Showing preview only (3,710K chars total). Download the full file or copy to clipboard to get everything.
Repository: wangheda/youtube-8m
Branch: master
Commit: 07e54b387ee0
Files: 581
Total size: 3.4 MB
Directory structure:
gitextract_1ok4slma/
├── .gitignore
├── .gitmodules
├── LICENSE
├── README.md
├── eda/
│ ├── explore.ipynb
│ └── vertical.tsv
├── youtube-8m-ensemble/
│ ├── .vimrc
│ ├── CONTRIBUTING.md
│ ├── LICENSE
│ ├── README.md
│ ├── __init__.py
│ ├── all_ensemble_models/
│ │ ├── .vimrc
│ │ ├── __init__.py
│ │ ├── attention_linear_model.py
│ │ ├── attention_linmatrix_model.py
│ │ ├── attention_matrix_model.py
│ │ ├── attention_moe_matrix_model.py
│ │ ├── attention_moe_model.py
│ │ ├── attention_rectified_linear_model.py
│ │ ├── deep_combine_chain_model.py
│ │ ├── input_moe_model.py
│ │ ├── linear_regression_model.py
│ │ ├── logistic_model.py
│ │ ├── matrix_regression_model.py
│ │ ├── mean_model.py
│ │ ├── moe_model.py
│ │ └── nonunit_matrix_regression_model.py
│ ├── average_precision_calculator.py
│ ├── check_distillation.py
│ ├── check_video_id.py
│ ├── check_video_id_match.py
│ ├── cloudml-gpu-distributed.yaml
│ ├── cloudml-gpu.yaml
│ ├── data_augmentation.py
│ ├── ensemble_command.example
│ ├── ensemble_level_models.py
│ ├── ensemble_scripts/
│ │ ├── .vimrc
│ │ ├── after_submission_no1.conf
│ │ ├── after_submission_no2.conf
│ │ ├── after_submission_no3.conf
│ │ ├── after_submission_no4.conf
│ │ ├── auto-preensemble-deep_combine_chain_model.sh
│ │ ├── auto-preensemble-matrix_model.sh
│ │ ├── check-video_id.sh
│ │ ├── check-video_id_match.sh
│ │ ├── combine-tfrecords-frame-v2.sh
│ │ ├── combine-tfrecords-frame.sh
│ │ ├── combine-tfrecords-video-v2.sh
│ │ ├── combine-tfrecords-video.sh
│ │ ├── ensemble_no1.conf
│ │ ├── ensemble_no10.conf
│ │ ├── ensemble_no11.conf
│ │ ├── ensemble_no12.conf
│ │ ├── ensemble_no13.conf
│ │ ├── ensemble_no14.conf
│ │ ├── ensemble_no15.conf
│ │ ├── ensemble_no16.conf
│ │ ├── ensemble_no17.conf
│ │ ├── ensemble_no18.conf
│ │ ├── ensemble_no19.conf
│ │ ├── ensemble_no2.conf
│ │ ├── ensemble_no20.conf
│ │ ├── ensemble_no21.conf
│ │ ├── ensemble_no3.conf
│ │ ├── ensemble_no4.conf
│ │ ├── ensemble_no5.conf
│ │ ├── ensemble_no6.conf
│ │ ├── ensemble_no7.conf
│ │ ├── ensemble_no8.conf
│ │ ├── ensemble_no9.conf
│ │ ├── eval-attention_linear_model.sh
│ │ ├── eval-attention_linmatrix_model.sh
│ │ ├── eval-attention_matrix_model.sh
│ │ ├── eval-attention_moe_matrix_model.sh
│ │ ├── eval-attention_moe_model.sh
│ │ ├── eval-attention_rectified_linear_model.sh
│ │ ├── eval-deep_combine_chain_model.sh
│ │ ├── eval-input_moe_model.sh
│ │ ├── eval-linear_model.sh
│ │ ├── eval-matrix_model.sh
│ │ ├── eval-mean_model.sh
│ │ ├── eval-moe_model.sh
│ │ ├── eval-nonunit_matrix_model.sh
│ │ ├── explore-mean_model.log
│ │ ├── explore-mean_model.sh
│ │ ├── final_submission.conf
│ │ ├── infer-attention_linear_model.sh
│ │ ├── infer-attention_linmatrix_model.sh
│ │ ├── infer-attention_matrix_model.sh
│ │ ├── infer-attention_moe_matrix_model.sh
│ │ ├── infer-attention_moe_model.sh
│ │ ├── infer-attention_rectified_linear_model.sh
│ │ ├── infer-linear_model.sh
│ │ ├── infer-matrix_model.sh
│ │ ├── infer-mean_model.sh
│ │ ├── infer-moe_model.sh
│ │ ├── make-bagging-of-ensembles.sh
│ │ ├── make-virtual-groups.sh
│ │ ├── preensemble-attention_matrix_model.sh
│ │ ├── preensemble-matrix_model.sh
│ │ ├── preensemble-mean_model.sh
│ │ ├── train-attention_linear_model.sh
│ │ ├── train-attention_linmatrix_model.sh
│ │ ├── train-attention_matrix_model.sh
│ │ ├── train-attention_moe_matrix_model.sh
│ │ ├── train-attention_moe_model.sh
│ │ ├── train-attention_rectified_linear_model.sh
│ │ ├── train-deep_combine_chain_model.sh
│ │ ├── train-input_moe_model.sh
│ │ ├── train-linear_model.sh
│ │ ├── train-matrix_model.sh
│ │ ├── train-matrix_model_lr.sh
│ │ ├── train-mean_model.sh
│ │ ├── train-moe_model.sh
│ │ └── train-nonunit_matrix_model.sh
│ ├── eval.py
│ ├── eval_util.py
│ ├── feature_transform.py
│ ├── inference-combine-tfrecords-frame.py
│ ├── inference-combine-tfrecords-video.py
│ ├── inference-pre-ensemble.py
│ ├── inference.py
│ ├── losses.py
│ ├── mean_average_precision_calculator.py
│ ├── model_selection_scripts/
│ │ ├── .vimrc
│ │ ├── extend-step-mean_model.sh
│ │ ├── get_extend_candidates.py
│ │ ├── get_patterns.py
│ │ ├── get_top_k.py
│ │ └── greedy-selection-mean_model.sh
│ ├── model_utils.py
│ ├── models.py
│ ├── readers.py
│ ├── top_k_scripts/
│ │ ├── eval-attention_matrix_model.sh
│ │ ├── infer-attention_matrix_model.sh
│ │ ├── preensemble-attention_matrix_model.sh
│ │ ├── run_top_k.sh
│ │ └── train-attention_matrix_model.sh
│ ├── train.py
│ ├── training_utils/
│ │ ├── del.py
│ │ ├── sample_conf.py
│ │ ├── sample_freq.py
│ │ └── select.py
│ └── utils.py
├── youtube-8m-wangheda/
│ ├── .vimrc
│ ├── CONTRIBUTING.md
│ ├── LICENSE
│ ├── README.md
│ ├── __init__.py
│ ├── all_data_augmentation/
│ │ ├── __init__.py
│ │ ├── clipping_augmenter.py
│ │ ├── default_augmenter.py
│ │ ├── half_augmenter.py
│ │ ├── half_video_augmenter.py
│ │ └── noise_augmenter.py
│ ├── all_feature_transform/
│ │ ├── __init__.py
│ │ ├── avg_transformer.py
│ │ ├── default_transformer.py
│ │ ├── engineer_transformer.py
│ │ ├── identical_transformer.py
│ │ └── resolution_transformer.py
│ ├── all_frame_models/
│ │ ├── .vimrc
│ │ ├── __init__.py
│ │ ├── bilstm_model.py
│ │ ├── biunilstm_model.py
│ │ ├── cnn_deep_combine_chain_model.py
│ │ ├── cnn_kmax_model.py
│ │ ├── cnn_lstm_memory_model.py
│ │ ├── cnn_lstm_memory_multitask_model.py
│ │ ├── cnn_lstm_memory_normalization_model.py
│ │ ├── cnn_model.py
│ │ ├── dbof_model.py
│ │ ├── deep_cnn_deep_combine_chain_model.py
│ │ ├── deep_lstm_model.py
│ │ ├── distillchain_cnn_deep_combine_chain_model.py
│ │ ├── distillchain_lstm_attention_max_pooling_model.py
│ │ ├── distillchain_lstm_cnn_deep_combine_chain_model.py
│ │ ├── distillchain_lstm_memory_deep_combine_chain_model.py
│ │ ├── distillchain_lstm_parallel_finaloutput_model.py
│ │ ├── distillchain_multiscale_cnn_lstm_model.py
│ │ ├── frame_seg_model.py
│ │ ├── framehop_lstm_memory_deep_combine_chain_model.py
│ │ ├── framehop_lstm_memory_model.py
│ │ ├── gru_pooling_model.py
│ │ ├── gru_with_pooling_model.py
│ │ ├── layernorm_lstm_memory_model.py
│ │ ├── logistic_model.py
│ │ ├── lstm_advanced_model.py
│ │ ├── lstm_attention_lstm_model.py
│ │ ├── lstm_attention_max_pooling_model.py
│ │ ├── lstm_attention_model.py
│ │ ├── lstm_auxloss_deep_combine_chain_model.py
│ │ ├── lstm_cnn_deep_combine_chain_model.py
│ │ ├── lstm_divided_model.py
│ │ ├── lstm_look_back_model.py
│ │ ├── lstm_memory_chain_model.py
│ │ ├── lstm_memory_deep_chain_model.py
│ │ ├── lstm_memory_input_chain_model.py
│ │ ├── lstm_memory_model.py
│ │ ├── lstm_memory_multitask_model.py
│ │ ├── lstm_memory_normalization_model.py
│ │ ├── lstm_memory_parallel_chain_model.py
│ │ ├── lstm_model.py
│ │ ├── lstm_multi_attention_model.py
│ │ ├── lstm_multi_pooling_model.py
│ │ ├── lstm_parallel_finaloutput_model.py
│ │ ├── lstm_parallel_memory_model.py
│ │ ├── lstm_parallel_model.py
│ │ ├── lstm_pooling_model.py
│ │ ├── lstm_positional_attention_max_pooling_model.py
│ │ ├── lstm_with_mean_input_model.py
│ │ ├── lstm_with_pooling_model.py
│ │ ├── mm_lstm_memory_model.py
│ │ ├── multi_view_cnn_deep_combine_chain_model.py
│ │ ├── multires_lstm_memory_deep_combine_chain_model.py
│ │ ├── multiscale_cnn_lstm_model.py
│ │ ├── positional_cnn_deep_combine_chain_model.py
│ │ ├── progressive_attention_lstm_model.py
│ │ └── wide_and_deep_model.py
│ ├── all_video_models/
│ │ ├── .vimrc
│ │ ├── __init__.py
│ │ ├── chain_main_relu_moe_model.py
│ │ ├── chain_moe_model.py
│ │ ├── chain_support_relu_moe_model.py
│ │ ├── deep_chain_model.py
│ │ ├── deep_combine_chain_model.py
│ │ ├── distillchain_deep_combine_chain_model.py
│ │ ├── hidden_chain_model.py
│ │ ├── hidden_combine_chain_model.py
│ │ ├── logistic_model.py
│ │ ├── mlp_moe_model.py
│ │ ├── moe_model.py
│ │ ├── multitask_divergence_deep_combine_chain_model.py
│ │ ├── multitask_divergence_moe_model.py
│ │ ├── multitask_moe_model.py
│ │ ├── shortcut_chain_support_relu_moe_model.py
│ │ └── stage2_logistic_model.py
│ ├── average_precision_calculator.py
│ ├── bagging_scripts/
│ │ ├── cnn-deep-combine-chain-bagging.sh
│ │ ├── distillation-video-dcc-bagging.sh
│ │ ├── lstmattention8max-bagging.sh
│ │ ├── lstmparalleloutput-bagging.sh
│ │ └── video-deep-combine-chain-bagging.sh
│ ├── boosting_scripts/
│ │ ├── cnn-deep-combine-chain-boosting.sh
│ │ ├── distillation-cnn-dcc-boosting.sh
│ │ ├── distillation-lstmcnn-dcc-boosting.sh
│ │ ├── distillation-lstmparalleloutput-boosting.sh
│ │ ├── distillation-multilstm-dcc-boosting.sh
│ │ ├── distillation-multiscale-cnn-lstm-boosting.sh
│ │ ├── distillation-positional-lstmattention8max-boosting.sh
│ │ ├── distillation-video-dcc-boosting.sh
│ │ ├── lstmattention8max-boosting-weightclip.sh
│ │ ├── lstmparalleloutput-boosting-weightclip.sh
│ │ ├── video-deep-combine-chain-boosting-discardhopeless.sh
│ │ ├── video-deep-combine-chain-boosting-weightclip.sh
│ │ └── video-deep-combine-chain-boosting.sh
│ ├── cascade_scripts/
│ │ ├── distillchain-v2-hybridchain.sh
│ │ ├── distillchain-v2-hybridchain2.sh
│ │ └── distillchain-v2-videochain.sh
│ ├── cloudml-gpu-distributed.yaml
│ ├── cloudml-gpu.yaml
│ ├── data_augmentation.py
│ ├── data_augmentation_scripts/
│ │ ├── eval-chaining-video.sh
│ │ ├── run-chaining-cnn.sh
│ │ ├── run-chaining-lstm.sh
│ │ ├── run-chaining-video.sh
│ │ ├── run-multiple-attention-pooling-positional-embedding.sh
│ │ ├── run-multiscale-cnn-lstm-model.sh
│ │ └── run-parallel-lstm-memory.sh
│ ├── eval.py
│ ├── eval.sh
│ ├── eval_scripts/
│ │ ├── eval-att-lstm.sh
│ │ ├── eval-att.sh
│ │ ├── eval-bi-uni-lstm.sh
│ │ ├── eval-chain-model-relu.sh
│ │ ├── eval-chain-model-suprelu.sh
│ │ ├── eval-chain-moe-0.4.sh
│ │ ├── eval-chain-moe-freq.sh
│ │ ├── eval-chain-moe-suprelu-vert+freq.sh
│ │ ├── eval-chain-moe.sh
│ │ ├── eval-cnn-deep-combine-chain.sh
│ │ ├── eval-cnn-model.sh
│ │ ├── eval-dbof.sh
│ │ ├── eval-deep-cnn-deep-combine-chain.sh
│ │ ├── eval-distill-video-dcc-noise-scene1.sh
│ │ ├── eval-distill-video-dcc-noise-scene2.sh
│ │ ├── eval-distillchain-cnn-dcc.sh
│ │ ├── eval-distillchain-lstmcnn.sh
│ │ ├── eval-distillchain-lstmparalleloutput.sh
│ │ ├── eval-distillchain-multilstm.sh
│ │ ├── eval-distillchain-v2-boostinglstmparalleloutput.sh
│ │ ├── eval-distillchain-v2-lstmattention8max.sh
│ │ ├── eval-distillchain-v2-lstmcnn.sh
│ │ ├── eval-distillchain-v2-lstmparalleloutput.sh
│ │ ├── eval-distillchain-v2-multilstm.sh
│ │ ├── eval-distillchain-v2-multiscale-cnnlstm.sh
│ │ ├── eval-distillchain-v2-video-dcc.sh
│ │ ├── eval-distillchain-video-dcc.sh
│ │ ├── eval-frame-seg.sh
│ │ ├── eval-framehop-lstmmem.sh
│ │ ├── eval-layer-chain-moe8-freq.sh
│ │ ├── eval-layer-moe-vert.sh
│ │ ├── eval-lstm-attention-8max.sh
│ │ ├── eval-lstm-cnn-deep-combine-chain.sh
│ │ ├── eval-lstm-look-back.sh
│ │ ├── eval-lstm-positional-attention-8max.sh
│ │ ├── eval-lstmmem-augmenter.sh
│ │ ├── eval-lstmmem-chain-freq.sh
│ │ ├── eval-lstmmem-chain.sh
│ │ ├── eval-lstmmem-cnnlstm.sh
│ │ ├── eval-lstmmem-deep-chain.sh
│ │ ├── eval-lstmmem-deep-combine-chain-length.sh
│ │ ├── eval-lstmmem-dropout.sh
│ │ ├── eval-lstmmem-feature.sh
│ │ ├── eval-lstmmem-input-chain.sh
│ │ ├── eval-lstmmem-input-noise.sh
│ │ ├── eval-lstmmem-l2norm.sh
│ │ ├── eval-lstmmem-layernorm.sh
│ │ ├── eval-lstmmem-lowres.sh
│ │ ├── eval-lstmmem-no-transform.sh
│ │ ├── eval-lstmmem-noise.sh
│ │ ├── eval-lstmmem-parallel.sh
│ │ ├── eval-lstmmem-shortcut-chain-freq.sh
│ │ ├── eval-lstmmem2048.sh
│ │ ├── eval-lstmmemory-audio.sh
│ │ ├── eval-lstmmemory-layer1.sh
│ │ ├── eval-lstmmemory.sh
│ │ ├── eval-lstmoutput-parallel.sh
│ │ ├── eval-mem.sh
│ │ ├── eval-mm-lstm.sh
│ │ ├── eval-moe-baseline.sh
│ │ ├── eval-moe-batchagreement1.sh
│ │ ├── eval-moe-batchagreement2.sh
│ │ ├── eval-moe-batchagreement3.sh
│ │ ├── eval-moe-model.sh
│ │ ├── eval-moe-topk-batchagreement1.sh
│ │ ├── eval-moe-topk-batchagreement2.sh
│ │ ├── eval-moe-topk-batchagreement3.sh
