[{'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:Y1 = tf.nn.relu(tf.nn.conv2d(X, w1, strides=[1, 1, 1, 1], padding='SAME') + b1)\nY1 = tf.nn.dropout(Y1,keep_prob=keep_prob) #for adding dropout, comment to disable\nY2 = tf.nn.conv2d(Y1, w2, strides=[1, 1, 1, 1], padding='SAME')\nY2 = tf.nn.max_pool(Y2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],padding='SAME')  # reduce to 28x28\nY2 = tf.nn.relu( Y2+ b2)\nY2 = tf.nn.dropout(Y2,keep_prob=keep_prob) #for adding dropout, comment to disable\nY3 = tf.nn.conv2d(Y2, w3, strides=[1, 1, 1, 1], padding='SAME')\nY3 = tf.nn.max_pool(Y3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],padding='SAME')  # reduce to 14x14\nY3 = tf.nn.relu(Y3+ b3)\nY3 = tf.nn.dropout(Y3,keep_prob=keep_prob) #for adding dropout, comment to disable\nY4 = tf.nn.conv2d(Y3, w4, strides=[1, 1, 1, 1], padding='SAME')\nY4 = tf.nn.max_pool(Y3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],padding='SAME')  # reduce to 7x7\nY4 = tf.nn.relu(Y4+ b4)\nY4 = tf.nn.dropout(Y4,keep_prob=keep_prob)\nfc = tf.reshape(Y4, shape=[-1, 7 * 7 * l4])\nY5 = tf.nn.relu(tf.matmul(fc, w5) + b5)\nYlogits = tf.matmul(Y5, w) + b\nlogits = tf.nn.softmax(Ylogits)\ncross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y)\ncross_entropy = tf.reduce_mean(cross_entropy)\ncorrect_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\ntrain_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)"}, {'role': 'assistant', 'content': "['evaluation', 'modelling', 'prediction']"}]