it. A bullet had—by whoever’s grace—scraped my scalp and traced a line from the far corner of my right eyebrow. It'd only been three days and it still caused pain. No doctors came and I was certain there would be infection—if not plain infection, then it could always be the worser: skitterbugs. I ached still. I had never fully recovered, not like how I should have.
The day of anger, as I’d begun to think of it in my mind, had caused no great ruckus beyond a few dead men. Two were Bosses, but who knew if they’d announce that as casually as they’d surely announce my execution. Perhaps they’d string me up alongside thieves. A good thief and a bad. What a riot; I deserved no thieves, of course.
What was I? Some great hero? Some idiot was more likely. I wanted misery to befall those that perpetrated it themselves and there I was, more miserable. Perhaps the wrath in my heart came from some mutation; the demon Mephisto resurrected me (so said the demon) and I’d begun to accept it. It was the reason for my poor state, surely, and the more I thought on it, the more I believed it was true; it felt true right down to my bones. The truth hurt or it was age and I rose from the cot I lay on; I’d been detained in a room beside the one I’d visited Andrew many months prior. They’d starved me, rattled the door to try and frighten me, and they’d wasted water on my head to keep me from good sleep.
I did not respond to the voice from the other side of the door and the object rattled in its frame and the voice came again, this time angrier, “Really? How did you think that was going to go? Crazy bastard! Thought you’d put the hurt on the Bosses? Thought you’d kill us at our worst? First, it’s that explosion. You have something to do with that? No! First, it was Harold’s daughter running off!” The voice on the other side of the door grew with mirth as it did with anger. “I’d seen you around town a bit. Thought the Bosses always liked you. Huh. Boss Harold mentioned you at his parties and said how you were a smart fella’, a good fella’, and there you killed him. Stone cold.” The man which spoke was a jailor that tortured me in those dreamlike days I spent locked in their prison, and he seemed personally affronted. “So first it’s the explosions; steam or dust rose out of cracks in the ground you know—some thought hell was rising up, but the Bosses put those thoughts to bed. God, what’s it with the likes of you? The explosions and now I’ve lost an eye and its because of the skitterbugs. You probably brought that on!” The voice muttered and then the door shook in its frame again, seemingly from a hard kick. I wished I could see the face of the man throwing his tantrum. “Can’t wait to see you hang.”
“So, I’ll hang?” I asked the door. There was a long silence, and I was uncertain if I’d pitched my voice enough for the man on the other side to hear me. I opened my mouth to ask, “So-
“You’ll hang.” The man on other side seemed to knock his knuckles against the surface of the door. “Or you’ll die here.”
“What’s Maron said?”
“Don’t you worry about him.”
“What’s he said?”
“Said you’d probably appreciate the punishment that we’d put on you. Said you’re a sick man. Said you like speaking with devils and people like you only find pleasure in such things.”
“So, I won’t hang?”
“Oh, you’ll hang, sir. You’ll hang if I need to do it myself with no one else. If not that, I’ll be sure to put you under one way or another. Accidents happen.” He chuckled. “Maybe you’d enjoy it, but it doesn’t matter. Whatever enjoyment you find in your tortures won’t compare to what ideas I have.”
A long silence followed, and I watched dust motes dance in the air; the place was stagnant and even a breath caused a shift in their glide. I closed my eyes and tried to remember a better time. I thought of Suzanne. I thought of Gemma. What a time to be alive. I thought of the movies, the books, the musical cartridges that sung of yesteryears. How unlucky I’d been, of course. Something had changed in me though and it was totally refreshing. Perhaps it was in realizing the evils of my brothers was that of a man and not some otherworldly force, or perhaps it was a push that came from years of terrible inconsistencies. All that living in the past and so it was. It didn’t matter—the past. I’d been so busy with it that I’d been in a constant state of unliving. I’d known that always, of course—something new had come.
“You dozing off in there?” asked the jailor.
“Nah.”
“Good. Stay awake or I’ll be forced to stay you awake.”
I’d been reborn with a rage, justified or otherwise, and it was felt all over. It was a wild compulsion. All that time and it had been me that was brought back.