│ │ ├── eval-multi-lstmmem-deep-chain.sh
│ │ ├── eval-multi-view-cnn-deep-combine-chain.sh
│ │ ├── eval-multires-lstm-deep-combine-chain.sh
│ │ ├── eval-multitask-ce.sh
│ │ ├── eval-multitask.sh
│ │ ├── eval-positional-cnn-dcc.sh
│ │ ├── eval-stage2-logistic.sh
│ │ ├── eval-stage2-moe.sh
│ │ ├── eval-video-deep-chain.sh
│ │ ├── eval-video-deep-combine-addnoise.sh
│ │ ├── eval-video-deep-combine-chain-dropout.sh
│ │ ├── eval-video-deep-combine-chain-labelsmoothing.sh
│ │ ├── eval-video-deep-combine-chain-noise.sh
│ │ ├── eval-video-deep-combine-chain.sh
│ │ ├── eval-video-distillchain-video-dcc.sh
│ │ ├── eval-video-divergence-chain-model.sh
│ │ ├── eval-video-divergence-moe-model.sh
│ │ ├── eval-video-hidden-chain.sh
│ │ ├── eval-video-hidden-combine-chain.sh
│ │ ├── eval-video-logistic.sh
│ │ ├── eval-video-moe.sh
│ │ ├── eval-video-pairwise.sh
│ │ └── eval-video-verydeep-combine-chain.sh
│ ├── eval_util.py
│ ├── feature_transform.py
│ ├── frame_level_models.py
│ ├── infer_scripts/
│ │ ├── infer-attentionlstm_moe4.sh
│ │ ├── infer-biunilstm1024_moe8.sh
│ │ ├── infer-cnn_deep_combine_chain.sh
│ │ ├── infer-cnn_lstmmemory1024_moe8.sh
│ │ ├── infer-cnn_model.sh
│ │ ├── infer-dbof.sh
│ │ ├── infer-deep_cnn_deep_combine.sh
│ │ ├── infer-deeplstm1024_layer6_moe4.sh
│ │ ├── infer-distill_video_dcc.sh
│ │ ├── infer-distillation-cnn-dcc.sh
│ │ ├── infer-distillation-lstmattention8max.sh
│ │ ├── infer-distillation-lstmgate.sh
│ │ ├── infer-distillation-video-dcc.sh
│ │ ├── infer-distillation.sh
│ │ ├── infer-distillchain-cnn-dcc.sh
│ │ ├── infer-distillchain-lstmcnn.sh
│ │ ├── infer-distillchain-lstmparalleloutput.sh
│ │ ├── infer-distillchain-v2-boost-lstmparalleloutput.sh
│ │ ├── infer-distillchain-v2-lstmattention8max.sh
│ │ ├── infer-distillchain-v2-lstmcnn.sh
│ │ ├── infer-distillchain-v2-lstmparalleloutput.sh
│ │ ├── infer-distillchain-v2-multilstm.sh
│ │ ├── infer-distillchain-v2-multiscal-cnnlstm.sh
│ │ ├── infer-distillchain-v2-video-dcc.sh
│ │ ├── infer-frame_seg.sh
│ │ ├── infer-framehop_lstm.sh
│ │ ├── infer-lstm_attention8_max.sh
│ │ ├── infer-lstm_cnn_deep_combine_chain.sh
│ │ ├── infer-lstmattlstm1024_moe8.sh
│ │ ├── infer-lstmmemory-audio.sh
│ │ ├── infer-lstmmemory-layer1.sh
│ │ ├── infer-lstmmemory1024_deep_combine_chain_add_length.sh
│ │ ├── infer-lstmmemory1024_moe8.sh
│ │ ├── infer-lstmmemory2048_moe4.sh
│ │ ├── infer-lstmparallelmemory1024_moe8.sh
│ │ ├── infer-lstmparalleloutput1024_moe8.sh
│ │ ├── infer-model_input.sh
│ │ ├── infer-multilstmmemory1024_moe4_deep_chain.sh
│ │ ├── infer-multires_lstm_deep_combine_chain.sh
│ │ ├── infer-positional-lstmattention8max.sh
│ │ ├── infer-video-distillchain-video-dcc.sh
│ │ ├── infer-video_group_moe4_noise0.2_layer4_elu.sh
│ │ ├── infer-video_logistic.sh
│ │ ├── infer-video_moe16_model.sh
│ │ └── infer-video_very_deep_combine_chain.sh
│ ├── inference-layer.py
│ ├── inference-pre-ensemble-get-input.py
│ ├── inference-pre-ensemble-with-predictions.py
│ ├── inference-pre-ensemble.py
│ ├── inference-sample-error-analysis.py
│ ├── inference-sample-error.py
│ ├── inference-stage1.py
│ ├── inference.py
│ ├── losses.py
│ ├── mean_average_precision_calculator.py
│ ├── model_utils.py
│ ├── models.py
│ ├── readers.py
│ ├── train-with-predictions.py
│ ├── train-with-rebuild.py
│ ├── train.py
│ ├── training_scripts/
│ │ ├── run-cascade-75-chaining-cnn.sh
│ │ ├── run-cascade-75-chaining-lstm-cnn.sh
│ │ ├── run-cascade-75-chaining-lstm.sh
│ │ ├── run-cascade-75-chaining-parallel-lstm.sh
│ │ ├── run-cascade-75-chaining-video.sh
│ │ ├── run-cascade-75-multiple-attention-pooling.sh
│ │ ├── run-cascade-76-chaining-cnn.sh
│ │ ├── run-cascade-76-chaining-lstm-cnn.sh
│ │ ├── run-cascade-76-chaining-lstm.sh
│ │ ├── run-cascade-76-chaining-video.sh
│ │ ├── run-cascade-76-multiple-attention-pooling.sh
│ │ ├── run-cascade-76-multiscale-cnn-lstm.sh
│ │ ├── run-cascade-76-parallel-lstm-boosting.sh
│ │ ├── run-cascade-76-parallel-lstm.sh
│ │ ├── run-chaining-cnn.sh
│ │ ├── run-chaining-deep-cnn.sh
│ │ ├── run-chaining-lstm-cnn.sh
│ │ ├── run-chaining-lstm.sh
│ │ ├── run-chaining-multi-resolution-lstm.sh
│ │ ├── run-chaining-shared-lstm.sh
│ │ ├── run-chaining-video.sh
│ │ ├── run-cnn-lstm.sh
│ │ ├── run-cnn-model.sh
│ │ ├── run-lstm-memory-cell1024.sh
│ │ ├── run-lstm-memory-cell2048.sh
│ │ ├── run-multiple-attention-pooling-positional-embedding.sh
│ │ ├── run-multiscale-cnn-lstm-model.sh
│ │ ├── run-parallel-lstm-memory.sh
│ │ ├── run-parallel-lstm-output.sh
│ │ └── run-temporal-pooling-lstm.sh
│ ├── training_utils/
│ │ ├── del.py
│ │ ├── human_readable_error_analysis.py
│ │ ├── reweight_sample_freq.py
│ │ ├── sample_freq.py
│ │ ├── select.py
│ │ └── video_original_boosting_error_analysis.py
│ ├── utils.py
│ └── video_level_models.py
└── youtube-8m-zhangteng/
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── YM_framemean.py
├── YM_labels_matrix.py
├── YM_labels_model.py
├── YM_labels_vocab.py
├── YM_readframefeature.py
├── __init__.py
├── average_precision_calculator.py
├── cloudml-gpu-distributed.yaml
├── cloudml-gpu.yaml
├── eval.py
├── eval_autoencoder.py
├── eval_distill.py
├── eval_embedding.py
├── eval_scripts/
│ ├── eval-distillchain_cnndcc_layer2moe4.sh
│ ├── eval-distillchain_lstm_extend_moe8.sh
│ ├── eval-distillchain_lstm_gate_moe8.sh
│ ├── eval-distillchain_lstm_gate_moe8_v2.sh
│ ├── eval-distillchain_lstm_glu2_moe8_v2.sh
│ ├── eval-distillchain_lstm_moe8.sh
│ ├── eval-distillchain_lstm_moe8_v2.sh
│ ├── eval-distillchain_lstm_multiscale2layer_moe8.sh
│ ├── eval-distillchain_lstm_multiscale4layer_moe8.sh
│ ├── eval-distillchain_video_norm_moe8_local.sh
│ ├── eval-distillsplit_lstm_gate_moe8.sh
│ ├── eval-lstm2_attention8_max.sh
│ ├── eval-lstm_attention8_max.sh
│ ├── eval-lstm_gate_multiscale4_moe4.sh
│ ├── eval-lstm_multiscale4_moe4.sh
│ ├── eval-lstm_random_moe8.sh
│ ├── eval-lstm_shortlayers_moe8.sh
│ ├── eval-lstmbiglu_1024_moe8.sh
│ ├── eval-lstmgate1024_moe8.sh
│ ├── eval-lstmglu2_1024_moe8.sh
│ ├── eval-video_knowledge_combine_chain.sh
│ ├── eval-video_notzero_combine_chain.sh
│ ├── eval-video_relabel_combine_chain.sh
│ └── eval-video_softmax_combine_chain.sh
├── eval_util.py
├── frame_level_models.py
├── infer_scripts/
│ ├── infer-distillchain_cnndcc_layer2moe4.sh
│ ├── infer-distillchain_cnndcc_layer2moe4_ensemble.sh
│ ├── infer-distillchain_lstm_extend_moe8.sh
│ ├── infer-distillchain_lstm_gate_moe8.sh
│ ├── infer-distillchain_lstm_gate_moe8_v2.sh
│ ├── infer-distillchain_lstm_glu2_moe8_v2.sh
│ ├── infer-distillchain_lstm_moe8.sh
│ ├── infer-distillchain_lstm_moe8_v2.sh
│ ├── infer-distillchain_lstm_multiscale2layer_moe8.sh
│ ├── infer-distillchain_lstm_multiscale4layer_moe8.sh
│ ├── infer-distillchain_video_norm_moe8.sh
│ ├── infer-distillchain_video_norm_moe8_local.sh
│ ├── infer-distillsplit_lstm_gate_moe8.sh
│ ├── infer-lstm2_attention8_max.sh
│ ├── infer-lstm_attention8_max.sh
│ ├── infer-lstm_attention_max_mean.sh
│ ├── infer-lstm_gate_multiscale4_moe4.sh
│ ├── infer-lstm_multiscale4_moe4.sh
│ ├── infer-lstm_random_mean_moe8.sh
│ ├── infer-lstm_shortlayers_moe8.sh
│ ├── infer-lstmbiglu_1024_moe8.sh
│ ├── infer-lstmgate1024_moe8.sh
│ ├── infer-lstmglu2_1024_moe8.sh
│ ├── infer-video_notzero_combine_chain.sh
│ └── infer-video_relabel_combine_chain.sh
├── inference-pre-ensemble-distill.py
├── inference-pre-ensemble.py
├── inference.py
├── inference_autoencoder.py
├── inference_embedding.py
├── inference_test.py
├── inference_with_rebuild.py
├── labels_autoencoder.py
├── labels_embedding.py
├── labels_rbm.py
├── losses.py
├── losses_embedding.py
├── mean_average_precision_calculator.py
├── model_utils.py
├── models.py
├── readers.py
├── rnn_residual.py
├── train-with-rebuild.py
├── train.py
├── train_autoencoder.py
├── train_embedding.py
├── train_ensemble.py
├── train_scripts/
│ ├── run-attention-pooling-lstm-a.sh
│ ├── run-attention-pooling-lstm-s.sh
│ ├── run-attention-pooling-lstm2lstm.sh
│ ├── run-attention-pooling.sh
│ ├── run-bilstm-a.sh
│ ├── run-cascade-76-lstm-a.sh
│ ├── run-cascade-76-lstm-s.sh
│ ├── run-cascade-76-lstm.sh
│ ├── run-cascade-attention-pooling.sh
│ ├── run-cascade-chaining-cnn-layer2.sh
│ ├── run-cascade-chaining-video-normalize.sh
│ ├── run-cascade-lstm-s-split.sh
│ ├── run-cascade-lstm-s.sh
│ ├── run-cascade-lstm.sh
│ ├── run-cascade-multiscale-cnn-lstm-laery4.sh
│ ├── run-cascade-multiscale-cnn-lstm-layer2.sh
│ ├── run-chaining-video-add-confident.sh
│ ├── run-chaining-video-infrequent-softmax.sh
│ ├── run-chaining-video-normal.sh
│ ├── run-chaining-video-vertical.sh
│ ├── run-lstm-random-augmentation.sh
│ ├── run-lstm-s.sh
│ ├── run-multiscale-cnn-lstm.sh
│ └── run-temporal-segment-lstm.sh
├── training_utils/
│ ├── del.py
│ └── select.py
├── utils.py
├── video_level_models.py
└── writers.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
model/
prediction/
*.pdf
*/__pycache__/
*.out
*.csv
*.tfrecord
*.pyc
.*.swp
eda/data/
================================================
FILE: .gitmodules
================================================
[submodule "3rd_party/annoy"]
path = 3rd_party/annoy
url = git@github.com:spotify/annoy.git
[submodule "tensorflow"]
path = tensorflow
url = git@github.com:tensorflow/tensorflow.git
================================================
FILE: LICENSE
================================================
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "{}"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2017 Heda Wang and Teng Zhang
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
================================================
FILE: README.md
================================================
# The Monkeytyping Solution to the Youtube-8M Video Understanding Challenge
This is the solution repository of the 2nd place team monkeytyping, licensed under the Apache License 2.0.
## Dependencies
Python 2.7
Tensorflow 1.0
Numpy 1.12
GNU Bash
## Resources
For an understanding of our system, read the report of our solution:
> https://arxiv.org/abs/1706.05150
Our source code:
> https://github.com/wangheda/youtube-8m
## Useful scripts
Training scripts (training a model may take 3-5 days) are in
> youtube-8m-wangheda/training_scripts
> youtube-8m-zhangteng/train_scripts
Eval scripts for selecting best performing checkpoints
> youtube-8m-wangheda/eval_scripts
> youtube-8m-zhangteng/eval_scripts
Infer scripts for generating intermediate files used by ensemble scripts
> youtube-8m-wangheda/infer_scripts
> youtube-8m-zhangteng/infer_scripts
Ensemble scripts
> youtube-8m-ensemble/ensemble_scripts
## Paths of models and data
There are some conventions that we use in our code:
models are saved in
> ./model
train1 data is saved in
> /Youtube-8M/data/frame/train
> /Youtube-8M/data/video/train
validate1 data is saved in
> /Youtube-8M/data/frame/validate
> /Youtube-8M/data/video/validate
test data is saved in
> /Youtube-8M/data/frame/test
> /Youtube-8M/data/video/test
train2 data is saved in
> /Youtube-8M/data/frame/ensemble_train
> /Youtube-8M/data/video/ensemble_train
validate2 data is saved in
> /Youtube-8M/data/frame/ensemble_validate
> /Youtube-8M/data/video/ensemble_validate
intermediate results are stored in
> /Youtube-8M/model_predictions/ensemble_train/[method]
> /Youtube-8M/model_predictions/ensemble_validate/[method]
> /Youtube-8M/model_predictions/test/[method]
## How to generate a solution
### Single model
1. Train a single model
2. evaluate the checkpoints to get the best one
3. infer the checkpoint to get intermediate result.
### Ensemble model
1. Write a configuration file
2. train a stacking model
3. evaluate the stacking model and pick the best checkpoint
4. infer the checkpoint to get a submission file
## Note
Some of the single models are developed by Heda and some by Teng, so they are distributed in two folders.
Bagging models are in `youtube-8m-wangheda/bagging_scripts`.
Boosting and distillation models are in `youtube-8m-wangheda/bagging_scripts`.
Cascade models are in `youtube-8m-wangheda/cascade_scripts`.
Stacking models are in `youtube-8m-ensemble/ensemble_scripts`.
