[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:def build_mlp(inputs, n_outputs, hidden_layers_widths, keep_prob):\n"""\nDefines architecture of an MLP.\n@type inputs: tensorflow.Tensor\n@type n_outputs: int\n@type hidden_layers_widths: list(int)\n@type keep_prob: tensorflow.Tensor\n@rtype: tensorflow.Tensor\n"""\ncurr_layer = inputs\nfor i in range(len(hidden_layers_widths)):\ncurr_layer = tf.contrib.layers.fully_connected(\ncurr_layer,\nhidden_layers_widths[i],\nweights_initializer=tf.truncated_normal_initializer(stddev=0.1),\nbiases_initializer=tf.truncated_normal_initializer(stddev=0.1))\ncurr_layer = tf.layers.dropout(curr_layer, keep_prob)\npreds = tf.contrib.layers.fully_connected(\ncurr_layer,\nn_outputs,\nactivation_fn=None,  # To have gradients for negative predictions.\nweights_initializer=tf.truncated_normal_initializer(stddev=0.1),\nbiases_initializer=tf.truncated_normal_initializer(stddev=0.1))\npreds = tf.identity(preds, name=\'predictions\')\nreturn preds'}, {'role': 'assistant', 'content': "['modelling']"}]