The wound on my head throbbed and I prodded it with a finger and brought the finger away and examined the digit; it was dried well enough, and I did not smell infection nor were there any of the accompanying symptoms of a fever or hallucination. I was me, through and through. For now.
The door banged. I didn’t bother an answer and the door banged again.
“Who’s there?” I asked, surprising myself with the sarcasm.
“Why’d you do it?” asked the jailor.
“You wanna’ ask me about it now?”
“Tell me.” The voice on the other side of the door was serious entirely.
“Bah!” “Bah to you! Why’d you do it?”
“Is there a reason to explain myself? If you knew better the things I knew, would it get you to unlock that door and let me walk free? Would it change your mind even?”
The jailor caught a laugh before responding. “Can’t say it would.”
“So, what’s it that you want? You won’t understand me, and I don’t think I’ve got the energies of persuasion to try.”
“Try.”
“You like the Bosses?”
“They’re okay. Keep me in work anyway. Keep people safe.” I slumped forward onto my knees where I sat and placed my elbows on my knees and watched the crack at the base of the door on the other side of the prison cell. “What’s it matter if they keep you in work? Think they care about you anymore than what you represent?”
“Huh?”
“I mean, you keep riffraff down and they like you for it. I wonder if they know you. You ever get invited to the feasts they hold at the hall? You ever worry about your water rations? You ever wonder why it is that so few of the women or men invited to the hall return? Children too, now that I think of it. They’d call those captured criminals, I know. Those brothers—the sheriff is to blame too—they’re bastards. You know they are.”
“Is that so? What’s that make me? A bastard too?”
“By proxy maybe.” I dryly chuckled. “What’s it matter? What do you want outta’ me anyhow? Some gratification? Some confession—you’ve gotten that already, ain’tcha? Maybe a repentance? Why don’t you call one of those Bosses on down from their throne and have them here on the other side of the door so I can apologize? Or call Lady and I’ll get her to channel some message to the afterlife and I’ll plead for forgiveness. That what you want? Now I’m a bad man and I know it, but it ain’t for the reasons you believe. What you want is belief that there’s a man under the skin of the monster you’ve projected? No, I won’t shoo away your boogeyman for you. It can’t be done, not from me.”
“You talk big for someone in your predicament. I like how you talk so holier. Like you’re talking down on me. I just wanted to know what made you want to go on a mad-killing spree the way you did.”
“Mm.” I cupped my hands together; as it was, my left knee shot off with pain and I tried to massage it to little comfort and stretched it out straight from my body. “When violence keeps you bound, violence is necessary to free yourself. That’s all I’ll say about it. If you hang me, then hang me. Spill my guts out for the birds and put a sack over my head so you won’t be sick by my face.”
“You’re a mouthy pig.”
I listened to the jailor’s footfalls disappear down the hall and finally it was totally quiet and all I could hear was the throb on my head. Lucky or unlucky? No, it wasn’t luck. I’d been marked. I was the payment, and I knew the price. The demon had my soul. Whatever protection it afforded me, I intended on using.
The image of that room continued over in my mind, with the peasantry (that’s what I saw them as then) knelt in front of the Bosses and the wall men, with the intense blood-smell, with the surprise on Maron’s face. Billy’s face. There was still a part of me, however small, that wanted to plead with him to change his ways. That wasn’t the part that welled up in me then though. The piece of me that wanted to see him die was what took over. It hadn’t been Maron that fired his gun; he’d still been fighting with his holster. I’d only taken a step in through the door and a spray of gunfire from one of the wall men’s rifles exploded and I was sure I was dead because I fell, and my vision went white. They should’ve put me down then.
I didn’t come too fully until I had a few goons on me, hauling me upright roughly under my arms. Maron didn’t say anything at first and those wall men took over; they shouted that I was alive still and I felt a hot gun barrel against my cheek.
“Stop!” shouted Maron. The Boss Sheriff stepped forward with his stilted gait and looked me over thoroughly. The gun barrel fell from my cheek, but they held me still; it wasn’t like I planned on fighting. “You got uglier,” said Boss Maron, “Really ugly.” His left eye, afflicted by the skitterbug infestation, had gone dead white with only the faintest trace of an iris; it dribbled pus.
I held his stare to the point that my eyes watered—whether from anger or sorrow or both—and my muscles tightened like an animal threatening to pounce. It was a ridiculous display.