================================================
FILE: eda/explore.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"label_names = pd.read_csv(\"data/label_names.csv\")\n",
"vocabulary = pd.read_csv(\"data/vocabulary.csv\")\n",
"train_labels = pd.read_csv(\"data/train_labels.csv\", header=None, names=[\"id\", \"labels\"])\n",
"validate_labels = pd.read_csv(\"data/validate_labels.csv\", header=None, names=[\"id\", \"labels\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lable names"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label_id</th>\n",
" <th>label_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Games</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Vehicle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>Video game</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Concert</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Car</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>Dance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>Animation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>Musician</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>Football</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>Music video</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>Animal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>Motorsport</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>Food</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>Musical ensemble</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>Guitar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>Cartoon</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>16</td>\n",
" <td>Performance art</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>17</td>\n",
" <td>Racing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>18</td>\n",
" <td>Outdoor recreation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>PC game</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20</td>\n",
" <td>Trailer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>21</td>\n",
" <td>Stadium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>22</td>\n",
" <td>Nature</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>23</td>\n",
" <td>Mobile phone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>24</td>\n",
" <td>String instrument</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>25</td>\n",
" <td>Toy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>26</td>\n",
" <td>Cooking</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>27</td>\n",
" <td>Motorcycle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>28</td>\n",
" <td>Fashion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>29</td>\n",
" <td>Smartphone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4686</th>\n",
" <td>4686</td>\n",
" <td>Nisekoi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4687</th>\n",
" <td>4687</td>\n",
" <td>Hollywood Palladium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4688</th>\n",
" <td>4688</td>\n",
" <td>Skullgirls</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4689</th>\n",
" <td>4689</td>\n",
" <td>Al-Masjid an-Nabawi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4690</th>\n",
" <td>4690</td>\n",
" <td>Shadow Fighter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4691</th>\n",
" <td>4691</td>\n",
" <td>Reptile</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4692</th>\n",
" <td>4692</td>\n",
" <td>Abercrombie & Fitch</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4693</th>\n",
" <td>4693</td>\n",
" <td>Daffy Duck</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4694</th>\n",
" <td>4694</td>\n",
" <td>Cruelty to animals</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4695</th>\n",
" <td>4695</td>\n",
" <td>Strikeout</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4696</th>\n",
" <td>4696</td>\n",
" <td>Arjuna</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4697</th>\n",
" <td>4697</td>\n",
" <td>Scarlet Weather Rhapsody</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4698</th>\n",
" <td>4698</td>\n",
" <td>Voetbal International</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4699</th>\n",
" <td>4699</td>\n",
" <td>Death Row Records</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4700</th>\n",
" <td>4700</td>\n",
" <td>Shashlik</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4701</th>\n",
" <td>4701</td>\n",
" <td>Scarecrow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4702</th>\n",
" <td>4702</td>\n",
" <td>Akame ga Kill!</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4703</th>\n",
" <td>4703</td>\n",
" <td>DJ Hero 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4704</th>\n",
" <td>4704</td>\n",
" <td>Gong</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4705</th>\n",
" <td>4705</td>\n",
" <td>SCP – Containment Breach</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4706</th>\n",
" <td>4706</td>\n",
" <td>Friday the 13th: Day of Death</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4707</th>\n",
" <td>4707</td>\n",
" <td>Poncho</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4708</th>\n",
" <td>4708</td>\n",
" <td>Visual novel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4709</th>\n",
" <td>4709</td>\n",
" <td>Luge</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4710</th>\n",
" <td>4710</td>\n",
" <td>Mammal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4711</th>\n",
" <td>4711</td>\n",
" <td>Aston Martin V8 Vantage (2005)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4712</th>\n",
" <td>4712</td>\n",
" <td>Russian pyramid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4713</th>\n",
" <td>4713</td>\n",
" <td>PBS Kids</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4714</th>\n",
" <td>4714</td>\n",
" <td>Air Gear</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4715</th>\n",
" <td>4715</td>\n",
" <td>Pina Records</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4716 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" label_id label_name\n",
"0 0 Games\n",
"1 1 Vehicle\n",
"2 2 Video game\n",
"3 3 Concert\n",
"4 4 Car\n",
"5 5 Dance\n",
"6 6 Animation\n",
"7 7 Musician\n",
"8 8 Football\n",
"9 9 Music video\n",
"10 10 Animal\n",
"11 11 Motorsport\n",
"12 12 Food\n",
"13 13 Musical ensemble\n",
"14 14 Guitar\n",
"15 15 Cartoon\n",
"16 16 Performance art\n",
"17 17 Racing\n",
"18 18 Outdoor recreation\n",
"19 19 PC game\n",
"20 20 Trailer\n",
"21 21 Stadium\n",
"22 22 Nature\n",
"23 23 Mobile phone\n",
"24 24 String instrument\n",
"25 25 Toy\n",
"26 26 Cooking\n",
"27 27 Motorcycle\n",
"28 28 Fashion\n",
"29 29 Smartphone\n",
"... ... ...\n",
"4686 4686 Nisekoi\n",
"4687 4687 Hollywood Palladium\n",
"4688 4688 Skullgirls\n",
"4689 4689 Al-Masjid an-Nabawi\n",
"4690 4690 Shadow Fighter\n",
"4691 4691 Reptile\n",
"4692 4692 Abercrombie & Fitch\n",
"4693 4693 Daffy Duck\n",
"4694 4694 Cruelty to animals\n",
"4695 4695 Strikeout\n",
"4696 4696 Arjuna\n",
"4697 4697 Scarlet Weather Rhapsody\n",
"4698 4698 Voetbal International\n",
"4699 4699 Death Row Records\n",
"4700 4700 Shashlik\n",
"4701 4701 Scarecrow\n",
"4702 4702 Akame ga Kill!\n",
"4703 4703 DJ Hero 2\n",
"4704 4704 Gong\n",
"4705 4705 SCP – Containment Breach\n",
"4706 4706 Friday the 13th: Day of Death\n",
"4707 4707 Poncho\n",
"4708 4708 Visual novel\n",
"4709 4709 Luge\n",
"4710 4710 Mammal\n",
"4711 4711 Aston Martin V8 Vantage (2005)\n",
"4712 4712 Russian pyramid\n",
"4713 4713 PBS Kids\n",
"4714 4714 Air Gear\n",
"4715 4715 Pina Records\n",
"\n",
"[4716 rows x 2 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_names"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"label = {}\n",
"for item in label_names.iterrows():\n",
" label_id = item[1].label_id\n",
" label_name = item[1].label_name\n",
" label[label_id] = label_name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Label details"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Index</th>\n",
" <th>TrainVideoCount</th>\n",
" <th>KnowledgeGraphId</th>\n",
" <th>Name</th>\n",
" <th>WikiUrl</th>\n",
" <th>Vertical1</th>\n",
" <th>Vertical2</th>\n",
" <th>Vertical3</th>\n",
" <th>WikiDescription</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>865174</td>\n",
" <td>/m/03bt1gh</td>\n",
" <td>Game</td>\n",
" <td>https://en.wikipedia.org/wiki/Game</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A game is structured form of play, usually und...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>683166</td>\n",
" <td>/m/07yv9</td>\n",
" <td>Vehicle</td>\n",
" <td>https://en.wikipedia.org/wiki/Vehicle</td>\n",
" <td>Autos & Vehicles</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A vehicle is a mobile machine that transports ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>522427</td>\n",
" <td>/m/01mw1</td>\n",
" <td>Video game</td>\n",
" <td>https://en.wikipedia.org/wiki/Video_game</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A video game is an electronic game that involv...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>497487</td>\n",
" <td>/m/01jddz</td>\n",
" <td>Concert</td>\n",
" <td>https://en.wikipedia.org/wiki/Concert</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A concert is a live music performance in front...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>373952</td>\n",
" <td>/m/0k4j</td>\n",
" <td>Car</td>\n",
" <td>https://en.wikipedia.org/wiki/Car</td>\n",
" <td>Autos & Vehicles</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A car is a wheeled, self-powered motor vehicle...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>305143</td>\n",
" <td>/m/026bk</td>\n",
" <td>Dance</td>\n",
" <td>https://en.wikipedia.org/wiki/Dance</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Dance is a performance art form consisting of ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>299054</td>\n",
" <td>/m/0hcr</td>\n",
" <td>Animation</td>\n",
" <td>https://en.wikipedia.org/wiki/Animation</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Animation is the process of making the illusio...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>296577</td>\n",
" <td>/m/09jwl</td>\n",
" <td>Musician</td>\n",
" <td>https://en.wikipedia.org/wiki/Musician</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A musician is a person who plays a musical ins...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>219506</td>\n",
" <td>/m/02vx4</td>\n",
" <td>Association football</td>\n",
" <td>https://en.wikipedia.org/wiki/Association_foot...</td>\n",
" <td>Sports</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Association football, more commonly known as f...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>217037</td>\n",
" <td>/m/0mdxd</td>\n",
" <td>Music video</td>\n",
" <td>https://en.wikipedia.org/wiki/Music_video</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A music video is a short film integrating a so...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>203700</td>\n",
" <td>/m/0jbk</td>\n",
" <td>Animal</td>\n",
" <td>https://en.wikipedia.org/wiki/Animal</td>\n",
" <td>Pets & Animals</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Animals are multicellular, eukaryotic organism...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>198630</td>\n",
" <td>/m/0410tth</td>\n",
" <td>Motorsport</td>\n",
" <td>https://en.wikipedia.org/wiki/Motorsport</td>\n",
" <td>Sports</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Motorsport or motorsports is a global term use...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>196108</td>\n",
" <td>/m/02wbm</td>\n",
" <td>Food</td>\n",
" <td>https://en.wikipedia.org/wiki/Food</td>\n",
" <td>Food & Drink</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Food is any substance consumed to provide nutr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>170351</td>\n",
" <td>/m/05229</td>\n",
" <td>Musical ensemble</td>\n",
" <td>https://en.wikipedia.org/wiki/Musical_ensemble</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A musical ensemble, also known as a music grou...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>162864</td>\n",
" <td>/m/0342h</td>\n",
" <td>Guitar</td>\n",
" <td>https://en.wikipedia.org/wiki/Guitar</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>The guitar is a musical instrument classified ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>162157</td>\n",
" <td>/m/0215n</td>\n",
" <td>Cartoon</td>\n",
" <td>https://en.wikipedia.org/wiki/Cartoon</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A cartoon is a type of two-dimensional illustr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>16</td>\n",
" <td>160383</td>\n",
" <td>/m/01350r</td>\n",
" <td>Performance art</td>\n",
" <td>https://en.wikipedia.org/wiki/Performance_art</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Performance art is a performance presented to ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>17</td>\n",
" <td>121318</td>\n",
" <td>/m/0dfbw</td>\n",
" <td>Racing</td>\n",
" <td>https://en.wikipedia.org/wiki/Racing</td>\n",
" <td>Sports</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>In sport, racing is a competition of speed, ag...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>18</td>\n",
" <td>110787</td>\n",
" <td>/m/05b0n7k</td>\n",
" <td>Outdoor recreation</td>\n",
" <td>https://en.wikipedia.org/wiki/Outdoor_recreation</td>\n",
" <td>Hobbies & Leisure</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Outdoor recreation or outdoor activity refers ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>107038</td>\n",
" <td>/m/04tr8s</td>\n",
" <td>PC game</td>\n",
" <td>https://en.wikipedia.org/wiki/PC_game</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PC games, also known as computer games or pers...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20</td>\n",
" <td>97912</td>\n",
" <td>/m/03hdbf</td>\n",
" <td>Trailer (promotion)</td>\n",
" <td>https://en.wikipedia.org/wiki/Trailer_(promotion)</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A trailer is an advertisement or a commercial ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>21</td>\n",
" <td>94966</td>\n",
" <td>/m/019cfy</td>\n",
" <td>Stadium</td>\n",
" <td>https://en.wikipedia.org/wiki/Stadium</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A stadium is a place or venue for outdoor spor...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>22</td>\n",
" <td>92866</td>\n",
" <td>/m/05h0n</td>\n",
" <td>Nature</td>\n",
" <td>https://en.wikipedia.org/wiki/Nature</td>\n",
" <td>Science</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Nature, in the broadest sense, is the natural,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>23</td>\n",
" <td>90233</td>\n",
" <td>/m/050k8</td>\n",
" <td>Mobile phone</td>\n",
