[{'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:x = tf.placeholder(tf.float32, [None, n_input])\ny = tf.placeholder(tf.float32, [None, n_output])\nkeepratio = tf.placeholder(tf.float32)\n_pred = conv_basic(x, weights, biases, keepratio)[\'out\']\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))\nWEIGHT_DECAY_FACTOR = 0.0001\nl2_loss = tf.add_n([tf.nn.l2_loss(v)\nfor v in tf.trainable_variables()])\ncost = cost + WEIGHT_DECAY_FACTOR*l2_loss\noptm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)\n_corr = tf.equal(tf.argmax(_pred,1), tf.argmax(y,1)) # Count corrects\naccr = tf.reduce_mean(tf.cast(_corr, tf.float32)) # Accuracy\ninit = tf.initialize_all_variables()\nprint("FUNCTIONS READY")'}, {'role': 'assistant', 'content': 'prediction'}]