[{'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:n_final_state = 32\n\ndef get_model(n_final_state, lr=1e-3, decay=1e-8):\n    input_shape = (H, W, C)\n    \n    input_x = Input(shape=input_shape)\n    \n    c1 = Conv2D(32, (3, 3))(input_x)\n    c1 = BatchNormalization()(c1)\n    c1 = Activation('relu')(c1)\n    c1 = MaxPooling2D((2, 2))(c1)\n    \n    c2 = Conv2D(32, (3, 3))(c1)\n    c2 = BatchNormalization()(c2)\n    c2 = Activation('relu')(c2)\n    c2 = MaxPooling2D((2, 2))(c2)\n    \n    c3 = Conv2D(64, (3, 3))(c2)\n    c3 = BatchNormalization()(c3)\n    c3 = Activation('relu')(c3)\n    c3 = MaxPooling2D((2, 2))(c3)\n    \n    flat = Flatten()(c3)\n    \n    d1 = Dense(\n        64, activation='relu'\n    )(flat)\n    #d1 = Dropout(0.5)(d1)\n    d1 = BatchNormalization()(d1)\n    \n    final_state = Dense(\n        n_final_state, activation='relu', name='final_state'\n    )(d1)\n    \n    x = Dropout(0.5)(final_state)\n    outputs = Dense(1, activation='sigmoid')(x)\n    model = Model(inputs=input_x, outputs=outputs)\n    optimizer=optimizers.Adam(lr=lr, decay=decay)\n    model.compile(loss='binary_crossentropy', \n                  optimizer=optimizer,\n                  metrics=['accuracy'])\n    return model\n\nmodel = get_model(n_final_state=n_final_state)\nmodel.summary()"}, {'role': 'assistant', 'content': 'modelling'}]