[{'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:num_units1 = 14*14*num_filters\nnum_units2 = 1024\nh_pool_flat = tf.reshape(h_pool, [-1,num_units1])\nw1 = tf.Variable(tf.random_normal([num_units1, num_units2]))\nb1 = tf.Variable(tf.random_normal([num_units2]))\nz1 = tf.add(tf.matmul(h_pool_flat, w1), b1)\nh1 = tf.nn.relu(z1)\nw0 = tf.Variable(tf.random_normal([num_units2, n_classes]))\nb0 = tf.Variable(tf.random_normal([n_classes]))\nlogits = tf.add(tf.matmul(h1, w0), b0)\nlearning_rate = tf.placeholder(tf.float32)\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\npredict = tf.argmax(logits, 1)\ncorrect_prediction = tf.equal(predict, tf.argmax(labels, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\ninit = tf.global_variables_initializer()'}, {'role': 'assistant', 'content': "['modelling']"}]