[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:# Definir correctamente la red neuronal (5 pts)<br>from keras.models import Sequential <br>from keras.layers import GlobalAveragePooling2D, Dense, Flatten,BatchNormalization, Dropout, Conv2D, MaxPool2D<br>from keras.optimizers import Adam, SGD<br>from keras import regularizers<br><br>lr = 0.01<br>bs = 256<br>nb = math.ceil(len(x_train)/bs)<br><br>final_model  = Sequential([<br>    Conv2D(32, 3, activation='relu', input_shape=(96,96,1)),<br>    MaxPool2D(),<br>    Conv2D(16, 3, activation='relu'),<br>    GlobalAveragePooling2D(),<br>    Dense(256, activation='relu', kernel_initializer='glorot_normal'),<br>    Dropout(0.7),<br>    Dense(128, activation='relu', kernel_initializer='glorot_normal'),<br>    Dense(64, activation='relu', kernel_initializer='glorot_normal'),<br>    Dense(30) #no se utiliza funcin de activacin porque se requiere hacer una regresin para cada coordenada<br>])<br>final_model .compile(Adam(lr), loss='mse', metrics=['mae'])<br>final_model .summary()"}, {'role': 'assistant', 'content': 'modelling'}]