I have these results: [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m80s[0m 796us/step - loss: 0.0206 - val_loss: 0.0250 - learning_rate: 0.0010 Epoch 2/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m80s[0m 804us/step - loss: 0.0180 - val_loss: 0.0170 - learning_rate: 0.0010 Epoch 3/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 821us/step - loss: 0.0149 - val_loss: 0.0191 - learning_rate: 0.0010 Epoch 4/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 813us/step - loss: 0.0155 - val_loss: 0.0235 - learning_rate: 0.0010 Epoch 5/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 814us/step - loss: 0.0159 - val_loss: 0.0202 - learning_rate: 0.0010 Epoch 6/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 818us/step - loss: 0.0158 - val_loss: 0.0177 - learning_rate: 0.0010 Epoch 7/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 816us/step - loss: 0.0181 - val_loss: 0.0166 - learning_rate: 0.0010 Epoch 8/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 813us/step - loss: 0.0173 - val_loss: 0.0207 - learning_rate: 0.0010 Epoch 9/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m77s[0m 780us/step - loss: 0.0173 - val_loss: 0.0218 - learning_rate: 0.0010 Epoch 10/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 818us/step - loss: 0.0156 - val_loss: 0.0202 - learning_rate: 0.0010 Epoch 11/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 814us/step - loss: 0.0163 - val_loss: 0.0202 - learning_rate: 0.0010 Epoch 12/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m79s[0m 802us/step - loss: 0.0164 - val_loss: 0.0194 - learning_rate: 0.0010 Epoch 13/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m81s[0m 819us/step - loss: 0.0139 - val_loss: 0.0197 - learning_rate: 5.0000e-04 Epoch 14/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m80s[0m 809us/step - loss: 0.0126 - val_loss: 0.0226 - learning_rate: 5.0000e-04 Epoch 15/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m80s[0m 812us/step - loss: 0.0124 - val_loss: 0.0208 - learning_rate: 5.0000e-04 Epoch 16/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m80s[0m 812us/step - loss: 0.0140 - val_loss: 0.0207 - learning_rate: 5.0000e-04 Epoch 17/50 [1m98866/98866[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m80s[0m 809us/step - loss: 0.0121 - val_loss: 0.0239 - learning_rate: 5.0000e-04 [1m3090/3090[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 514us/step [1m582/582[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 612us/step Training Profit: 2036.9708511084318 Test Profit: 236.7102106027305 [1m3672/3672[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 469us/step Verified Total Profit: 2273.680908203125 Saving training_profit_plot.png to /home/frode/OneDrive/Desktop/executioncode Saving test_profit_plot.png to /home/frode/OneDrive/Desktop/executioncode Cumulative profit plots saved as 'training_profit_plot.png' and 'test_profit_plot.png'

using these model settings: def build_model(input_dim): inputs = layers.Input(shape=(max_dogs, input_dim)) x = layers.Reshape((-1, input_dim))(inputs) #x = layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x) #x = layers.Dropout(0.5)(x) x = layers.Dense(32, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01))(x) x = layers.Dropout(0.5)(x) x = layers.Dense(16, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01))(x) x = layers.Dropout(0.5)(x) x = layers.Dense(1)(x) outputs = layers.Reshape((max_dogs,))(x) return Model(inputs, outputs)

try: early_stopping = callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True) lr_scheduler = callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5) profit_logger = ProfitLogger() model.fit( train_dataset, epochs=50, validation_data=test_dataset, callbacks=[early_stopping, lr_scheduler, profit_logger], verbose=1 ) logger.info("Model training completed") except Exception as e: logger.error(f"Model training error: {e}") raise

What should I change for the training and test loss numbers to get better?