[{'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:def conv_net(x, keep_prob):\n"""\nCreate a convolutional neural network model\n: x: Placeholder tensor that holds image data.\n: keep_prob: Placeholder tensor that hold dropout keep probability.\n: return: Tensor that represents logits\n"""\nconv_ksize = (5,5)\nconv_strides = (1,1)\npool_ksize = (2,2)\npool_strides = (2,2)\nconv1 = conv2d_maxpool(x, 10, conv_ksize, conv_strides, pool_ksize, pool_strides)\nconv2 = conv2d_maxpool(conv1, 25, conv_ksize, conv_strides, pool_ksize, pool_strides)\nconv3 = conv2d_maxpool(conv2, 40, conv_ksize, conv_strides, pool_ksize, pool_strides)\nflatten1 = flatten(conv3)\nfc1 = fully_conn(flatten1, 64)\nfc1 = tf.nn.dropout(fc1, keep_prob)\nfc2 = fully_conn(fc1, 32)\nfc2 = tf.nn.dropout(fc2, keep_prob)\nres = output(fc2, 10)\nreturn res\n"""\nDON\'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n"""\ntf.reset_default_graph()\nx = neural_net_image_input((32, 32, 3))\ny = neural_net_label_input(10)\nkeep_prob = neural_net_keep_prob_input()\nlogits = conv_net(x, keep_prob)\nlogits = tf.identity(logits, name=\'logits\')\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\noptimizer = tf.train.AdamOptimizer().minimize(cost)\ncorrect_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name=\'accuracy\')\ntests.test_conv_net(conv_net)'}, {'role': 'assistant', 'content': "['evaluation', 'modelling', 'prediction']"}]