[{'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:def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n"""\nTrain the GAN\n:param epoch_count: Number of epochs\n:param batch_size: Batch Size\n:param z_dim: Z dimension\n:param learning_rate: Learning Rate\n:param beta1: The exponential decay rate for the 1st moment in the optimizer\n:param get_batches: Function to get batches\n:param data_shape: Shape of the data\n:param data_image_mode: The image mode to use for images ("RGB" or "L")\n"""\n_ , img_w, img_h, img_d = data_shape\ninput_real, input_z, lr = model_inputs(img_w, img_h, img_d, z_dim)\nd_loss, g_loss = model_loss(input_real, input_z, img_d)\nd_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)\ncounter = 0\nn_images = 4\nn_batches = data_shape[0]/batch_size\nwith tf.Session() as sess:\nsess.run(tf.global_variables_initializer())\nfor epoch_i in range(epoch_count):\nfor batch_images in get_batches(batch_size):\nbatch_z = np.random.uniform(-1,1, size=(batch_size, z_dim))\n_ = sess.run(d_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})\n_ = sess.run(g_opt, feed_dict={input_z: batch_z, lr: learning_rate})\nif counter % 100 == 0:\ntrain_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})\ntrain_loss_g = g_loss.eval({input_z: batch_z})\nshow_generator_output(sess, n_images, input_z, img_d, data_image_mode)\nprint("Percent complete for epoch {}: {}%".format(epoch_i+1, round(counter/n_batches*100)),\n"\\nDiscriminator Loss: {:.4f}".format(train_loss_d),\n"\\nGenerator Loss: {:.4f}".format(train_loss_g))\ncounter += 1\ncounter = 0'}, {'role': 'assistant', 'content': 'modelling'}]