“Lock him up,” said Boss Maron.
So, I was locked up and those uncounted days I was mildly tortured: sleep deprivation, pummeling, and sometimes they spit on me. It could have been worse. I’d seen worse.
The cell was numbingly quiet, and I continued to massage my knee, continued in thinking about how investing so much thought with the past twisted any future of mine into a dismal satire.
I could not tell how long it had been without sunlight and the jailor returned (he was bulbous and fattened and old but very strong—it could be sensed in how he carried himself) pushed through the door this time with a tray of diced potatoes, steamed but cold, and a metal cup of water. He sat them on the floor, stared at the tray there with his one good left eye, and it was like I could read his mind as he looked at the food there. He could destroy it; he jerked from the tray without saying a word to me then disappeared behind the door he closed. The jailor remained there outside.
Pride swelled in me momentarily before I pushed whatever silliness that was and devoured the food and drank the clear water. If it was poison, so be it. If it was poison, then all the problems of the world would disperse.
Again, the jailor pushed in through the door and bent to remove the tray and I was struck by the immediate thought of strangling him. So, I tried and threw myself at the man.
My hands felt the scruff around his throat, and I pressed hard with my fingers on his Adams apple. He’d lurched forward to lift the tray and he immediately came up with force, throwing me off him; my nails raked his cheek as I scrambled for purchase. He took the metal tray in both of his hands and thwapped me across the head—it rang, and I was stunned while he lifted back his right hand in a swing. In the dizziness, I momentarily caught a glimpse of the holster on his left hip and reached out dumbly for the revolver there. A meaty smack could be heard, and I didn’t even feel it when his fist met my face the second time. My head rocked and I fought to look upright, and his hand came again, and I put up my own hand in return; it was pushed away, and he continued at me, muttering epithets he found useful.
Once he was heaving and spitting, he left me on the cot and directly before slamming the door, he mentioned something about violence and how if I liked violence so much that he’d show it to me.
I nursed myself to sitting right-up and though adrenaline kept the pain away, I felt my face bruising already. There was no way for me to inspect the welts his hands had left, but I could guess their places by touch and how they thrummed with my heart.
Two days passed, if I counted them by the visits from the jailor and then Maron made his appearance to me, and I was surprised to see him with a leather eye patch over his left eye; he seemed ill on his feet and the jailor, though the man was there, did not move to stop Maron from entering the room and relieving me of my prison. He and the jailor roped my hands together in front of my pelvis and I didn’t fight.
Boss Maron stank of infection and yellow oozed from beneath his eye patch and he kept his cowboy hat pulled snugly over both his ears and did not speak so jovially—there were no crude jokes at my expense. A warmth radiated off him. The Boss carried my shotgun with him but made no remark on it. He marched me from the prison, and I met daylight, and it burned my eyes while I stared up into the reddish sky. Dust scattered from the nearest portion of wall and caught on the wind till it was carried and disappeared overhead, and I briefly thought how nice it must be to fly.
Golgotha stirred as ever, and people spoke loudly and candidly as I passed them by. Words came my way from passing faces like, “You kissed the devil’s ass!” or, “You sure are a monster, look at you!” and Maron pushed me on with the gun at my back, and I wavered on my legs like I was without any control.
“Is it true?” asked Boss Maron, “Did you kiss the devil’s ass?” He tilted the shotgun casually on his shoulder and kept me ahead of himself. He was taking me to hang—and making a big deal out of it too. “I know how you like to speak to them. The demons. I know how you conspire with them. I told them all how you do. Now they know I was right.”
What a rotten town it was, and it smelled like it. The atrophied muscles and diseased infections of those fine folks emanated in the air, flies buzzed around my head, bloated and doubtlessly happy from whatever corpse they’d sprung from.
“Say somethin’,” said Maron.
“What do you want?” I asked, watching my footfalls, ignoring the screeches of those on the sidelines; he marched me through the runways, past the onlookers which saw me with faces of twisted hatred. The tension was palpable—I could feel the venom off the eyes of those that watched. Blood red eyes which judged carelessly.