" <td>https://en.wikipedia.org/wiki/Mobile_phone</td>\n",
" <td>Internet & Telecom</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A mobile phone is a portable telephone that ca...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>24</td>\n",
" <td>85888</td>\n",
" <td>/m/0d8lm</td>\n",
" <td>String instrument</td>\n",
" <td>https://en.wikipedia.org/wiki/String_instrument</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>String instruments, stringed instruments, or c...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>25</td>\n",
" <td>84833</td>\n",
" <td>/m/0138tl</td>\n",
" <td>Toy</td>\n",
" <td>https://en.wikipedia.org/wiki/Toy</td>\n",
" <td>Shopping</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A toy is an item that is generally used for ch...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>26</td>\n",
" <td>84600</td>\n",
" <td>/m/01mtb</td>\n",
" <td>Cooking</td>\n",
" <td>https://en.wikipedia.org/wiki/Cooking</td>\n",
" <td>Food & Drink</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cooking or cookery is the art, technology and ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>27</td>\n",
" <td>84366</td>\n",
" <td>/m/04_sv</td>\n",
" <td>Motorcycle</td>\n",
" <td>https://en.wikipedia.org/wiki/Motorcycle</td>\n",
" <td>Autos & Vehicles</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A motorcycle is a two- or three-wheeled motor ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>28</td>\n",
" <td>84355</td>\n",
" <td>/m/032tl</td>\n",
" <td>Fashion</td>\n",
" <td>https://en.wikipedia.org/wiki/Fashion</td>\n",
" <td>Beauty & Fitness</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Fashion is a popular style or practice, especi...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>29</td>\n",
" <td>82217</td>\n",
" <td>/m/0169zh</td>\n",
" <td>Smartphone</td>\n",
" <td>https://en.wikipedia.org/wiki/Smartphone</td>\n",
" <td>Internet & Telecom</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A smartphone is a mobile phone with an advance...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4686</th>\n",
" <td>4686</td>\n",
" <td>113</td>\n",
" <td>/m/0k3hjq7</td>\n",
" <td>Nisekoi</td>\n",
" <td>https://en.wikipedia.org/wiki/Nisekoi</td>\n",
" <td>(Unknown)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Nisekoi, released in English as Nisekoi: False...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4687</th>\n",
" <td>4687</td>\n",
" <td>113</td>\n",
" <td>/m/048hm5</td>\n",
" <td>Hollywood Palladium</td>\n",
" <td>https://en.wikipedia.org/wiki/Hollywood_Palladium</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Hollywood Palladium is a theater located at 62...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4688</th>\n",
" <td>4688</td>\n",
" <td>112</td>\n",
" <td>/m/0gtw3vh</td>\n",
" <td>Skullgirls</td>\n",
" <td>https://en.wikipedia.org/wiki/Skullgirls</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Skullgirls is a 2D fighting game developed by ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4689</th>\n",
" <td>4689</td>\n",
" <td>112</td>\n",
" <td>/m/024902</td>\n",
" <td>Al-Masjid an-Nabawi</td>\n",
" <td>https://en.wikipedia.org/wiki/Al-Masjid_an-Nabawi</td>\n",
" <td>Travel</td>\n",
" <td>People & Society</td>\n",
" <td>NaN</td>\n",
" <td>Al-Masjid an-Nabawī is a mosque established an...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4690</th>\n",
" <td>4690</td>\n",
" <td>111</td>\n",
" <td>/m/0fv8j0</td>\n",
" <td>Shadow Fighter (video game)</td>\n",
" <td>https://en.wikipedia.org/wiki/Shadow_Fighter_(...</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Shadow Fighter is a computer game for the Comm...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4691</th>\n",
" <td>4691</td>\n",
" <td>111</td>\n",
" <td>/m/044p09</td>\n",
" <td>Reptile (Mortal Kombat)</td>\n",
" <td>https://en.wikipedia.org/wiki/Reptile_(Mortal_...</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Reptile is a video game character from the Mor...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4692</th>\n",
" <td>4692</td>\n",
" <td>111</td>\n",
" <td>/m/02z2m_</td>\n",
" <td>Abercrombie & Fitch</td>\n",
" <td>https://en.wikipedia.org/wiki/Abercrombie_%26_...</td>\n",
" <td>Shopping</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Abercrombie & Fitch is an American retailer th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4693</th>\n",
" <td>4693</td>\n",
" <td>110</td>\n",
" <td>/m/0dng4</td>\n",
" <td>Daffy Duck</td>\n",
" <td>https://en.wikipedia.org/wiki/Daffy_Duck</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Daffy Duck is an animated cartoon character pr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4694</th>\n",
" <td>4694</td>\n",
" <td>110</td>\n",
" <td>/m/032nch</td>\n",
" <td>Cruelty to animals</td>\n",
" <td>https://en.wikipedia.org/wiki/Cruelty_to_animals</td>\n",
" <td>Pets & Animals</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cruelty to animals, also called animal abuse o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4695</th>\n",
" <td>4695</td>\n",
" <td>110</td>\n",
" <td>/m/01k796</td>\n",
" <td>Strikeout</td>\n",
" <td>https://en.wikipedia.org/wiki/Strikeout</td>\n",
" <td>Sports</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>In baseball or softball, a strikeout occurs wh...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4696</th>\n",
" <td>4696</td>\n",
" <td>109</td>\n",
" <td>/m/0pjz3</td>\n",
" <td>Arjuna</td>\n",
" <td>https://en.wikipedia.org/wiki/Arjuna</td>\n",
" <td>People & Society</td>\n",
" <td>Reference</td>\n",
" <td>NaN</td>\n",
" <td>Arjuna along with Krishna is the protagonist o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4697</th>\n",
" <td>4697</td>\n",
" <td>108</td>\n",
" <td>/m/0661mn1</td>\n",
" <td>Scarlet Weather Rhapsody</td>\n",
" <td>https://en.wikipedia.org/wiki/Scarlet_Weather_...</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Touhou Hisouten ~ Scarlet Weather Rhapsody. is...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4698</th>\n",
" <td>4698</td>\n",
" <td>108</td>\n",
" <td>/m/02z1c71</td>\n",
" <td>Voetbal International</td>\n",
" <td>https://en.wikipedia.org/wiki/Voetbal_Internat...</td>\n",
" <td>Books & Literature</td>\n",
" <td>Sports</td>\n",
" <td>NaN</td>\n",
" <td>Voetbal International is a Dutch football maga...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4699</th>\n",
" <td>4699</td>\n",
" <td>108</td>\n",
" <td>/m/01n2m6</td>\n",
" <td>Death Row Records</td>\n",
" <td>https://en.wikipedia.org/wiki/Death_Row_Records</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Death Row Records is a record company founded ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4700</th>\n",
" <td>4700</td>\n",
" <td>108</td>\n",
" <td>/m/02p1p9m</td>\n",
" <td>Shashlik</td>\n",
" <td>https://en.wikipedia.org/wiki/Shashlik</td>\n",
" <td>Food & Drink</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Shashlik or shashlyk, is a dish of skewered an...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4701</th>\n",
" <td>4701</td>\n",
" <td>107</td>\n",
" <td>/m/01tcs0</td>\n",
" <td>Scarecrow (DC Comics)</td>\n",
" <td>https://en.wikipedia.org/wiki/Scarecrow_(DC_Co...</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>The Scarecrow is a fictional supervillain appe...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4702</th>\n",
" <td>4702</td>\n",
" <td>107</td>\n",
" <td>/m/0_83vt5</td>\n",
" <td>Akame ga Kill!</td>\n",
" <td>https://en.wikipedia.org/wiki/Akame_ga_Kill!</td>\n",
" <td>(Unknown)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Akame ga Kill! is a Japanese shōnen manga seri...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4703</th>\n",
" <td>4703</td>\n",
" <td>107</td>\n",
" <td>/m/0c3_50s</td>\n",
" <td>DJ Hero 2</td>\n",
" <td>https://en.wikipedia.org/wiki/DJ_Hero_2</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>DJ Hero 2 is a rhythm video game and a sequel ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4704</th>\n",
" <td>4704</td>\n",
" <td>106</td>\n",
" <td>/m/0mbct</td>\n",
" <td>Gong</td>\n",
" <td>https://en.wikipedia.org/wiki/Gong</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A gong is an African, East and South East Asia...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4705</th>\n",
" <td>4705</td>\n",
" <td>105</td>\n",
" <td>/m/0lq1lzn</td>\n",
" <td>SCP – Containment Breach</td>\n",
" <td>https://en.wikipedia.org/wiki/SCP_%E2%80%93_Co...</td>\n",
" <td>(Unknown)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>SCP – Containment Breach is an indie supernatu...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4706</th>\n",
" <td>4706</td>\n",
" <td>104</td>\n",
" <td>/m/0875jr</td>\n",
" <td>Friday the 13th (franchise)</td>\n",
" <td>https://en.wikipedia.org/wiki/Friday_the_13th_...</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Friday the 13th is an American horror franchis...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4707</th>\n",
" <td>4707</td>\n",
" <td>104</td>\n",
" <td>/m/03qtgn</td>\n",
" <td>Poncho</td>\n",
" <td>https://en.wikipedia.org/wiki/Poncho</td>\n",
" <td>Shopping</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A poncho is an outer garment designed to keep ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4708</th>\n",
" <td>4708</td>\n",
" <td>103</td>\n",
" <td>/m/02stb2</td>\n",
" <td>Visual novel</td>\n",
" <td>https://en.wikipedia.org/wiki/Visual_novel</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A visual novel is an interactive game, featuri...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4709</th>\n",
" <td>4709</td>\n",
" <td>103</td>\n",
" <td>/m/09f6b</td>\n",
" <td>Luge</td>\n",
" <td>https://en.wikipedia.org/wiki/Luge</td>\n",
" <td>Sports</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A luge /ˈluːʒ/ is a small one- or two-person s...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4710</th>\n",
" <td>4710</td>\n",
" <td>103</td>\n",
" <td>/m/04rky</td>\n",
" <td>Mammal</td>\n",
" <td>https://en.wikipedia.org/wiki/Mammal</td>\n",
" <td>Science</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mammals are any vertebrates within the class M...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4711</th>\n",
" <td>4711</td>\n",
" <td>103</td>\n",
" <td>/m/06mk92</td>\n",
" <td>Aston Martin Vantage (2005)</td>\n",
" <td>https://en.wikipedia.org/wiki/Aston_Martin_Van...</td>\n",
" <td>Autos & Vehicles</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>The Aston Martin Vantage is series of hand-bui...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4712</th>\n",
" <td>4712</td>\n",
" <td>102</td>\n",
" <td>/m/048ytx</td>\n",
" <td>Russian pyramid</td>\n",
" <td>https://en.wikipedia.org/wiki/Russian_pyramid</td>\n",
" <td>Games</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Russian pyramid, also known as Russian billiar...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4713</th>\n",
" <td>4713</td>\n",
" <td>101</td>\n",
" <td>/m/06_zh7</td>\n",
" <td>PBS Kids</td>\n",
" <td>https://en.wikipedia.org/wiki/PBS_Kids</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>People & Society</td>\n",
" <td>NaN</td>\n",
" <td>PBS Kids, stylized as PBS KIDS and formerly PT...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4714</th>\n",
" <td>4714</td>\n",
" <td>101</td>\n",
" <td>/m/089w_2</td>\n",
" <td>Air Gear</td>\n",
" <td>https://en.wikipedia.org/wiki/Air_Gear</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Air Gear is a shōnen manga written and illustr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4715</th>\n",
" <td>4715</td>\n",
" <td>101</td>\n",
" <td>/m/02qg86l</td>\n",
" <td>Pina Records</td>\n",
" <td>https://en.wikipedia.org/wiki/Pina_Records</td>\n",
" <td>Arts & Entertainment</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Pina Records is a Puerto Rican record label fo...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4716 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Index TrainVideoCount KnowledgeGraphId Name \\\n",
"0 0 865174 /m/03bt1gh Game \n",
"1 1 683166 /m/07yv9 Vehicle \n",
"2 2 522427 /m/01mw1 Video game \n",
"3 3 497487 /m/01jddz Concert \n",
"4 4 373952 /m/0k4j Car \n",
"5 5 305143 /m/026bk Dance \n",
"6 6 299054 /m/0hcr Animation \n",
"7 7 296577 /m/09jwl Musician \n",
"8 8 219506 /m/02vx4 Association football \n",
"9 9 217037 /m/0mdxd Music video \n",
"10 10 203700 /m/0jbk Animal \n",
"11 11 198630 /m/0410tth Motorsport \n",
"12 12 196108 /m/02wbm Food \n",
"13 13 170351 /m/05229 Musical ensemble \n",
"14 14 162864 /m/0342h Guitar \n",
"15 15 162157 /m/0215n Cartoon \n",
"16 16 160383 /m/01350r Performance art \n",
"17 17 121318 /m/0dfbw Racing \n",
"18 18 110787 /m/05b0n7k Outdoor recreation \n",
"19 19 107038 /m/04tr8s PC game \n",
"20 20 97912 /m/03hdbf Trailer (promotion) \n",
"21 21 94966 /m/019cfy Stadium \n",
"22 22 92866 /m/05h0n Nature \n",
"23 23 90233 /m/050k8 Mobile phone \n",
"24 24 85888 /m/0d8lm String instrument \n",
"25 25 84833 /m/0138tl Toy \n",
"26 26 84600 /m/01mtb Cooking \n",
"27 27 84366 /m/04_sv Motorcycle \n",
"28 28 84355 /m/032tl Fashion \n",
"29 29 82217 /m/0169zh Smartphone \n",
"... ... ... ... ... \n",
"4686 4686 113 /m/0k3hjq7 Nisekoi \n",
"4687 4687 113 /m/048hm5 Hollywood Palladium \n",
"4688 4688 112 /m/0gtw3vh Skullgirls \n",
"4689 4689 112 /m/024902 Al-Masjid an-Nabawi \n",
"4690 4690 111 /m/0fv8j0 Shadow Fighter (video game) \n",
"4691 4691 111 /m/044p09 Reptile (Mortal Kombat) \n",
"4692 4692 111 /m/02z2m_ Abercrombie & Fitch \n",
"4693 4693 110 /m/0dng4 Daffy Duck \n",
"4694 4694 110 /m/032nch Cruelty to animals \n",
"4695 4695 110 /m/01k796 Strikeout \n",
"4696 4696 109 /m/0pjz3 Arjuna \n",
"4697 4697 108 /m/0661mn1 Scarlet Weather Rhapsody \n",
"4698 4698 108 /m/02z1c71 Voetbal International \n",
"4699 4699 108 /m/01n2m6 Death Row Records \n",
"4700 4700 108 /m/02p1p9m Shashlik \n",
"4701 4701 107 /m/01tcs0 Scarecrow (DC Comics) \n",
"4702 4702 107 /m/0_83vt5 Akame ga Kill! \n",
"4703 4703 107 /m/0c3_50s DJ Hero 2 \n",
"4704 4704 106 /m/0mbct Gong \n",
"4705 4705 105 /m/0lq1lzn SCP – Containment Breach \n",
"4706 4706 104 /m/0875jr Friday the 13th (franchise) \n",
"4707 4707 104 /m/03qtgn Poncho \n",
"4708 4708 103 /m/02stb2 Visual novel \n",
"4709 4709 103 /m/09f6b Luge \n",
"4710 4710 103 /m/04rky Mammal \n",
"4711 4711 103 /m/06mk92 Aston Martin Vantage (2005) \n",
"4712 4712 102 /m/048ytx Russian pyramid \n",
"4713 4713 101 /m/06_zh7 PBS Kids \n",
"4714 4714 101 /m/089w_2 Air Gear \n",
"4715 4715 101 /m/02qg86l Pina Records \n",
"\n",
" WikiUrl Vertical1 \\\n",
"0 https://en.wikipedia.org/wiki/Game Games \n",
"1 https://en.wikipedia.org/wiki/Vehicle Autos & Vehicles \n",
"2 https://en.wikipedia.org/wiki/Video_game Games \n",
"3 https://en.wikipedia.org/wiki/Concert Arts & Entertainment \n",
