[{'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:EPOCHS = 10\nBATCH_SIZE = 128\nmu = 0\nsigma = 0.1\ndef LeNet(x):\nconv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 3, 6), mean = mu, stddev = sigma))\nconv1_b = tf.Variable(tf.zeros(6))\nconv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\nconv1 = tf.nn.relu(conv1)\nconv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\nconv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\nconv2_b = tf.Variable(tf.zeros(16))\nconv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\nconv2 = tf.nn.relu(conv2)\nconv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\nfc0   = flatten(conv2)\nfc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\nfc1_b = tf.Variable(tf.zeros(120))\nfc1   = tf.matmul(fc0, fc1_W) + fc1_b\nfc1    = tf.nn.relu(fc1)\nfc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\nfc2_b  = tf.Variable(tf.zeros(84))\nfc2    = tf.matmul(fc1, fc2_W) + fc2_b\nfc2    = tf.nn.relu(fc2)\nfc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\nfc3_b  = tf.Variable(tf.zeros(43))\nlogits = tf.matmul(fc2, fc3_W) + fc3_b\nreturn logits"}, {'role': 'assistant', 'content': "['modelling']"}]