“I want you to say it,” said Maron; I felt the nudge of the shotgun at my back again and I stumbled forward, caught myself, carried on, “I want you to admit it to me. You’re like a mutant, ain’tcha? No better than any other monster. I knew it all them years. I seen it.” We took an alley and cretins followed behind; wall men flanked Maron and on either side of the narrow stretch there were faces made even with the wall, pressed there like they were afraid to be involved.
“Whatever you say, brother.”
“Don’t,” hissed Maron, “Don’t even.”
“What?” I spat the word, “Afraid they’ll treat you differently if they all know how close we are?” I felt the gun barrel press against my back, and I yelped out the words, “Hey! He’s my brother! My baby brother!” The barrel jabbed me in the spine, and I spilled forward, catching myself on one of those nearby faces. It was an old woman. She shoved me from her, and I flailed across the ground after trying to catch myself with my bound hands. Dirt met my face and exploded around me. I laughed, blinking through the dust. I spit too. He couldn’t kill me. Whatever black magic there was in me—bequeathed by Mephisto—refused me death. Maron lifted me with the help of his wall men, pinching the coat around my throat with his fist. He shoved me on, and we continued.
“You smell that?” I asked Maron.
“Stop talkin’. You might not be a man, but you’ll die like one,” he said. The wall men around muttered, and we took the way to the front square; already there were looky-loos gathered, throngs of them not at all bashful to see the day’s line-up—it was just me. The platform was emptier and that was good (Frank, Paul, and Matt looked naked without their eldest brother). Those Bosses which remained looked drunk as they did for any other execution. It was a good day for it. Warm. The stink of the crowd was worse and as those gathered parted for my entourage, the warmth of them cloistered us like the blood of a wound.
Even through the vile aroma, the smell of rotted poultry rose like nothing else. “You don’t smell it then?”
The roar, a cacophony of the damned souls stolen, shook the ground and the air changed. A dragon—Leviathan.
Along the wall which old skeletal corpses hung against dried blood stains from hook-chains, men and women scattered the length of the parapets with their weapons. Gunfire came and one of those atop the wall shouted, “Artillery! Dragon! Big guns!”
There was fire in the sky and the creature circled overhead and its wings beat the wind like mad; those organic ropes that hung from its body took on horrid shapes with its movement in the high noon sunlight.
Screams filled the air as the square erupted into panic. I dove into the sickly crowd; among the loudness, the horses which were lined by the big door fought against their ties and bolted across the square. Arms and heads disappeared beneath those dashing hooves, and it was not long before people were trampling people and in a quick glance I saw the Boss platform came down in splinters as the horses rushes it. Blood slickened the feet of many as they rushed to the buildings adjacent the square—what a small protection that’d be against Leviathan. A wall man went stumbling over the wall’s ledge and his body met the ground beneath the hanging corpses and he didn’t get up.
In the wild fray, Maron fired the shotgun into the air, and I briefly thought of where the pellets might fall.
Finally, artillery fire came and put a hole in the creature. It wavered in the air, its head lurched downward like it might pierce the ground and it pulled its long neck back and blew flames across the buildings. The heat was immaculate. Rotted chicken filled my lungs.
“There’s more!” shouted a wall man above, “Running across the field.”
The crowd grew more enamored with escape; there’s no good way to say it—blood frothed around our heels as I was shoved through the avenues of elbows, rocking heads, plunging knees. I pushed on, shielding myself with my bound hands as well as I could. I kept my head as high, and felt scratches reach my throat—doubtlessly those which could not continue—nails and fists came from every direction. In the ephemeral madness, I too screamed and it did not stop until I spilled into an alleyway along the wall nearest the execution chains. I ran and tripped from the crowd, slid, and bit my tongue so thoroughly that my teeth clicked together though the tissue; my breath was knocked from me. My pants were wet from the viscera. Others too had found the opening and barreled past me. I went to my feet and panted thought the pain, through the twinge in my left knee. I took the walls for support and still, those which rushed past nearly knocked me from my feet.
Some poor child—a lean, bony-faced boy—fell in the rush and before I had a moment to reach out, he was gone. Whether he lived or not, I did not stop to know. The crunch of bones as more people spilled into the narrow stretch indicated the worst.
