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| caption_model: transformer | |
| noamopt: true | |
| noamopt_warmup: 20000 | |
| label_smoothing: 0.0 | |
| input_json: data/cocotalk.json | |
| input_label_h5: data/cocotalk_label.h5 | |
| input_fc_dir: data/cocotalk_clip_RN50_fc | |
| input_att_dir: data/cocotalk_clip_RN50_att | |
| input_clipscore_vis_dir: data/cocotalk_clipscore_vis | |
| seq_per_img: 5 | |
| batch_size: 160 | |
| learning_rate: 0.0005 | |
| checkpoint_path: save/clipRN50_clips/clipRN50_clips | |
| # Notice: because I'm to lazy, I reuse the option name for RNNs to set the hyperparameters for transformer: | |
| # N=num_layers | |
| # d_model=input_encoding_size | |
| # d_ff=rnn_size | |
| # will be ignored | |
| num_layers: 6 | |
| input_encoding_size: 512 | |
| rnn_size: 2048 | |
| # Transformer config | |
| N_enc: 6 | |
| N_dec: 6 | |
| d_model: 512 | |
| d_ff: 2048 | |
| num_att_heads: 8 | |
| dropout: 0.1 | |
| learning_rate_decay_start: 0 | |
| scheduled_sampling_start: -1 | |
| save_checkpoint_every: 3000 | |
| language_eval: 1 | |
| val_images_use: 5000 | |
| max_epochs: 15 | |
| train_sample_n: 5 | |
| REFORWARD: false | |
| # _BASE_: transformer.yml | |
| reduce_on_plateau: false | |
| noamopt: false | |
| learning_rate: 0.000005 | |
| learning_rate_decay_start: -1 | |
| self_critical_after: 15 | |
| max_epochs: 40 | |
| verbose: false | |
| precision: 32 | |
| use_clipscore: true | |
| clipscore_reward_weight: 2.0 |