def create_hparams(experiment):
    'Creates the hyper parameters.'
    hparams = {}
    hparams['batch_size'] = 64
    hparams['eval_batch_size'] = 64
    hparams['learning_rate_warmup_steps'] = 2000
    hparams['learning_rate_constant'] = 1
    hparams['learning_rate'] = 0.001
    hparams['train_epoches'] = 20
    hparams['steps_per_epoch'] = 30
    hparams['train_steps'] = (100 * 1000)
    hparams['eval_steps'] = 100
    hparams['caption_optimizer'] = 't2t'
    hparams['clip_norm'] = 5.0
    hparams['widget_encoder_checkpoint'] = ''
    hparams['train_files'] = ''
    hparams['eval_files'] = ''
    hparams['train_buffer_size'] = 2000
    hparams['eval_buffer_size'] = 500
    hparams['train_pixel_encoder'] = True
    hparams['debug'] = False
    hparams['distribution_strategy'] = 'mirrored'
    hparams['decoding_task'] = True
    hparams['classification_task'] = False
    hparams['use_decoding_for_classification'] = False
    hparams['classification_loss_weight'] = 1
    hparams['train_with_one_node'] = False
    hparams['embedding_file'] = ''
    hparams['word_vocab_path'] = ''
    hparams['glove_trainable'] = True
    hparams['vocab_size'] = 10000
    hparams['phrase_vocab_size'] = 10000
    hparams['max_pixel_pos'] = 100
    hparams['max_dom_pos'] = 500
    hparams['screen_encoder'] = 'gcn'
    hparams['screen_embedding_feature'] = ['text', 'type', 'pos', 'click', 'dom']
    hparams['obj_text_aggregation'] = 'max'
    hparams['synthetic_screen_noise'] = 0.0
    hparams['encode_screen_with_context'] = False
    hparams['add_pixel_skip_link'] = False
    hparams['num_hidden_layers'] = 2
    hparams['hidden_size'] = 2
    hparams['filter_size'] = 2
    hparams['num_heads'] = 2
    hparams['dropout'] = 0.2
    hparams['layer_prepostprocess_dropout'] = 0.2
    hparams['attention_dropout'] = 0.2
    hparams['relu_dropout'] = 0.2
    transformer_hparams = model_params.BASE_PARAMS
    hparams.update(transformer_hparams)
    config = widget_caption_config.experiments[experiment]
    hparams.update(config)
    return hparams
Creates the hyper parameters.
def create_hparams(experiment):
    
    hparams = {}
    hparams['batch_size'] = 64
    hparams['eval_batch_size'] = 64
    hparams['learning_rate_warmup_steps'] = 2000
    hparams['learning_rate_constant'] = 1
    hparams['learning_rate'] = 0.001
    hparams['train_epoches'] = 20
    hparams['steps_per_epoch'] = 30
    hparams['train_steps'] = (100 * 1000)
    hparams['eval_steps'] = 100
    hparams['caption_optimizer'] = 't2t'
    hparams['clip_norm'] = 5.0
    hparams['widget_encoder_checkpoint'] = 
    hparams['train_files'] = 
    hparams['eval_files'] = 
    hparams['train_buffer_size'] = 2000
    hparams['eval_buffer_size'] = 500
    hparams['train_pixel_encoder'] = True
    hparams['debug'] = False
    hparams['distribution_strategy'] = 'mirrored'
    hparams['decoding_task'] = True
    hparams['classification_task'] = False
    hparams['use_decoding_for_classification'] = False
    hparams['classification_loss_weight'] = 1
    hparams['train_with_one_node'] = False
    hparams['embedding_file'] = 
    hparams['word_vocab_path'] = 
    hparams['glove_trainable'] = True
    hparams['vocab_size'] = 10000
    hparams['phrase_vocab_size'] = 10000
    hparams['max_pixel_pos'] = 100
    hparams['max_dom_pos'] = 500
    hparams['screen_encoder'] = 'gcn'
    hparams['screen_embedding_feature'] = ['text', 'type', 'pos', 'click', 'dom']
    hparams['obj_text_aggregation'] = 'max'
    hparams['synthetic_screen_noise'] = 0.0
    hparams['encode_screen_with_context'] = False
    hparams['add_pixel_skip_link'] = False
    hparams['num_hidden_layers'] = 2
    hparams['hidden_size'] = 2
    hparams['filter_size'] = 2
    hparams['num_heads'] = 2
    hparams['dropout'] = 0.2
    hparams['layer_prepostprocess_dropout'] = 0.2
    hparams['attention_dropout'] = 0.2
    hparams['relu_dropout'] = 0.2
    transformer_hparams = model_params.BASE_PARAMS
    hparams.update(transformer_hparams)
    config = widget_caption_config.experiments[experiment]
    hparams.update(config)
    return hparams