[{'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:W_conv1 = weight_variable([5, 5, 1, 32])\nb_conv1 = bias_variable([32])\nx_image = tf.reshape(x, [-1,28,28,1]) # mnist image comes in as 784 vector\nh_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\nh_pool1 = max_pooling_2x2(h_conv1)\nW_conv2 = weight_variable([5, 5, 32, 64])\nb_conv2 = bias_variable([64])\nh_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\nh_pool2 = max_pooling_2x2(h_conv2)\nW_fc1 = weight_variable([7 * 7 * 64, 1024]) # max pooling reduced image to 7x7\nb_fc1 = bias_variable([1024])\nh_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) # keras 'flatten'\nh_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\nkeep_prob = tf.placeholder(tf.float32)\nh_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\nW_fc2 = weight_variable([1024, 10])\nb_fc2 = bias_variable([10])\ny_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2"}, {'role': 'assistant', 'content': "['modelling']"}]