text: import tensorflow.compat.v2 as tf
  
  tf.enable_v2_behavior()
  
  from tensorflow.python.framework.ops import disable_eager_execution
  disable_eager_execution()
  
  from tensorflow.python.compiler.mlcompute import mlcompute
  mlcompute.set_mlc_device(device_name='gpu')
  
  EPOCHS = 5
  BATCH_SIZE = 64
  LEARNING_RATE = 0.003
  SIGDIG=3
  SEED=1220
  THRESHOLD=0.001
  
  normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)
  
  if __name__ == '__main__':
  	
  	#Set random seeds
  	
  	# Load the Data
  	train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  								'../detexify-data/drawings/', 
  								validation_split=0.2,
  							    subset="training",
    								seed=SEED,
  								labels='inferred')
  
  	# Get the class names
      # class_ids = {v:k for k,v in data.class_to_idx.items()}
  	class_names = train_ds.class_names
  	num_classes = len(class_names)
  
  	# Transform to values between 0 and 1
  	train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
  	
  	val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  								'../detexify-data/drawings/', 
  								validation_split=0.2,
  							    subset="validation",
    								seed=SEED,
  								labels='inferred')
  	val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))
      
  	# Create model
  # 	model = tf.keras.applications.MobileNetV2(
  # 		input_shape=(256,256,3), alpha=1.0, include_top=True, weights=None,
  # 		input_tensor=None, pooling=None, classes=num_classes, 
  # 		classifier_activation='softmax')
  	
  	model = tf.keras.applications.MobileNetV3Small(
      	input_shape=(256,256,3), alpha=1.0, minimalistic=False, include_top=True,
      	weights=None, input_tensor=None, classes=num_classes, pooling=None,
      	dropout_rate=0, classifier_activation='softmax')
  	
  	# Compile model
  	model.compile(optimizer='adam',
                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
  # 	model.summary()	
                
      # Training
  	history = model.fit(train_ds, validation_data=val_ds, epochs=EPOCHS)