"4 https://en.wikipedia.org/wiki/Car Autos & Vehicles \n",
"5 https://en.wikipedia.org/wiki/Dance Arts & Entertainment \n",
"6 https://en.wikipedia.org/wiki/Animation Arts & Entertainment \n",
"7 https://en.wikipedia.org/wiki/Musician Arts & Entertainment \n",
"8 https://en.wikipedia.org/wiki/Association_foot... Sports \n",
"9 https://en.wikipedia.org/wiki/Music_video Arts & Entertainment \n",
"10 https://en.wikipedia.org/wiki/Animal Pets & Animals \n",
"11 https://en.wikipedia.org/wiki/Motorsport Sports \n",
"12 https://en.wikipedia.org/wiki/Food Food & Drink \n",
"13 https://en.wikipedia.org/wiki/Musical_ensemble Arts & Entertainment \n",
"14 https://en.wikipedia.org/wiki/Guitar Arts & Entertainment \n",
"15 https://en.wikipedia.org/wiki/Cartoon Arts & Entertainment \n",
"16 https://en.wikipedia.org/wiki/Performance_art Arts & Entertainment \n",
"17 https://en.wikipedia.org/wiki/Racing Sports \n",
"18 https://en.wikipedia.org/wiki/Outdoor_recreation Hobbies & Leisure \n",
"19 https://en.wikipedia.org/wiki/PC_game Games \n",
"20 https://en.wikipedia.org/wiki/Trailer_(promotion) Arts & Entertainment \n",
"21 https://en.wikipedia.org/wiki/Stadium Arts & Entertainment \n",
"22 https://en.wikipedia.org/wiki/Nature Science \n",
"23 https://en.wikipedia.org/wiki/Mobile_phone Internet & Telecom \n",
"24 https://en.wikipedia.org/wiki/String_instrument Arts & Entertainment \n",
"25 https://en.wikipedia.org/wiki/Toy Shopping \n",
"26 https://en.wikipedia.org/wiki/Cooking Food & Drink \n",
"27 https://en.wikipedia.org/wiki/Motorcycle Autos & Vehicles \n",
"28 https://en.wikipedia.org/wiki/Fashion Beauty & Fitness \n",
"29 https://en.wikipedia.org/wiki/Smartphone Internet & Telecom \n",
"... ... ... \n",
"4686 https://en.wikipedia.org/wiki/Nisekoi (Unknown) \n",
"4687 https://en.wikipedia.org/wiki/Hollywood_Palladium Arts & Entertainment \n",
"4688 https://en.wikipedia.org/wiki/Skullgirls Games \n",
"4689 https://en.wikipedia.org/wiki/Al-Masjid_an-Nabawi Travel \n",
"4690 https://en.wikipedia.org/wiki/Shadow_Fighter_(... Games \n",
"4691 https://en.wikipedia.org/wiki/Reptile_(Mortal_... Games \n",
"4692 https://en.wikipedia.org/wiki/Abercrombie_%26_... Shopping \n",
"4693 https://en.wikipedia.org/wiki/Daffy_Duck Arts & Entertainment \n",
"4694 https://en.wikipedia.org/wiki/Cruelty_to_animals Pets & Animals \n",
"4695 https://en.wikipedia.org/wiki/Strikeout Sports \n",
"4696 https://en.wikipedia.org/wiki/Arjuna People & Society \n",
"4697 https://en.wikipedia.org/wiki/Scarlet_Weather_... Arts & Entertainment \n",
"4698 https://en.wikipedia.org/wiki/Voetbal_Internat... Books & Literature \n",
"4699 https://en.wikipedia.org/wiki/Death_Row_Records Arts & Entertainment \n",
"4700 https://en.wikipedia.org/wiki/Shashlik Food & Drink \n",
"4701 https://en.wikipedia.org/wiki/Scarecrow_(DC_Co... Arts & Entertainment \n",
"4702 https://en.wikipedia.org/wiki/Akame_ga_Kill! (Unknown) \n",
"4703 https://en.wikipedia.org/wiki/DJ_Hero_2 Games \n",
"4704 https://en.wikipedia.org/wiki/Gong Arts & Entertainment \n",
"4705 https://en.wikipedia.org/wiki/SCP_%E2%80%93_Co... (Unknown) \n",
"4706 https://en.wikipedia.org/wiki/Friday_the_13th_... Arts & Entertainment \n",
"4707 https://en.wikipedia.org/wiki/Poncho Shopping \n",
"4708 https://en.wikipedia.org/wiki/Visual_novel Games \n",
"4709 https://en.wikipedia.org/wiki/Luge Sports \n",
"4710 https://en.wikipedia.org/wiki/Mammal Science \n",
"4711 https://en.wikipedia.org/wiki/Aston_Martin_Van... Autos & Vehicles \n",
"4712 https://en.wikipedia.org/wiki/Russian_pyramid Games \n",
"4713 https://en.wikipedia.org/wiki/PBS_Kids Arts & Entertainment \n",
"4714 https://en.wikipedia.org/wiki/Air_Gear Arts & Entertainment \n",
"4715 https://en.wikipedia.org/wiki/Pina_Records Arts & Entertainment \n",
"\n",
" Vertical2 Vertical3 \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 NaN NaN \n",
"7 NaN NaN \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"10 NaN NaN \n",
"11 NaN NaN \n",
"12 NaN NaN \n",
"13 NaN NaN \n",
"14 NaN NaN \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN NaN \n",
"19 NaN NaN \n",
"20 NaN NaN \n",
"21 NaN NaN \n",
"22 NaN NaN \n",
"23 NaN NaN \n",
"24 NaN NaN \n",
"25 NaN NaN \n",
"26 NaN NaN \n",
"27 NaN NaN \n",
"28 NaN NaN \n",
"29 NaN NaN \n",
"... ... ... \n",
"4686 NaN NaN \n",
"4687 NaN NaN \n",
"4688 NaN NaN \n",
"4689 People & Society NaN \n",
"4690 NaN NaN \n",
"4691 NaN NaN \n",
"4692 NaN NaN \n",
"4693 NaN NaN \n",
"4694 NaN NaN \n",
"4695 NaN NaN \n",
"4696 Reference NaN \n",
"4697 NaN NaN \n",
"4698 Sports NaN \n",
"4699 NaN NaN \n",
"4700 NaN NaN \n",
"4701 NaN NaN \n",
"4702 NaN NaN \n",
"4703 NaN NaN \n",
"4704 NaN NaN \n",
"4705 NaN NaN \n",
"4706 NaN NaN \n",
"4707 NaN NaN \n",
"4708 NaN NaN \n",
"4709 NaN NaN \n",
"4710 NaN NaN \n",
"4711 NaN NaN \n",
"4712 NaN NaN \n",
"4713 People & Society NaN \n",
"4714 NaN NaN \n",
"4715 NaN NaN \n",
"\n",
" WikiDescription \n",
"0 A game is structured form of play, usually und... \n",
"1 A vehicle is a mobile machine that transports ... \n",
"2 A video game is an electronic game that involv... \n",
"3 A concert is a live music performance in front... \n",
"4 A car is a wheeled, self-powered motor vehicle... \n",
"5 Dance is a performance art form consisting of ... \n",
"6 Animation is the process of making the illusio... \n",
"7 A musician is a person who plays a musical ins... \n",
"8 Association football, more commonly known as f... \n",
"9 A music video is a short film integrating a so... \n",
"10 Animals are multicellular, eukaryotic organism... \n",
"11 Motorsport or motorsports is a global term use... \n",
"12 Food is any substance consumed to provide nutr... \n",
"13 A musical ensemble, also known as a music grou... \n",
"14 The guitar is a musical instrument classified ... \n",
"15 A cartoon is a type of two-dimensional illustr... \n",
"16 Performance art is a performance presented to ... \n",
"17 In sport, racing is a competition of speed, ag... \n",
"18 Outdoor recreation or outdoor activity refers ... \n",
"19 PC games, also known as computer games or pers... \n",
"20 A trailer is an advertisement or a commercial ... \n",
"21 A stadium is a place or venue for outdoor spor... \n",
"22 Nature, in the broadest sense, is the natural,... \n",
"23 A mobile phone is a portable telephone that ca... \n",
"24 String instruments, stringed instruments, or c... \n",
"25 A toy is an item that is generally used for ch... \n",
"26 Cooking or cookery is the art, technology and ... \n",
"27 A motorcycle is a two- or three-wheeled motor ... \n",
"28 Fashion is a popular style or practice, especi... \n",
"29 A smartphone is a mobile phone with an advance... \n",
"... ... \n",
"4686 Nisekoi, released in English as Nisekoi: False... \n",
"4687 Hollywood Palladium is a theater located at 62... \n",
"4688 Skullgirls is a 2D fighting game developed by ... \n",
"4689 Al-Masjid an-Nabawī is a mosque established an... \n",
"4690 Shadow Fighter is a computer game for the Comm... \n",
"4691 Reptile is a video game character from the Mor... \n",
"4692 Abercrombie & Fitch is an American retailer th... \n",
"4693 Daffy Duck is an animated cartoon character pr... \n",
"4694 Cruelty to animals, also called animal abuse o... \n",
"4695 In baseball or softball, a strikeout occurs wh... \n",
"4696 Arjuna along with Krishna is the protagonist o... \n",
"4697 Touhou Hisouten ~ Scarlet Weather Rhapsody. is... \n",
"4698 Voetbal International is a Dutch football maga... \n",
"4699 Death Row Records is a record company founded ... \n",
"4700 Shashlik or shashlyk, is a dish of skewered an... \n",
"4701 The Scarecrow is a fictional supervillain appe... \n",
"4702 Akame ga Kill! is a Japanese shōnen manga seri... \n",
"4703 DJ Hero 2 is a rhythm video game and a sequel ... \n",
"4704 A gong is an African, East and South East Asia... \n",
"4705 SCP – Containment Breach is an indie supernatu... \n",
"4706 Friday the 13th is an American horror franchis... \n",
"4707 A poncho is an outer garment designed to keep ... \n",
"4708 A visual novel is an interactive game, featuri... \n",
"4709 A luge /ˈluːʒ/ is a small one- or two-person s... \n",
"4710 Mammals are any vertebrates within the class M... \n",
"4711 The Aston Martin Vantage is series of hand-bui... \n",
"4712 Russian pyramid, also known as Russian billiar... \n",
"4713 PBS Kids, stylized as PBS KIDS and formerly PT... \n",
"4714 Air Gear is a shōnen manga written and illustr... \n",
"4715 Pina Records is a Puerto Rican record label fo... \n",
"\n",
"[4716 rows x 9 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vocabulary"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vertical_dict = {}\n",
"with open(\"vertical.tsv\", \"w\") as F:\n",
" lines = []\n",
" for num, item in vocabulary.iterrows():\n",
" index = item[\"Index\"]\n",
" vertical = item[\"Vertical1\"]\n",
" if vertical not in vertical_dict:\n",
" vertical_dict[vertical] = len(vertical_dict)\n",
" lines.append(\"%d\\t%d\\n\" % (index, vertical_dict[vertical]))\n",
" F.writelines(lines)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"25\n"
]
}
],
"source": [
"print len(vertical_dict)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training labels"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>--DwgB78t-c</td>\n",
" <td>16 5 430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>--NZRkXBV7k</td>\n",
" <td>128 3 39 7 44 13 16 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>--hoQ2sGG4M</td>\n",
" <td>694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>--ABhs9ik7c</td>\n",
" <td>2232 1 4 517 270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>--sBoaqBlzA</td>\n",
" <td>45 125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>--7h1S4neDM</td>\n",
" <td>0 48 10 356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>--F672jfCMo</td>\n",
" <td>488 1067 1078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>--ezS5q-mZg</td>\n",
" <td>1 962 12 625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>--XT8O4T3Wc</td>\n",
" <td>379 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>--Qgwg7mGZY</td>\n",
" <td>0 33 2 2379 3423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>--_I8vffnIw</td>\n",
" <td>768 37 72 330 331 29 373 23 189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>--VDzNzHHic</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>--pQCGgGjE8</td>\n",
" <td>0 2 836 432 665 2779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>--amuSGpp94</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>--mkDBHaFIs</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>--DyrrdZbUk</td>\n",
" <td>1 114 1395 4 3409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>--cPdta0k4Y</td>\n",
" <td>276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>--5iMsHNuDE</td>\n",
" <td>1206 6 967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>--f5fEIOW1Q</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>--jcB8BTawk</td>\n",
" <td>5 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>--0soWyo5gQ</td>\n",
" <td>1 403 4 1511 975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>--8MxffdH9k</td>\n",
" <td>1 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>--j9Z0JKrjE</td>\n",
" <td>193 436 45 173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>--o719vqu6Q</td>\n",
" <td>0 49 168 60 54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>--p02WMptcA</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>--0pQywa6OM</td>\n",
" <td>1139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>--sW2Fk-LmY</td>\n",
" <td>0 2 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>--iEJ1NTTHQ</td>\n",
" <td>8 813 79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>--39-Ml5Ylw</td>\n",
" <td>99 3581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>--udooZCtNE</td>\n",
" <td>200 25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906630</th>\n",
" <td>zzCGK4T43sI</td>\n",
" <td>16 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906631</th>\n",
" <td>zz9AvyGAFhg</td>\n",
" <td>0 2 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906632</th>\n",
" <td>zzAhVFy3o5s</td>\n",
" <td>233 222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906633</th>\n",
" <td>zzhKCCjBtN0</td>\n",
" <td>208 170 830 246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906634</th>\n",
" <td>zzQfq7MBrfk</td>\n",
" <td>80 10 438 247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906635</th>\n",
" <td>zz5H1l4fTRs</td>\n",
" <td>0 33 2 1382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906636</th>\n",
" <td>zztyezN9m80</td>\n",
" <td>1002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906637</th>\n",
" <td>zz9urhmAd6M</td>\n",
" <td>16 3 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906638</th>\n",
" <td>zzieB3E90oc</td>\n",
" <td>49 3 13 38 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906639</th>\n",
" <td>zz-AlFFQAbI</td>\n",
" <td>354 713 12 2482 466 1047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906640</th>\n",
" <td>zzYbFq5qF8w</td>\n",
" <td>400 2017 972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906641</th>\n",
" <td>zzyTPLaoWxo</td>\n",
" <td>3 38 39 7 13 49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906642</th>\n",
" <td>zzp9Q2bQhck</td>\n",
" <td>1 4 202 11 17 164 92 447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906643</th>\n",
" <td>zz4L8mQ0Zr8</td>\n",
" <td>112 59 316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906644</th>\n",
" <td>zzxF2j31aBE</td>\n",
" <td>1 1323 11 172 17 278 728 57 27 669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906645</th>\n",
" <td>zzTw-YSIp1g</td>\n",
" <td>163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906646</th>\n",
" <td>zznsbYnqvyU</td>\n",
" <td>0 2 34 1069 19 118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906647</th>\n",
" <td>zz9z1DR2RXA</td>\n",
" <td>24 14 63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906648</th>\n",
" <td>zzk5fGf0nMw</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906649</th>\n",
" <td>zzrCAKdNth4</td>\n",
" <td>744 1794 3182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906650</th>\n",
" <td>zzX28lrE9Fc</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906651</th>\n",
" <td>zzMFAaqNGX4</td>\n",
" <td>208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906652</th>\n",
" <td>zzhc0nTbC4Q</td>\n",
" <td>49 5 95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906653</th>\n",
" <td>zztFMPeS4KU</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906654</th>\n",
" <td>zz5R0c6JHBk</td>\n",
" <td>56 106 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906655</th>\n",
" <td>zzRKzJ5iCRs</td>\n",
" <td>416 113 652 339 580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906656</th>\n",
" <td>zzDg9v6733A</td>\n",
