[{'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:def keras_model(input_dims):\n    model = Sequential()\n    \n    model.add(Dense(input_dims, input_dim=input_dims))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Dropout(0.3))\n    \n    model.add(Dense(input_dims))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Dropout(0.3))\n    \n    model.add(Dense(input_dims//2))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Dropout(0.3))\n    \n    model.add(Dense(input_dims//5))\n    model.add(BatchNormalization())\n    model.add(Activation('relu'))\n    model.add(Dropout(0.3))\n    \n    # output layer (y_pred)\n    model.add(Dense(1))\n    model.add(Activation('sigmoid'))\n    \n    # compile this model\n    model.compile(loss='binary_crossentropy', \n                  optimizer='adam', metrics=['accuracy'])\n    return model\n\n"}, {'role': 'assistant', 'content': 'helper_functions'}]