[{'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:inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\ntargets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\nconv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu)\nmaxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\nconv2 = tf.layers.conv2d(maxpool1, 8, (3,3), padding='same', activation=tf.nn.relu)\nmaxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\nconv3 = tf.layers.conv2d(maxpool2, 8, (3,3), padding='same', activation=tf.nn.relu)\nencoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\nupsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\nconv4 = tf.layers.conv2d(upsample1, 8, (3,3), padding='same', activation=tf.nn.relu)\nupsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\nconv5 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\nupsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\nconv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\nlogits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\ndecoded = tf.nn.sigmoid(logits, name='decoded')\nloss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\ncost = tf.reduce_mean(loss)\nopt = tf.train.AdamOptimizer(0.001).minimize(cost)"}, {'role': 'assistant', 'content': 'modelling'}]