75982d3d634fb358be6c4fea6fd8dd705db13d9e,a00_Bert/train_bert_multi-label.py,,main,#Any#,34

Before Change

    loss, per_example_loss, logits, probabilities, model = create_model(bert_config, is_training, input_ids, input_mask,
                                                            segment_ids, label_ids, num_labels,use_one_hot_embeddings)
    // define train operation
    num_train_steps = int(float(num_examples) / float(FLAGS.batch_size * FLAGS.num_epochs));use_tpu=False
    num_warmup_steps = int(num_train_steps * 0.1)
    train_op = optimization.create_optimizer(loss, FLAGS.learning_rate, num_train_steps, num_warmup_steps, use_tpu)

After Change

    // define train operation
    //num_train_steps = int(float(num_examples) / float(FLAGS.batch_size * FLAGS.num_epochs)); use_tpu=False; num_warmup_steps = int(num_train_steps * 0.1)
    //train_op = optimization.create_optimizer(loss, FLAGS.learning_rate, num_train_steps, num_warmup_steps, use_tpu)
    global_step = tf.Variable(0, trainable=False, name="Global_Step")
    train_op = tf.contrib.layers.optimize_loss(loss, global_step=global_step, learning_rate=FLAGS.learning_rate,optimizer="Adam", clip_gradients=3.0)

    is_training_eval=False
    loss_eval, per_example_loss_eval, logits_eval, probabilities_eval, model_eval = create_model(bert_config, is_training_eval, input_ids, input_mask,

Instances