" <td>1 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906657</th>\n",
" <td>zzZl6-AxXv8</td>\n",
" <td>0 60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906658</th>\n",
" <td>zzoW1afkGKk</td>\n",
" <td>2033 1083 3959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4906659</th>\n",
" <td>zzfubODk_8E</td>\n",
" <td>10 6 15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4906660 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" id labels\n",
"0 --DwgB78t-c 16 5 430\n",
"1 --NZRkXBV7k 128 3 39 7 44 13 16 30\n",
"2 --hoQ2sGG4M 694\n",
"3 --ABhs9ik7c 2232 1 4 517 270\n",
"4 --sBoaqBlzA 45 125\n",
"5 --7h1S4neDM 0 48 10 356\n",
"6 --F672jfCMo 488 1067 1078\n",
"7 --ezS5q-mZg 1 962 12 625\n",
"8 --XT8O4T3Wc 379 6\n",
"9 --Qgwg7mGZY 0 33 2 2379 3423\n",
"10 --_I8vffnIw 768 37 72 330 331 29 373 23 189\n",
"11 --VDzNzHHic 20\n",
"12 --pQCGgGjE8 0 2 836 432 665 2779\n",
"13 --amuSGpp94 118\n",
"14 --mkDBHaFIs 6\n",
"15 --DyrrdZbUk 1 114 1395 4 3409\n",
"16 --cPdta0k4Y 276\n",
"17 --5iMsHNuDE 1206 6 967\n",
"18 --f5fEIOW1Q 9\n",
"19 --jcB8BTawk 5 31\n",
"20 --0soWyo5gQ 1 403 4 1511 975\n",
"21 --8MxffdH9k 1 4\n",
"22 --j9Z0JKrjE 193 436 45 173\n",
"23 --o719vqu6Q 0 49 168 60 54\n",
"24 --p02WMptcA 49\n",
"25 --0pQywa6OM 1139\n",
"26 --sW2Fk-LmY 0 2 35\n",
"27 --iEJ1NTTHQ 8 813 79\n",
"28 --39-Ml5Ylw 99 3581\n",
"29 --udooZCtNE 200 25\n",
"... ... ...\n",
"4906630 zzCGK4T43sI 16 3\n",
"4906631 zz9AvyGAFhg 0 2 35\n",
"4906632 zzAhVFy3o5s 233 222\n",
"4906633 zzhKCCjBtN0 208 170 830 246\n",
"4906634 zzQfq7MBrfk 80 10 438 247\n",
"4906635 zz5H1l4fTRs 0 33 2 1382\n",
"4906636 zztyezN9m80 1002\n",
"4906637 zz9urhmAd6M 16 3 5\n",
"4906638 zzieB3E90oc 49 3 13 38 7\n",
"4906639 zz-AlFFQAbI 354 713 12 2482 466 1047\n",
"4906640 zzYbFq5qF8w 400 2017 972\n",
"4906641 zzyTPLaoWxo 3 38 39 7 13 49\n",
"4906642 zzp9Q2bQhck 1 4 202 11 17 164 92 447\n",
"4906643 zz4L8mQ0Zr8 112 59 316\n",
"4906644 zzxF2j31aBE 1 1323 11 172 17 278 728 57 27 669\n",
"4906645 zzTw-YSIp1g 163\n",
"4906646 zznsbYnqvyU 0 2 34 1069 19 118\n",
"4906647 zz9z1DR2RXA 24 14 63\n",
"4906648 zzk5fGf0nMw 8\n",
"4906649 zzrCAKdNth4 744 1794 3182\n",
"4906650 zzX28lrE9Fc 46\n",
"4906651 zzMFAaqNGX4 208\n",
"4906652 zzhc0nTbC4Q 49 5 95\n",
"4906653 zztFMPeS4KU 20\n",
"4906654 zz5R0c6JHBk 56 106 28\n",
"4906655 zzRKzJ5iCRs 416 113 652 339 580\n",
"4906656 zzDg9v6733A 1 4\n",
"4906657 zzZl6-AxXv8 0 60\n",
"4906658 zzoW1afkGKk 2033 1083 3959\n",
"4906659 zzfubODk_8E 10 6 15\n",
"\n",
"[4906660 rows x 2 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### show some of the label combinations"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5-Dance / 16-Performance art / 430-Ballroom dance / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 16-Performance art / 30-Drummer / 39-Drums / 44-Drums / 128-Cymbal / \n",
"694-Tai chi / \n",
"1-Vehicle / 4-Car / 270-Sedan / 517-Audi / 2232-Audi A3 / \n",
"45-Cosmetics / 125-Television / \n",
"0-Games / 10-Animal / 48-Pet / 356-Star / \n",
"488-Door / 1067-Lock / 1078-Installation art / \n",
"1-Vehicle / 12-Food / 625-Factory / 962-McDonald's / \n",
"6-Animation / 379-Touhou Project / \n",
"0-Games / 2-Video game / 33-Weapon / 2379-Tom Clancy's Ghost Recon / 3423-Tom Clancy's Ghost Recon: Future Soldier / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 72-iPhone / 189-iPad / 330-Money / 331-iPod touch / 373-Clash of Clans / 768-Diamond / \n",
"20-Trailer / \n",
"0-Games / 2-Video game / 432-Kingdom Hearts / 665-Kingdom Hearts / 836-Sora / 2779-Universe of Kingdom Hearts / \n",
"118-Grand Theft Auto V / \n",
"6-Animation / \n",
"1-Vehicle / 4-Car / 114-Engine / 1395-Valve / 3409-Nissan Altima / \n",
"276-Room / \n",
"6-Animation / 967-Text / 1206-Cinema 4D / \n",
"9-Music video / \n",
"5-Dance / 31-Disc jockey / \n",
"1-Vehicle / 4-Car / 403-Toyota / 975-Dune / 1511-Toyota Land Cruiser / \n",
"1-Vehicle / 4-Car / \n",
"45-Cosmetics / 173-Eye shadow / 193-Eye liner / 436-Concealer / \n",
"0-Games / 49-School / 54-Highlight film / 60-Basketball / 168-Basketball moves / \n",
"49-School / \n",
"1139-Belle / \n",
"0-Games / 2-Video game / 35-Minecraft / \n",
"8-Football / 79-American football / 813-Running back / \n",
"99-Christmas / 3581-Rudolph the Red-Nosed Reindeer / \n",
"25-Toy / 200-Doll / \n",
"1427-TalesRunner / \n",
"983-Lexus / \n",
"59-Winter sport / 84-Snow / 177-Skiing / \n",
"3-Concert / 5-Dance / 16-Performance art / \n",
"3-Concert / 5-Dance / 7-Musician / 13-Musical ensemble / 38-Orchestra / 46-Choir / \n",
"3-Concert / 5-Dance / 68-Lighting / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 40-Road / 41-Sports car / 57-Race track / 135-Drifting / 212-Highway / 231-Supercar / 705-Forza Motorsport / 1530-Forza Motorsport 4 / 2261-Nissan Silvia / \n",
"5-Dance / \n",
"12-Food / 26-Cooking / 32-Recipe / 226-Dessert / 232-Cake / 236-Baking / 295-Chocolate / 534-Milk / 759-Icing / 985-Strawberry / 1728-Chocolate cake / \n",
"2-Video game / 20-Trailer / \n",
"42-Fishing / 902-Fishing bait / 1018-Salmon / \n",
"3-Concert / \n",
"0-Games / 2-Video game / 1027-Terraria / 2821-Cactus / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 25-Toy / 57-Race track / 92-Radio-controlled model / 167-Four-wheel drive / 174-Farm / 202-Radio-controlled car / 718-Duck / 926-Traxxas / \n",
"1-Vehicle / 17-Racing / 27-Motorcycle / 114-Engine / 1036-Moped / \n",
"2-Video game / 47-Personal computer / 216-Call of Duty: Modern Warfare 3 / \n",
"36-Piano / 53-Keyboard / 107-Musical keyboard / 265-Electronic keyboard / 397-Organ / 1265-Kyle Kingson / \n",
"3-Concert / 5-Dance / 68-Lighting / 133-Nightclub / \n",
"42-Fishing / 315-Knife / 510-Fishing lure / 3745-Eel / \n",
"7-Musician / 36-Piano / \n",
"337-Advertising / \n",
"0-Games / 447-Sand / \n",
"6-Animation / 2176-Fist of the North Star / 3349-Kenshiro / \n",
"6-Animation / 15-Cartoon / 911-Studio / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 25-Toy / 92-Radio-controlled model / 135-Drifting / 202-Radio-controlled car / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 40-Road / 70-Driving / 952-Chevrolet Corvette / 2885-Chevrolet Corvette (C6) / \n",
"10-Animal / 91-Fish / 225-Lake / 258-Aquarium / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 124-Tractor / 249-Heavy equipment / 252-Tractor pulling / \n",
"6-Animation / 25-Toy / 269-Comic book / 1049-Collectible card game / \n",
"6-Animation / 15-Cartoon / 723-Pig / \n",
"86-Plant / 136-Gardening / 279-Garden / 363-Soil / \n",
"12-Food / \n",
"0-Games / 1-Vehicle / 2-Video game / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 57-Race track / 70-Driving / 135-Drifting / \n",
"0-Games / 19-PC game / 51-Strategy video game / 165-League of Legends / \n",
"1-Vehicle / 4-Car / 125-Television / 950-Blu-ray disc / \n",
"1-Vehicle / 4-Car / 74-Truck / 158-Tire / 1429-Dump truck / \n",
"28-Fashion / \n",
"253-Carnival / 641-Mask / \n",
"12-Food / \n",
"174-Farm / \n",
"5-Dance / 16-Performance art / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 47-Personal computer / 145-Tablet computer / \n",
"131-Computer / 497-Macintosh / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 57-Race track / 711-Gran Turismo / 790-Nissan GT-R / 1519-Gran Turismo 6 / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 41-Sports car / 231-Supercar / 569-Lamborghini / \n",
"1650-The Idolmaster / 1755-The Idolmaster / \n",
"0-Games / 2-Video game / 55-Video game console / 3217-Neo Geo / \n",
"23-Mobile phone / 29-Smartphone / \n",
"712-Outer space / 992-Rocket / 1508-Spacecraft / 1992-Space Shuttle / \n",
"350-Circus / 788-Juggling / \n",
"1-Vehicle / 50-Aircraft / 82-Airplane / 102-Building / 129-Aviation / 139-Airport / 151-Landing / 157-Jet aircraft / 175-Airliner / 187-Takeoff / 188-Airline / 254-Runway / 1736-Microsoft Flight Simulator / 2136-Boeing 757 / 2391-American Airlines / \n",
"3-Concert / 16-Performance art / \n",
"125-Television / 1172-VHS / \n",
"0-Games / 211-RuneScape / \n",
"46-Choir / 318-Church / \n",
"36-Piano / 53-Keyboard / \n",
"130-Gymnastics / \n",
"45-Cosmetics / 436-Concealer / \n",
"1-Vehicle / 11-Motorsport / 124-Tractor / 156-Agriculture / 174-Farm / 252-Tractor pulling / 617-Maize / \n",
"6-Animation / 15-Cartoon / 4244-Rozen Maiden / \n",
"0-Games / 2-Video game / 19-PC game / 3235-Monopoly / \n",
"7-Musician / 13-Musical ensemble / 46-Choir / 150-Violin / \n",
"1-Vehicle / 50-Aircraft / 129-Aviation / 151-Landing / 451-Cockpit / \n",
"1-Vehicle / 1409-Missile / 1643-Submarine / 3842-Radio-controlled submarine / \n",
"66-Bollywood / \n",
"10-Animal / 80-Horse / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 96-Soldier / 257-Counter-Strike / \n",
"3-Concert / 7-Musician / 30-Drummer / 44-Drums / \n",
"345-Loudspeaker / 484-Subwoofer / \n",
"772-Bowling / 930-Ten-pin bowling / 1217-Bowling ball / \n",
"45-Cosmetics / 238-Nail / 300-Nail art / 304-Nail polish / 307-Hand / 324-Finger / 338-Manicure / \n",
"0-Games / 2-Video game / 43-Call of Duty / 116-Pokémon / 134-Call of Duty: Black Ops / 138-Call of Duty: Black Ops II / 3050-Mewtwo / \n",
"10-Animal / 48-Pet / 71-Dog / 182-Puppy / 2626-Shih Tzu / \n",
"102-Building / 812-Plastic / 1384-Greenhouse / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 72-iPhone / 189-iPad / 272-iPod / 331-iPod touch / 508-iPhone 5 / \n",
"10-Animal / 71-Dog / 1784-Goblin / \n",
"2071-Indiana Jones / \n",
"0-Games / 489-Board game / 2267-Geometry Dash / 2833-Go / \n",
"1-Vehicle / 62-Train / 64-Transport / 224-Rapid transit / \n",
"1-Vehicle / 4-Car / 706-Deer / 955-Police officer / \n",
"14-Guitar / 24-String instrument / 401-Cello / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / 230-Brass instrument / 545-Quartet (ensemble) / 3185-Bronze / \n",
"261-Tool / 521-Metal / 1033-Drill / 1073-Chainsaw / \n",
"9-Music video / \n",
"6-Animation / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 57-Race track / \n",
"6-Animation / \n",
"28-Fashion / 657-Autumn / \n",
"3-Concert / 7-Musician / 1684-Hillsong Church / \n",
"85-Combat / \n",
"67-Cycling / 69-Bicycle / 469-Bag / 835-Backpack / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 30-Drummer / 39-Drums / \n",
"6-Animation / 15-Cartoon / 116-Pokémon / 186-Pokémon / \n",
"6-Animation / 15-Cartoon / 25-Toy / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 167-Four-wheel drive / 198-Off-road vehicle / 327-Mud / 360-Jeep / 565-Mud bogging / \n",
"0-Games / 2-Video game / 19-PC game / 43-Call of Duty / 138-Call of Duty: Black Ops II / \n",
"1-Vehicle / 4-Car / 263-Light / \n",
"113-House / 416-Apartment / \n",
"9-Music video / \n",
"1-Vehicle / 4-Car / 20-Trailer / \n",
"1-Vehicle / 11-Motorsport / \n",
"1-Vehicle / 4-Car / 519-Recreational vehicle / \n",
"1-Vehicle / 25-Toy / 92-Radio-controlled model / 140-LEGO / 202-Radio-controlled car / \n",
"1-Vehicle / 4-Car / 114-Engine / 234-Ford / 473-Ford Mustang / 509-Classic car / \n",
"10-Animal / 208-Wood / 540-Insect / 775-Bee / 784-Honey / 1121-Beehive / 2501-Picture frame / \n",
"0-Games / 211-RuneScape / 1178-Quest / \n",
"5-Dance / 16-Performance art / 115-Ballet / \n",
"7-Musician / 36-Piano / 53-Keyboard / 104-Pianist / 107-Musical keyboard / \n",
"0-Games / 8-Football / 21-Stadium / 73-Ball / \n",
"1-Vehicle / 18-Outdoor recreation / 22-Nature / 67-Cycling / 69-Bicycle / 197-Trail / 220-Mountain bike / 284-Mountain biking / \n",
"8-Football / \n",
"3-Concert / 16-Performance art / \n",
"0-Games / 2-Video game / 111-PlayStation 3 / \n",
"1-Vehicle / 4-Car / 27-Motorcycle / 114-Engine / \n",
"8-Football / 21-Stadium / \n",
"9-Music video / \n",
"5-Dance / 46-Choir / \n",
"1-Vehicle / \n",
"14-Guitar / 24-String instrument / 100-Electric guitar / \n",
"0-Games / 1-Vehicle / 2-Video game / 34-Action-adventure game / 118-Grand Theft Auto V / 176-PlayStation 4 / 181-Xbox / 314-Xbox One / \n",
"1-Vehicle / 50-Aircraft / 82-Airplane / 139-Airport / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 85-Combat / 656-Metal Gear / 2698-Metal Gear Rising: Revengeance / \n",
"20-Trailer / \n",
"0-Games / 6-Animation / 51-Strategy video game / 460-Yu-Gi-Oh! Trading Card Game / \n",
"3-Concert / 16-Performance art / \n",
"9-Music video / \n",
"0-Games / 21-Stadium / 54-Highlight film / 81-Athlete / 562-Pitcher / 1111-Home run / \n",
"6-Animation / \n",
"6-Animation / 15-Cartoon / 89-Comics / 294-Batman / 803-DC Comics / 2082-Catwoman / \n",
"36-Piano / 53-Keyboard / 107-Musical keyboard / 1909-Analog synthesizer / 2521-Jupiter / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 40-Road / 41-Sports car / 70-Driving / 231-Supercar / 344-Coupé / 1056-Porsche 911 / 1965-Porsche Carrera / 2208-Porsche 911 GT3 / \n",
"6-Animation / 15-Cartoon / 801-Inuyasha / \n",
"12-Food / 26-Cooking / 32-Recipe / 232-Cake / 236-Baking / 3020-Raspberry / \n",
"3-Concert / 38-Orchestra / 1431-Madison Square Garden / \n",
"1-Vehicle / 50-Aircraft / 129-Aviation / 139-Airport / 151-Landing / 157-Jet aircraft / 175-Airliner / 187-Takeoff / 188-Airline / 254-Runway / 471-Wing / 1316-Airbus A330 / \n",
"28-Fashion / 56-Hair / 106-Hairstyle / 583-Wig / \n",
"0-Games / 2-Video game / 229-Halo / 470-Halo 3 / \n",
"56-Hair / 106-Hairstyle / \n",
"6-Animation / 15-Cartoon / 1866-Katara / \n",
"0-Games / 2-Video game / 51-Strategy video game / 791-Magic: The Gathering / \n",
"6-Animation / 25-Toy / 140-LEGO / 477-Transformers / 990-Optimus Prime / \n",
"2-Video game / 23-Mobile phone / 29-Smartphone / 37-Gadget / 72-iPhone / 189-iPad / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 73-Ball / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"3674-Barcode / 3780-QR code / \n",
"3-Concert / 5-Dance / 21-Stadium / 342-Windows Media Video / \n",
"8-Football / \n",
"9-Music video / \n",
"0-Games / 1-Vehicle / 2-Video game / 74-Truck / 328-Simulation video game / 1099-Euro Truck Simulator 2 / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 101-Telephone / 2217-Moto G / \n",
"0-Games / 2-Video game / 33-Weapon / 43-Call of Duty / 422-Call of Duty: Ghosts / \n",
"75-Wrestling / 85-Combat / \n",
"0-Games / 2-Video game / 75-Wrestling / 127-Xbox 360 / 777-WWE 2K / 2629-WWE SmackDown vs. Raw 2010 / \n",
"3-Concert / 16-Performance art / \n",
"468-Biology / 1110-Cell / \n",
"3-Concert / 5-Dance / \n",
"3-Concert / 7-Musician / \n",
"5-Dance / 115-Ballet / 980-Solo dance / \n",
"28-Fashion / \n",
"3-Concert / 5-Dance / 16-Performance art / 95-Talent show / \n",
"227-Water / \n",
"0-Games / 2-Video game / 474-Destiny / \n",
"2-Video game / 20-Trailer / 34-Action-adventure game / 1144-Castlevania / \n",
"110-Album / \n",
"23-Mobile phone / 29-Smartphone / 101-Telephone / 932-Microsoft Lumia / 3376-Memory card / \n",
"2627-Little Red Riding Hood / \n",
"6-Animation / 123-Naruto / \n",
"1-Vehicle / 18-Outdoor recreation / 67-Cycling / 69-Bicycle / 220-Mountain bike / 284-Mountain biking / 520-Bicycle frame / \n",
