[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:import tensorflow as tf\nimport pickle\nimport numpy as np\ntrain_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')\nn_nodes_hl1 = 250\nn_nodes_hl2 = 250\nn_classes = 2\nhm_epochs = 10\nx = tf.placeholder('float')\ny = tf.placeholder('float')\nhidden_1_layer = {'f_fum':n_nodes_hl1,\n'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),\n'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}\nhidden_2_layer = {'f_fum':n_nodes_hl2,\n'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),\n'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}\noutput_layer = {'f_fum':None,\n'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_classes])),\n'bias':tf.Variable(tf.random_normal([n_classes])),}\ndef neural_network_model(data):\nl1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])\nl1 = tf.sigmoid(l1)\nl2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])\nl2 = tf.sigmoid(l2)\noutput = tf.matmul(l2,output_layer['weight']) + output_layer['bias']\nreturn output\ndef train_neural_network(x):\nprediction = neural_network_model(x)\ncost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )\noptimizer = tf.train.GradientDescentOptimizer(1).minimize(cost)\nwith tf.Session() as sess:\nsess.run(tf.global_variables_initializer())\nfor epoch in range(hm_epochs):\nepoch_loss = 0\nlast_cost = 0\nbatch_x = np.array(train_x[0:len(train_x)])\nbatch_y = np.array(train_y[0:len(train_x)])\n_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})\nepoch_loss += c\nlast_cost = c\nif (epoch% 2) == 0 and epoch > 1:\nprint('Epoch', epoch+1, 'completed out of',hm_epochs,'cost:', last_cost)\ncorrect = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct, 'float'))\nprint('Accuracy:',accuracy.eval({x:test_x, y:test_y}))\nprint(x.__dict__)\ntrain_neural_network(x)"}, {'role': 'assistant', 'content': 'modelling'}]