[{'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:batch_size = 16\npatch_size = 5\ndepth = 16\nnum_hidden = 64\ngraph = tf.Graph()\nwith graph.as_default():\ntf_train_dataset = tf.placeholder(\ntf.float32, shape=(batch_size, image_size, image_size, num_channels))\ntf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\ntf_valid_dataset = tf.constant(valid_dataset)\ntf_test_dataset = tf.constant(test_dataset)\nlayer1_weights = tf.Variable(tf.truncated_normal(\n[patch_size, patch_size, num_channels, depth], stddev=0.1))\nlayer1_biases = tf.Variable(tf.zeros([depth]))\nlayer2_weights = tf.Variable(tf.truncated_normal(\n[patch_size, patch_size, depth, depth], stddev=0.1))\nlayer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\nlayer3_weights = tf.Variable(tf.truncated_normal(\n[image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))\nlayer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\nlayer4_weights = tf.Variable(tf.truncated_normal(\n[num_hidden, num_labels], stddev=0.1))\nlayer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\ndef model(data):\nconv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')\nhidden = tf.nn.relu(conv + layer1_biases)\nconv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')\nhidden = tf.nn.relu(conv + layer2_biases)\nshape = hidden.get_shape().as_list()\nreshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\nhidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\nreturn tf.matmul(hidden, layer4_weights) + layer4_biases\nlogits = model(tf_train_dataset)\nloss = tf.reduce_mean(\ntf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\noptimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\ntrain_prediction = tf.nn.softmax(logits)\nvalid_prediction = tf.nn.softmax(model(tf_valid_dataset))\ntest_prediction = tf.nn.softmax(model(tf_test_dataset))"}, {'role': 'assistant', 'content': "['modelling']"}]