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Devin, the ai software engineer, made a memecoin
Hey guys,
This is perhaps the most bullish of all finds I’ve ever seen in my life
This is Shiba Inu material when it was EARLY
Every shitter says they’re looking a 1000x gain
But this coin, is unironically looking at a 1000x gain to be a reasonable 800m cap at least.
And even at 800m it’s still CHEAP! Today, it’s only 3.6m.
I’m literally all in on this coin and will continue to buy with each paycheck I have
The story requires some context:
Devin, is the latest AI Software engineer in the world
If you don’t already know, Devin, can code programs all on its own, and even make websites and read GitHub all on its own
It’s expected to take over software engineers and even take down ChatGPT
So Cognition Labs, the owners of Devin, hosted a hackathon at Stanford University, where they let the uni kids have a sneak peak of Devin
Mind you, no one has access to Devin yet in public right now.
One of the teams asked Devin, to make a memecoin
And it could do so entirely, from start to end, without human help!
It even made the socials, Twitter , website and was able to tweet autonomously on its own as an AI agent
Devin deployed the smart contracts and the coin went live, thus giving birth to an AI memecoin which lets you long both AI and the Memecoin sector 🔥🔥🔥
It also lets you basically long Cognition Labs with each news they release
There is currently only one coin in the whole world made by Devin
Cognition labs will one day be 2T on the NYSE no doubt about that
It’s inevitable that Cognition labs will IPO at 2T someday
2T is 2000 billion
So a 1% of 2T means $devin will someday be worth 20 billion MC
Relative to competition, $Turbo and $Grok ran to 100m - 200m with an objectively weaker narrative as they were basically copying and pasting from ChatGPT- the ai didn’t actually make the coin
$devin, however, ai made the coin fully from start to finish as Devin was the dev.
Thus in the short term, $devin will be priced at least 500m to exceed the highs set by $turbo and $grok given its objectively superior narrative.
Infinitely insanely bullish. Despite having big bags, I still feel underexposed to $devin.
Remember, $turbo and $grok went to 200m copying and pasting ChatGPT, and heck even someone had to code the contracts. What should the first AI memecoin that truly made itself without human help be worth?
Higher.
The ticker is $devin on Solana
CA: 7gbEP2TAy5wM3TmMp5utCrRvdJ3FFqYjgN5KDpXiWPmo
Dextools: https://www.dextools.io/app/en/solana/pair-explore2cZQ71uDTBwFZT456koEwfZDLSV736hT688A18sD3n4M
Twitter: @1stsolanaaicoin
Telegram: @devinonsol

Devin, the ai software engineer, made a memecoin
Hey guys,
This is perhaps the most bullish of all finds I’ve ever seen in my life
This is Shiba Inu material when it was EARLY
Every shitter says they’re looking a 1000x gain
But this coin, is unironically looking at a 1000x gain to be a reasonable 800m cap at least.
And even at 800m it’s still CHEAP! Today, it’s only 3.6m.
I’m literally all in on this coin and will continue to buy with each paycheck I have
The story requires some context:
Devin, is the latest AI Software engineer in the world
If you don’t already know, Devin, can code programs all on its own, and even make websites and read GitHub all on its own
It’s expected to take over software engineers and even take down ChatGPT
So Cognition Labs, the owners of Devin, hosted a hackathon at Stanford University, where they let the uni kids have a sneak peak of Devin
Mind you, no one has access to Devin yet in public right now.
One of the teams asked Devin, to make a memecoin
And it could do so entirely, from start to end, without human help!
It even made the socials, Twitter , website and was able to tweet autonomously on its own as an AI agent
Devin deployed the smart contracts and the coin went live, thus giving birth to an AI memecoin which lets you long both AI and the Memecoin sector 🔥🔥🔥
It also lets you basically long Cognition Labs with each news they release
There is currently only one coin in the whole world made by Devin
Cognition labs will one day be 2T on the NYSE no doubt about that
It’s inevitable that Cognition labs will IPO at 2T someday
2T is 2000 billion
So a 1% of 2T means $devin will someday be worth 20 billion MC
Relative to competition, $Turbo and $Grok ran to 100m - 200m with an objectively weaker narrative as they were basically copying and pasting from ChatGPT- the ai didn’t actually make the coin
$devin, however, ai made the coin fully from start to finish as Devin was the dev.