"0-Games / 60-Basketball / 81-Athlete / 255-Slam dunk / \n",
"961-Sega Genesis / \n",
"5-Dance / 16-Performance art / 98-Festival / \n",
"0-Games / 2-Video game / 241-Fighting game / 764-The King of Fighters / 2714-Iori Yagami / 3160-Kyo Kusanagi / \n",
"0-Games / 2-Video game / 6-Animation / 241-Fighting game / 486-M.U.G.E.N / \n",
"345-Loudspeaker / \n",
"695-Balloon / \n",
"8-Football / \n",
"18-Outdoor recreation / 42-Fishing / 120-Winter / 262-Ice / 698-Fisherman / 896-Largemouth bass / 2836-Walleye / \n",
"1-Vehicle / 50-Aircraft / 923-Boeing 777 / \n",
"1-Vehicle / 4-Car / 349-Chevrolet / 406-Chevrolet / 509-Classic car / 2446-Used car / \n",
"6-Animation / \n",
"0-Games / 8-Football / 79-American football / \n",
"287-Human swimming / 329-Swimming pool / \n",
"0-Games / 2-Video game / 505-Mega Man / 2602-Mega Man X / \n",
"18-Outdoor recreation / 59-Winter sport / 177-Skiing / 305-Ski / 789-Alpine skiing / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"1-Vehicle / \n",
"9-Music video / \n",
"536-Glass / 640-Bottle / \n",
"1-Vehicle / 4-Car / 27-Motorcycle / 105-Motorcycling / 114-Engine / 336-Scooter / 1036-Moped / 1448-Vespa / \n",
"130-Gymnastics / 204-Gym / \n",
"60-Basketball / \n",
"223-Weather / \n",
"3-Concert / \n",
"546-Stitch / 607-Lace / 1057-Embroidery / 1804-Sewing needle / \n",
"0-Games / 2-Video game / 1419-GameSpot / \n",
"10-Animal / 48-Pet / 71-Dog / 182-Puppy / 1278-Labrador Retriever / \n",
"3-Concert / 7-Musician / \n",
"3-Concert / \n",
"3-Concert / \n",
"6-Animation / 15-Cartoon / 97-Drawing / \n",
"0-Games / 2-Video game / 159-World of Warcraft / 171-Warcraft / 849-Tales / \n",
"0-Games / 8-Football / \n",
"0-Games / 2-Video game / 3296-Transistor / \n",
"8-Football / 76-Ball / \n",
"23-Mobile phone / 29-Smartphone / 72-iPhone / 101-Telephone / 272-iPod / \n",
"1-Vehicle / 4-Car / 1324-Roadster / \n",
"56-Hair / 106-Hairstyle / 340-Afro-textured hair / \n",
"8-Football / \n",
"86-Plant / 136-Gardening / 184-Vegetable / 1209-Bean / \n",
"3-Concert / 16-Performance art / \n",
"3-Concert / \n",
"66-Bollywood / \n",
"1-Vehicle / 62-Train / 64-Transport / 103-Rail transport / 119-Locomotive / 143-Railroad car / 152-Track / \n",
"28-Fashion / 206-Shoe / 333-Nike; Inc. / 368-Sneakers / 1423-Nike Air Max / 1999-Eggplant / \n",
"6-Animation / 15-Cartoon / \n",
"6-Animation / \n",
"102-Building / 574-Elevator / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 24-String instrument / 36-Piano / 38-Orchestra / 150-Violin / 376-Flute / 450-Viola / \n",
"3-Concert / 13-Musical ensemble / 16-Performance art / \n",
"61-Art / 97-Drawing / 118-Grand Theft Auto V / 201-Grand Theft Auto: San Andreas / \n",
"410-Teacher / 776-Kindergarten / \n",
"59-Winter sport / 84-Snow / 177-Skiing / 281-Snowboarding / 305-Ski / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / \n",
"25-Toy / 89-Comics / 435-Action figure / 2121-Marvel Legends / 3109-Clint Barton / \n",
"337-Advertising / \n",
"6-Animation / 123-Naruto / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 14-Guitar / 38-Orchestra / 39-Drums / \n",
"1578-Red Dead Redemption / 1891-Red Dead / \n",
"12-Food / 232-Cake / 295-Chocolate / 1102-Cake decorating / 1189-Torte / 2941-Wedding cake / \n",
"6-Animation / 15-Cartoon / \n",
"260-Prayer / \n",
"5-Dance / 49-School / 1019-Christ / \n",
"1-Vehicle / 114-Engine / \n",
"33-Weapon / \n",
"6-Animation / 15-Cartoon / 1130-Armour / 2398-Personal armor / 3185-Bronze / \n",
"18-Outdoor recreation / 22-Nature / \n",
"45-Cosmetics / \n",
"14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"13-Musical ensemble / 21-Stadium / 79-American football / 203-Marching band / 209-University / \n",
"1-Vehicle / 4-Car / 2396-Coyote / \n",
"45-Cosmetics / 402-Jewellery / \n",
"83-Skateboarding / 108-Skateboard / 556-Skateboarding trick / \n",
"3-Concert / 5-Dance / 16-Performance art / \n",
"6-Animation / 15-Cartoon / 294-Batman / \n",
"1584-Kettlebell / \n",
"0-Games / 8-Football / 65-Kick / 73-Ball / 90-Sports game / 180-FIFA 15 / \n",
"125-Television / 223-Weather / \n",
"45-Cosmetics / 193-Eye liner / 195-Mascara / 196-Eye / \n",
"1058-Volcano / \n",
"6-Animation / 15-Cartoon / 61-Art / 97-Drawing / 379-Touhou Project / 408-Sketch / 2162-Marker pen / \n",
"3-Concert / \n",
"3-Concert / 5-Dance / 7-Musician / 13-Musical ensemble / 30-Drummer / 39-Drums / \n",
"3-Concert / 7-Musician / \n",
"1254-Cave / \n",
"3-Concert / 133-Nightclub / \n",
"131-Computer / 141-Microsoft Windows / \n",
"6-Animation / 15-Cartoon / 123-Naruto / 183-Manga / \n",
"10-Animal / 1330-Bull riding / \n",
"1-Vehicle / 11-Motorsport / 17-Racing / 27-Motorcycle / 172-Motocross / 253-Carnival / \n",
"18-Outdoor recreation / 59-Winter sport / 84-Snow / 120-Winter / 177-Skiing / 281-Snowboarding / 305-Ski / 789-Alpine skiing / \n",
"5-Dance / 31-Disc jockey / \n",
"5-Dance / 49-School / \n",
"36-Piano / 265-Electronic keyboard / \n",
"36-Piano / 53-Keyboard / 107-Musical keyboard / \n",
"9-Music video / \n",
"5-Dance / \n",
"6-Animation / 269-Comic book / 460-Yu-Gi-Oh! Trading Card Game / 494-Card game / \n",
"774-Galaxy / \n",
"3-Concert / 7-Musician / \n",
"0-Games / 2-Video game / 19-PC game / 192-Battlefield / 374-Battlefield 3 / 464-First-person Shooter / \n",
"1-Vehicle / 17-Racing / 50-Aircraft / \n",
"0-Games / 2-Video game / 6-Animation / 2237-Kantai Collection / \n",
"36-Piano / 46-Choir / 53-Keyboard / 104-Pianist / 107-Musical keyboard / \n",
"12-Food / \n",
"0-Games / 2-Video game / 35-Minecraft / \n",
"33-Weapon / 315-Knife / \n",
"22-Nature / 86-Plant / 136-Gardening / 279-Garden / 1023-Rose / \n",
"36-Piano / \n",
"0-Games / 60-Basketball / \n",
"9-Music video / \n",
"121-Photography / \n",
"5-Dance / 78-Wedding / 3693-Groomsman / \n",
"59-Winter sport / 84-Snow / 120-Winter / 177-Skiing / 281-Snowboarding / 789-Alpine skiing / 1830-Downhill / \n",
"3478-Meme / \n",
"0-Games / 2-Video game / 43-Call of Duty / 194-Call of Duty: Modern Warfare 2 / 2074-Killzone / \n",
"49-School / \n",
"9-Music video / \n",
"6-Animation / 61-Art / 981-Sculpture / 1420-Gear / \n",
"223-Weather / 419-Earth / 448-Planet / 593-Moon / 654-Sun / 2643-Asteroid / 4478-Sunspot / \n",
"8-Football / \n",
"0-Games / 2-Video game / 19-PC game / 34-Action-adventure game / 201-Grand Theft Auto: San Andreas / 858-Carl Johnson / \n",
"6-Animation / 4129-Crayon / \n",
"3-Concert / \n",
"2-Video game / 425-City / 2307-SimCity / 3290-SimCity / \n",
"6-Animation / 15-Cartoon / \n",
"242-Paper / \n",
"3-Concert / \n",
"110-Album / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 72-iPhone / 272-iPod / 331-iPod touch / 554-iPhone 4 / 1155-iPhone 3GS / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 27-Motorcycle / 57-Race track / \n",
"128-Cymbal / \n",
"1-Vehicle / 27-Motorcycle / 158-Tire / \n",
"10-Animal / \n",
"0-Games / 8-Football / \n",
"10-Animal / 48-Pet / 71-Dog / 182-Puppy / 1084-Bulldog / 1845-Pug / \n",
"6-Animation / 31-Disc jockey / \n",
"1-Vehicle / 82-Airplane / 92-Radio-controlled model / 149-Model aircraft / 160-Radio-controlled aircraft / 366-Dragon / 471-Wing / \n",
"1-Vehicle / 4-Car / 245-BMW / 344-Coupé / \n",
"1-Vehicle / 27-Motorcycle / 67-Cycling / 69-Bicycle / 114-Engine / 806-Chopper / \n",
"22-Nature / 86-Plant / \n",
"2-Video game / 33-Weapon / 96-Soldier / 736-ARMA / 1034-ARMA 2 / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 40-Road / 64-Transport / 74-Truck / \n",
"222-Surfing / 672-Bodyboarding / \n",
"22-Nature / 147-River / 283-Rock / \n",
"5-Dance / 16-Performance art / 95-Talent show / \n",
"0-Games / 2-Video game / 581-PlayStation / 3161-Tarzan / \n",
"7-Musician / 13-Musical ensemble / 44-Drums / 122-Weight training / 203-Marching band / 250-Parade / 820-Bagpipes / 1646-Pipe band / \n",
"30-Drummer / 39-Drums / 44-Drums / 128-Cymbal / 146-Snare drum / \n",
"121-Photography / 155-Camera / 1855-Video camera / \n",
"25-Toy / 89-Comics / 319-Robot / 435-Action figure / \n",
"886-James Bond / \n",
"1-Vehicle / 11-Motorsport / 17-Racing / 27-Motorcycle / 172-Motocross / \n",
"14-Guitar / 4101-Lap steel guitar / \n",
"0-Games / 2-Video game / 35-Minecraft / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 57-Race track / 135-Drifting / 1301-Nissan Skyline / \n",
"9-Music video / 96-Soldier / \n",
"0-Games / 23-Mobile phone / 29-Smartphone / 37-Gadget / \n",
"37-Gadget / 47-Personal computer / 228-Laptop / 513-Computer keyboard / \n",
"3-Concert / 7-Musician / 14-Guitar / \n",
"1-Vehicle / \n",
"130-Gymnastics / \n",
"10-Animal / 540-Insect / 775-Bee / 1296-Nest / 2867-Wasp / \n",
"110-Album / \n",
"3-Concert / 13-Musical ensemble / \n",
"0-Games / 1-Vehicle / 2-Video game / 4-Car / 34-Action-adventure game / 118-Grand Theft Auto V / 330-Money / \n",
"10-Animal / 48-Pet / 71-Dog / 182-Puppy / 3046-Maltese / \n",
"0-Games / 2-Video game / 241-Fighting game / 1961-Virtua Fighter 5 / 3077-Virtua Fighter / \n",
"5-Dance / 16-Performance art / \n",
"1-Vehicle / 2635-Water well / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 43-Call of Duty / 111-PlayStation 3 / 127-Xbox 360 / 134-Call of Duty: Black Ops / 194-Call of Duty: Modern Warfare 2 / \n",
"10-Animal / 80-Horse / \n",
"28-Fashion / 206-Shoe / 333-Nike; Inc. / 368-Sneakers / 4171-Air Force 1 / \n",
"6-Animation / 1206-Cinema 4D / \n",
"86-Plant / 136-Gardening / 156-Agriculture / 174-Farm / 279-Garden / 1720-Grape / 2473-Vineyard / 4644-Vine / \n",
"1-Vehicle / 4-Car / 17-Racing / 41-Sports car / 234-Ford / 352-Need for Speed / 1361-Ford GT / 1600-Need for Speed: World / \n",
"0-Games / 2-Video game / 35-Minecraft / 55-Video game console / 127-Xbox 360 / 181-Xbox / 810-Village / \n",
"148-Painting / 153-Dress / 303-Paint / 476-Textile / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 46-Choir / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 215-Eating / 274-Meat / 442-Bread / 2313-Bánh / \n",
"6-Animation / 15-Cartoon / 89-Comics / 123-Naruto / 183-Manga / \n",
"28-Fashion / 206-Shoe / 333-Nike; Inc. / 368-Sneakers / \n",
"3-Concert / \n",
"3-Concert / 39-Drums / \n",
"0-Games / 1-Vehicle / 2-Video game / 4-Car / 34-Action-adventure game / 40-Road / 118-Grand Theft Auto V / 127-Xbox 360 / 796-Fire engine / \n",
"36-Piano / 53-Keyboard / 107-Musical keyboard / 397-Organ / 1666-Hammond organ / \n",
"9-Music video / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 76-Ball / \n",
"9-Music video / \n",
"15-Cartoon / 123-Naruto / 285-Sasuke Uchiha / \n",
"0-Games / 2-Video game / 237-Final Fantasy / 1658-Dissidia Final Fantasy / \n",
"98-Festival / 163-Running / \n",
"0-Games / 8-Football / 76-Ball / \n",
"75-Wrestling / 85-Combat / \n",
"842-Harp / \n",
"1-Vehicle / 87-Boat / 498-Motorboat / \n",
"2297-Elf / \n",
"5-Dance / 16-Performance art / 130-Gymnastics / 350-Circus / 2358-Contortion / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 76-Ball / 81-Athlete / \n",
"0-Games / 745-Writing / 971-Alphabet / \n",
"0-Games / 199-Wii / 960-Badminton / \n",
"10-Animal / 1116-Goat / \n",
"0-Games / 5-Dance / 60-Basketball / \n",
"0-Games / 130-Gymnastics / 1030-Trampoline / 1335-Trampolining / \n",
"1-Vehicle / 4-Car / 1237-Baby carriage / 1932-Child safety seat / \n",
"465-Microphone / \n",
"88-Machine / 261-Tool / 400-Woodturning / 972-Lathe / 2256-Bearing / \n",
"20-Trailer / \n",
"12-Food / 86-Plant / 136-Gardening / 184-Vegetable / 279-Garden / 671-Tomato / 1022-Chili pepper / 1384-Greenhouse / \n",
"117-Boxing / \n",
"1-Vehicle / 4-Car / 74-Truck / 158-Tire / \n",
"3-Concert / 7-Musician / 9-Music video / 13-Musical ensemble / \n",
"6-Animation / 123-Naruto / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 92-Radio-controlled model / 167-Four-wheel drive / 202-Radio-controlled car / 926-Traxxas / \n",
"6-Animation / 15-Cartoon / 126-The Walt Disney Company / 1106-Character / 1266-Death Note / \n",
"6-Animation / 15-Cartoon / 359-Harry Potter (Literary Series) / 3605-Puppetry / \n",
"339-Architecture / 394-Map / 425-City / \n",
"7-Musician / 14-Guitar / 24-String instrument / 100-Electric guitar / \n",
"324-Finger / 444-Family / 1599-Bone / \n",
"282-Skin / 2108-Hair removal / \n",
"1-Vehicle / 6-Animation / 449-Honda / 610-Snowmobile / \n",
"18-Outdoor recreation / 59-Winter sport / 112-Ice skating / 316-Figure skating / 632-Ice dancing / \n",
"78-Wedding / \n",
"0-Games / 59-Winter sport / 112-Ice skating / 142-Hockey / \n",
"1-Vehicle / 50-Aircraft / 82-Airplane / 129-Aviation / 929-Aerobatics / \n",
"0-Games / 256-Tennis / 974-Racket / 1337-Serve / 1626-Forehand / \n",
"612-Beer / \n",
"9-Music video / 13-Musical ensemble / \n",
"49-School / \n",
"0-Games / 326-Metin2 / \n",
"3-Concert / \n",
"0-Games / 8-Football / 73-Ball / \n",
"3-Concert / \n",
"5-Dance / 2943-KFC / \n",
"402-Jewellery / 829-Bead / 859-Beadwork / 907-Necklace / 1001-Gemstone / 1300-Earrings / \n",
"1172-VHS / \n",
"1000-AdventureQuest Worlds / \n",
"1-Vehicle / 64-Transport / 156-Agriculture / 174-Farm / 660-Rural area / 931-Combine Harvester / \n",
"85-Combat / 693-Sword / 694-Tai chi / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 270-Sedan / 680-Subaru / 1041-Subaru Impreza / 1286-Subaru / 1897-Subaru Impreza WRX / \n",
"93-Comedy / 355-Comedian / \n",
"97-Drawing / 148-Painting / 717-Darth Vader / 4331-Darth Maul / \n",
"93-Comedy / \n",
"1-Vehicle / 4-Car / 114-Engine / 610-Snowmobile / \n",
"122-Weight training / 164-Beach / 214-Muscle / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 73-Ball / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 109-Cooking show / 184-Vegetable / 210-Cookware and bakeware / 301-Meal / \n",
"1-Vehicle / 2457-Organism / \n",
"8-Football / \n",
"42-Fishing / 423-Coast / 1008-Kite / 4380-Sport kite / \n",
"359-Harry Potter (Literary Series) / \n",
"131-Computer / 141-Microsoft Windows / \n",
"28-Fashion / 78-Wedding / 190-Bride / \n",
"0-Games / 8-Football / \n",
"1-Vehicle / 62-Train / 64-Transport / 152-Track / 3015-Monorail / \n",
"8-Football / 90-Sports game / 180-FIFA 15 / \n",
"3-Concert / \n",
"6-Animation / 15-Cartoon / 816-Gundam / \n",
"0-Games / 35-Minecraft / 488-Door / \n",
"12-Food / 86-Plant / 136-Gardening / 184-Vegetable / 215-Eating / 348-Fruit / 1060-Raw food / \n",
"96-Soldier / \n",
"3-Concert / 31-Disc jockey / \n",
"7-Musician / 46-Choir / 397-Organ / 870-Chapel / \n",
"1-Vehicle / 4-Car / 277-Sport utility vehicle / 515-Dodge / \n",
"1-Vehicle / 4-Car / 167-Four-wheel drive / 198-Off-road vehicle / 360-Jeep / 1205-Jeep Wrangler / \n",
"425-City / \n",
"110-Album / \n",
"18-Outdoor recreation / 67-Cycling / 69-Bicycle / 221-Skatepark / 377-BMX bike / \n",
"12-Food / 26-Cooking / 32-Recipe / 236-Baking / 485-Dough / 560-Sugar / 600-Flour / 1284-Frying / 1576-Doughnut / \n",
"0-Games / 2-Video game / 51-Strategy video game / 2182-Mario Party / \n",
"56-Hair / \n",
"10-Animal / 42-Fishing / 71-Dog / \n",
"1318-Flashlight / \n",
"56-Hair / 106-Hairstyle / 340-Afro-textured hair / \n",