Thus in the short term, $devin will be priced at least 500m to exceed the highs set by $turbo and $grok given its objectively superior narrative.
Infinitely insanely bullish. Despite having big bags, I still feel underexposed to $devin.
Remember, $turbo and $grok went to 200m copying and pasting ChatGPT, and heck even someone had to code the contracts. What should the first AI memecoin that truly made itself without human help be worth?
Higher.
The ticker is $devin on Solana
CA: 7gbEP2TAy5wM3TmMp5utCrRvdJ3FFqYjgN5KDpXiWPmo
Dextools: https://www.dextools.io/app/en/solana/pair-explore2cZQ71uDTBwFZT456koEwfZDLSV736hT688A18sD3n4M
Twitter: @1stsolanaaicoin
Telegram: @devinonsol

Last year my dad collapsed and I (23 M) was put in a situation where I had to do CPR. Ive spent the last few years working full time to help my older mom take care of my aunt with lung cancer and my dads declining health so I didn’t finish college.
After that event, I reflected and wanted to try to make a change for other families or at least help in a way I couldn’t for my situation.
I am now 2 prerequisites, microbiology and anatomy & physiology II taking both advanced in summer, from being able to apply for the local community college’s nursing program.
Any advice for the program or what to expect in the field would be amazing!
Thank you :)

Hi, I'm an international student who is currently still in my Foundation program (Pathway to Bachelor of Nursing). I'm wondering if there's any job options that are healthcare related I can take up despite having no experience/certificate? Because I'd like to gain as much experience as possible to help with my future career.
I assume most people would say "No", and I thought the same. However, my teacher explicitly said she has students who are in the same Foundation program who are working as Residential Care Workers and whatnot while studying, and suggested that we try to get jobs like that as well as they supposedly require no certifications. But I've searched far and wide on SEEK and other job sites and found no luck due to certificate requirements (e.g. Cert III+ in Individual Support), and typically an Australian driver's license, so I'm wondering if I'm missing anything?? Or should I just stay put and only start looking once I get into My Bachelor's and go through my first placements?
I'm going to ask her about it when I see her again but I'd just like to get some general input and advice from professionals in this subreddit as well.

i’m a CNA. never been to college. i’m 25, and i want to start nursing school for RN/ i live in atlanta but finding the school/program has been a struggle! i set up a meeting(walk through) at south college this coming week, which was recommended a few times by LPNs and RNs i’ve worked with. any tips on how you chose which school worked best for you? kind of figuring things out on my own so i’m a little lost. just need a little guidance. thanks(:

hi guys. i’m a 23 year old EMT. i graduated with my bachelors of science in nutrition from a relatively nice university coming up on two years ago now. i decided halfway through undergrad i didn’t want to pursue dietetics, and wanted to pursue physician assistant. i started taking prereqs for that, got my EMT license for patient care experience, and have been able to shadow an MD and a PA for my applications.
the downside is i didn’t realize until two months ago my science GPA is too low for a fighting chance at an acceptance at any program, and it’ll take at least a year of taking and retaking science courses to bring it up to a decent level.
i make, give or take, 33k/year. i can support myself but i’m not saving and i feel like im always working. my hours are long and hard on me physically. it’s daunting looking at another year to two years of this before MAYBE having a shot at a PA program.
recently an account manager for a staffing firm reached out to me and expressed interest in recruiting me. after a 3 month training period, i would be making 66.5k/year salary, plus commission. the workplace culture does seem pretty cutthroat and competitive, though ive heard that this position has lots of room for upward mobility. it’s not healthcare - i would be tasked with finding, recruiting, and managing educational staff for a nearby school district. but it’s a livable wage, it’s a company i can grow in, and i will be making plenty to support myself. am i passionate about this industry? no, but it’s a solid job.
looking for some guidance on if i should jump ship from healthcare and go for this position or not. i’ve already passed the first interview, but there’s more to go before a job offer. i’m leaning towards thuggin it out and staying the healthcare route, but if PA (and/or nursing, an alternative route that is unfortunately also competitive) doesn’t work out, i’m worried i won’t come by this kind of opportunity again.