"0-Games / 60-Basketball / \n",
"248-Hotel / 369-Resort / \n",
"6-Animation / 701-Beyblade / \n",
"1-Vehicle / 4-Car / 40-Road / 64-Transport / 74-Truck / 1020-Emergency vehicle / 1168-Neighbourhood / \n",
"0-Games / 434-Ninja / 2169-Ninja World / \n",
"12-Food / \n",
"18-Outdoor recreation / 163-Running / \n",
"246-Furniture / 580-Bedroom / 830-Bed / 1281-Couch / \n",
"31-Disc jockey / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 243-Sonic the Hedgehog / 251-Sonic the Hedgehog / 1512-Sonic Generations / \n",
"9-Music video / \n",
"4499-Fable II / \n",
"4584-Street Legal Racing: Redline / \n",
"116-Pokémon / 186-Pokémon / \n",
"0-Games / 60-Basketball / 168-Basketball moves / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 70-Driving / \n",
"56-Hair / \n",
"0-Games / 1000-AdventureQuest Worlds / \n",
"9-Music video / \n",
"2625-Gabrielle / \n",
"694-Tai chi / \n",
"222-Surfing / \n",
"14-Guitar / 1231-Mandolin / \n",
"1-Vehicle / 27-Motorcycle / 105-Motorcycling / \n",
"18-Outdoor recreation / 83-Skateboarding / 108-Skateboard / \n",
"3-Concert / 5-Dance / 13-Musical ensemble / \n",
"3-Concert / \n",
"22-Nature / 42-Fishing / 275-Diving / 288-Underwater / 381-Underwater diving / 407-Scuba diving / \n",
"9-Music video / \n",
"6-Animation / 15-Cartoon / 396-One Piece / \n",
"0-Games / 2-Video game / 90-Sports game / 180-FIFA 15 / \n",
"12-Food / 99-Christmas / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 101-Telephone / 178-Samsung Galaxy / \n",
"222-Surfing / \n",
"3-Concert / \n",
"36-Piano / 53-Keyboard / 107-Musical keyboard / \n",
"1-Vehicle / 22-Nature / 87-Boat / 92-Radio-controlled model / 225-Lake / \n",
"3-Concert / 16-Performance art / \n",
"56-Hair / 106-Hairstyle / 583-Wig / \n",
"102-Building / 113-House / 170-Home improvement / 276-Room / 339-Architecture / 416-Apartment / \n",
"8-Football / 21-Stadium / 77-Arena / \n",
"0-Games / 1576-Doughnut / 3277-The Simpsons: Tapped Out / \n",
"3-Concert / 16-Performance art / \n",
"0-Games / 2-Video game / 311-Street Fighter / 461-Street Fighter IV / 496-Super Street Fighter IV / 563-Ryu / 2468-M. Bison / 2526-Zangief / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 553-Sony Xperia / 4635-Sony Ericsson Xperia X10 Mini / \n",
"10-Animal / 48-Pet / 71-Dog / 182-Puppy / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 30-Drummer / 39-Drums / 1108-Mushroom / \n",
"78-Wedding / \n",
"1-Vehicle / 27-Motorcycle / 105-Motorcycling / 114-Engine / 154-Wheel / 158-Tire / 207-Exhaust system / 806-Chopper / 2927-Shovel / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / \n",
"163-Running / 206-Shoe / 368-Sneakers / \n",
"2-Video game / 380-Assassin's Creed / 1048-Assassin's Creed / \n",
"5-Dance / \n",
"66-Bollywood / \n",
"1-Vehicle / 4-Car / 517-Audi / 531-Hatchback / 945-MINI Cooper / 1221-MINI / 4496-Citroën DS3 / \n",
"1-Vehicle / 4-Car / \n",
"0-Games / 2-Video game / 111-PlayStation 3 / 216-Call of Duty: Modern Warfare 3 / \n",
"10-Animal / 18-Outdoor recreation / 80-Horse / 247-Stallion / 289-Dressage / \n",
"3-Concert / \n",
"10-Animal / 12-Food / 132-Bird / \n",
"1-Vehicle / 2-Video game / 4-Car / 11-Motorsport / 17-Racing / 27-Motorcycle / 2712-Just Cause / \n",
"0-Games / 372-Playing card / 791-Magic: The Gathering / \n",
"7-Musician / 13-Musical ensemble / 46-Choir / \n",
"6-Animation / 15-Cartoon / 123-Naruto / 183-Manga / 285-Sasuke Uchiha / 622-Sakura Haruno / \n",
"93-Comedy / 205-Newscaster / \n",
"7-Musician / 14-Guitar / 36-Piano / 44-Drums / \n",
"10-Animal / 86-Plant / 91-Fish / 258-Aquarium / 2029-Filter / \n",
"83-Skateboarding / 108-Skateboard / 221-Skatepark / 336-Scooter / 595-Champion / \n",
"3-Concert / \n",
"1964-TV4 / \n",
"0-Games / 8-Football / \n",
"20-Trailer / \n",
"0-Games / 1-Vehicle / 74-Truck / 124-Tractor / 174-Farm / 715-Farming Simulator / \n",
"467-Chipmunk / \n",
"8-Football / 79-American football / \n",
"15-Cartoon / 183-Manga / 396-One Piece / \n",
"0-Games / 199-Wii / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 30-Drummer / 39-Drums / \n",
"0-Games / 2-Video game / 445-Guitar Hero / 724-Rock Band / \n",
"1-Vehicle / 4-Car / 299-Volkswagen Passenger Cars / 827-Pump / \n",
"7-Musician / 9-Music video / 14-Guitar / \n",
"776-Kindergarten / \n",
"0-Games / 8-Football / 54-Highlight film / \n",
"0-Games / 489-Board game / 2833-Go / 4510-Shogi / \n",
"0-Games / 2-Video game / 55-Video game console / 320-Super Smash Bros. / 692-Link / \n",
"783-Zee TV / \n",
"3-Concert / 5-Dance / 16-Performance art / \n",
"14-Guitar / 842-Harp / 3172-Lyre / \n",
"259-Fire / \n",
"7-Musician / 14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"2324-Hang / \n",
"9-Music video / \n",
"1-Vehicle / 62-Train / 64-Transport / 84-Snow / 103-Rail transport / 120-Winter / \n",
"23-Mobile phone / 29-Smartphone / 72-iPhone / 189-iPad / 272-iPod / 331-iPod touch / \n",
"61-Art / 83-Skateboarding / 108-Skateboard / \n",
"0-Games / 2-Video game / 264-Star Wars / \n",
"1-Vehicle / 4-Car / 154-Wheel / 158-Tire / 493-Rim / \n",
"3-Concert / \n",
"3-Concert / 7-Musician / 389-Restaurant / \n",
"0-Games / 2-Video game / 6-Animation / 506-Garry's Mod / \n",
"6-Animation / 15-Cartoon / 89-Comics / \n",
"0-Games / 463-Cricket / 1495-Twenty20 / \n",
"1-Vehicle / 4-Car / 25-Toy / 84-Snow / 92-Radio-controlled model / 202-Radio-controlled car / 449-Honda / 3086-Honda CR-V / \n",
"6-Animation / 4527-South Park: The Stick of Truth / \n",
"12-Food / 26-Cooking / 32-Recipe / 58-Cuisine / \n",
"1-Vehicle / 92-Radio-controlled model / 113-House / 149-Model aircraft / 155-Camera / 160-Radio-controlled aircraft / 393-Unmanned aerial vehicle / 604-Quadcopter / \n",
"6-Animation / 15-Cartoon / 185-Dragon Ball / 244-Goku / 418-Gohan / \n",
"1-Vehicle / 62-Train / 119-Locomotive / 143-Railroad car / 428-Rail transport modelling / \n",
"6-Animation / 15-Cartoon / 89-Comics / 3595-Shaman King / \n",
"12-Food / 86-Plant / 865-Turtle / 1500-Cherry blossom / \n",
"6-Animation / 213-Forest / 1555-Sword Art Online / \n",
"0-Games / 2-Video game / 192-Battlefield / 374-Battlefield 3 / 383-Battlefield 4 / \n",
"1-Vehicle / 4-Car / 87-Boat / 498-Motorboat / 2338-Inflatable boat / \n",
"3-Concert / 30-Drummer / \n",
"0-Games / 2-Video game / 19-PC game / \n",
"3162-Postage stamp / \n",
"1-Vehicle / 4-Car / 114-Engine / 509-Classic car / 1301-Nissan Skyline / \n",
"1-Vehicle / 62-Train / 64-Transport / 161-Train station / \n",
"23-Mobile phone / 101-Telephone / \n",
"47-Personal computer / 131-Computer / 141-Microsoft Windows / \n",
"66-Bollywood / \n",
"14-Guitar / \n",
"3-Concert / 7-Musician / 98-Festival / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 274-Meat / 542-Potato / 968-Pie / 1108-Mushroom / 2650-Frying pan / \n",
"405-Braid / 1249-Rainbow Loom / \n",
"0-Games / 49-School / 81-Athlete / 562-Pitcher / \n",
"22-Nature / 164-Beach / \n",
"2-Video game / 127-Xbox 360 / \n",
"20-Trailer / 42-Fishing / 222-Surfing / 917-Sail / \n",
"10-Animal / 61-Art / 97-Drawing / 148-Painting / 936-Watercolor paint / 1063-Ink / 2630-Doodle / \n",
"0-Games / 2-Video game / 19-PC game / 20-Trailer / 359-Harry Potter (Literary Series) / 4489-Harry Potter and the Philosopher's Stone / \n",
"3-Concert / \n",
"5-Dance / \n",
"28-Fashion / 206-Shoe / 4151-Flip-flops / \n",
"72-iPhone / 83-Skateboarding / 108-Skateboard / 221-Skatepark / \n",
"6-Animation / 15-Cartoon / 61-Art / 97-Drawing / 183-Manga / 408-Sketch / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"497-Macintosh / 3974-Mac Mini / \n",
"5-Dance / \n",
"1-Vehicle / 4-Car / 20-Trailer / \n",
"0-Games / 2-Video game / 55-Video game console / 317-PlayStation / \n",
"1-Vehicle / 87-Boat / 308-Ship / 579-Yacht / 1957-Luxury yacht / \n",
"1-Vehicle / 4-Car / 11-Motorsport / \n",
"0-Games / 8-Football / 21-Stadium / \n",
"12-Food / 52-Dish / 58-Cuisine / 184-Vegetable / 301-Meal / 391-Chicken meat / 1569-Steamed rice / 3049-Convenience store / \n",
"121-Photography / \n",
"31-Disc jockey / \n",
"122-Weight training / 214-Muscle / \n",
"0-Games / 2-Video game / 55-Video game console / 111-PlayStation 3 / 127-Xbox 360 / 181-Xbox / 317-PlayStation / 386-Xbox / 1482-PlayStation Network / \n",
"130-Gymnastics / \n",
"0-Games / 2-Video game / 55-Video game console / 199-Wii / 774-Galaxy / 1397-Super Mario Galaxy / 1759-Super Mario Galaxy 2 / \n",
"49-School / \n",
"1-Vehicle / 4-Car / 114-Engine / 3399-Timing belt / 3939-Nissan Maxima / \n",
"9-Music video / \n",
"339-Architecture / \n",
"925-Brain / \n",
"61-Art / 97-Drawing / 408-Sketch / 479-Graffiti / 538-Illustration / \n",
"0-Games / 2-Video game / 20-Trailer / 34-Action-adventure game / 111-PlayStation 3 / 176-PlayStation 4 / 656-Metal Gear / 2212-Metal Gear Solid V: The Phantom Pain / \n",
"14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"0-Games / 2-Video game / 33-Weapon / 43-Call of Duty / 302-Call of Duty 4: Modern Warfare / \n",
"8-Football / 358-Rugby football / \n",
"122-Weight training / 529-Squat / 532-Barbell / \n",
"7-Musician / 13-Musical ensemble / 46-Choir / \n",
"7-Musician / 14-Guitar / 24-String instrument / 100-Electric guitar / \n",
"9-Music video / 14-Guitar / \n",
"66-Bollywood / \n",
"137-Tree / 1198-Birth / \n",
"1-Vehicle / 9-Music video / 62-Train / 428-Rail transport modelling / \n",
"2642-CNBC / \n",
"5-Dance / \n",
"3-Concert / 5-Dance / \n",
"9-Music video / 99-Christmas / 558-Santa Claus / 964-Christmas tree / \n",
"3-Concert / 16-Performance art / \n",
"3-Concert / \n",
"7-Musician / 14-Guitar / 15-Cartoon / 24-String instrument / 63-Acoustic guitar / \n",
"5-Dance / 31-Disc jockey / 133-Nightclub / \n",
"263-Light / 1375-Laser lighting display / \n",
"20-Trailer / 264-Star Wars / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / 46-Choir / 376-Flute / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 101-Telephone / \n",
"9-Music video / \n",
"0-Games / 2-Video game / 19-PC game / 34-Action-adventure game / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 43-Call of Duty / 96-Soldier / 422-Call of Duty: Ghosts / 4485-Warhawk / \n",
"6-Animation / 15-Cartoon / \n",
"7-Musician / 219-Accordion / \n",
"3-Concert / 68-Lighting / 263-Light / \n",
"35-Minecraft / \n",
"59-Winter sport / 81-Athlete / 112-Ice skating / 142-Hockey / 312-Ice rink / \n",
"49-School / \n",
"0-Games / 2-Video game / 6-Animation / 1878-SD Gundam Capsule Fighter / \n",
"0-Games / 2-Video game / 6-Animation / 15-Cartoon / 1668-Claw crane / \n",
"3-Concert / 7-Musician / \n",
"2-Video game / 429-Website / \n",
"0-Games / 326-Metin2 / \n",
"20-Trailer / 3226-Chucky / \n",
"45-Cosmetics / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 40-Road / 41-Sports car / 57-Race track / 70-Driving / 212-Highway / 231-Supercar / 711-Gran Turismo / 953-Gran Turismo 5 / 1521-Jaguar Cars / \n",
"37-Gadget / 47-Personal computer / 145-Tablet computer / 189-iPad / 1464-iPad Mini / \n",
"0-Games / 8-Football / 1247-Penalty kick / \n",
"0-Games / 83-Skateboarding / 221-Skatepark / 336-Scooter / \n",
"0-Games / 2-Video game / 47-Personal computer / 1028-Devil May Cry / 2344-DmC: Devil May Cry / \n",
"18-Outdoor recreation / 33-Weapon / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 16-Performance art / 30-Drummer / \n",
"117-Boxing / \n",
"3-Concert / 13-Musical ensemble / 30-Drummer / 133-Nightclub / \n",
"0-Games / 2-Video game / 19-PC game / 51-Strategy video game / 382-Dota 2 / 549-Defense of the Ancients / \n",
"0-Games / 59-Winter sport / 77-Arena / 112-Ice skating / 142-Hockey / 312-Ice rink / \n",
"0-Games / 530-Cue sports / 568-Pool / 994-Cue stick / \n",
"9-Music video / 3124-Kendama / \n",
"1-Vehicle / 11-Motorsport / 27-Motorcycle / 84-Snow / 105-Motorcycling / 120-Winter / 262-Ice / 2070-Suzuki Hayabusa / \n",
"49-School / 410-Teacher / \n",
"13-Musical ensemble / \n",
"5-Dance / 49-School / \n",
"702-Counter-Strike: Source / \n",
"9-Music video / 1221-MINI / \n",
"0-Games / 2-Video game / 47-Personal computer / 118-Grand Theft Auto V / 464-First-person Shooter / \n",
"10-Animal / 80-Horse / \n",
"10-Animal / 208-Wood / 540-Insect / 775-Bee / 1296-Nest / \n",
"0-Games / 49-School / 237-Final Fantasy / \n",
"3-Concert / 68-Lighting / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / 46-Choir / \n",
"0-Games / 710-Handball / \n",
"110-Album / \n",
"0-Games / 2-Video game / 60-Basketball / 77-Arena / 90-Sports game / 168-Basketball moves / 255-Slam dunk / 1262-NBA 2K14 / 2156-Rookie / \n",
"10-Animal / 48-Pet / 166-Cat / 469-Bag / 1142-Laundry / \n",
"10-Animal / 48-Pet / 166-Cat / 346-Kitten / \n",
"66-Bollywood / \n",
"1-Vehicle / 74-Truck / 114-Engine / \n",
"3-Concert / 5-Dance / 95-Talent show / 191-Cheerleading / \n",
"5-Dance / 16-Performance art / 133-Nightclub / \n",
"267-Mountain / \n",
"5-Dance / \n",
"616-The Bible / \n",
"1-Vehicle / 4-Car / 261-Tool / 4066-Wrench / \n",
"0-Games / 2-Video game / 116-Pokémon / 186-Pokémon / 1204-Pokémon Ruby and Sapphire / 1665-Pokémon Emerald / 2236-Emerald / \n",
"512-Printing / 696-T-shirt / 1757-Screen printing / \n",
"1-Vehicle / 4-Car / 40-Road / \n",
"36-Piano / 53-Keyboard / 104-Pianist / 107-Musical keyboard / \n",
"14-Guitar / \n",
"1-Vehicle / 18-Outdoor recreation / 22-Nature / 67-Cycling / 69-Bicycle / 197-Trail / 213-Forest / 220-Mountain bike / 278-GoPro / 284-Mountain biking / \n",
"2-Video game / 47-Personal computer / 1045-Computer case / 4515-Antec / \n",
"12-Food / 26-Cooking / 32-Recipe / 226-Dessert / 232-Cake / 295-Chocolate / 1176-Pumpkin / 1707-Microwave oven / 2768-Mug / \n",
"6-Animation / 15-Cartoon / 183-Manga / 185-Dragon Ball / 244-Goku / \n",
"810-Village / \n",
"78-Wedding / \n",
"0-Games / 2-Video game / 11-Motorsport / 2815-F-Zero / \n",
"0-Games / 2-Video game / 37-Gadget / 55-Video game console / 90-Sports game / 292-Handheld game console / 317-PlayStation / 480-FIFA 13 / 682-PlayStation Vita / \n",
"1-Vehicle / 11-Motorsport / 17-Racing / 18-Outdoor recreation / 27-Motorcycle / 172-Motocross / \n",
"0-Games / 67-Cycling / 69-Bicycle / 83-Skateboarding / 222-Surfing / 669-Extreme sport / 2014-Vans / \n",
"25-Toy / 89-Comics / 185-Dragon Ball / 244-Goku / 435-Action figure / 1986-Piccolo / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 40-Road / 41-Sports car / 57-Race track / 349-Chevrolet / 378-Drag racing / \n",
"0-Games / 1-Vehicle / 2-Video game / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 57-Race track / 70-Driving / \n",
"1-Vehicle / 62-Train / 64-Transport / 103-Rail transport / 119-Locomotive / 143-Railroad car / 161-Train station / 224-Rapid transit / \n",
"0-Games / 2-Video game / 19-PC game / 1566-Doom / \n",
"3-Concert / 5-Dance / 133-Nightclub / 613-Bar / \n",
"1-Vehicle / 4-Car / 40-Road / 120-Winter / 2016-Volvo Cars / \n",
"0-Games / 2851-Fight Night / \n",
"31-Disc jockey / 133-Nightclub / \n",
"18-Outdoor recreation / 217-Hunting / 4035-Pheasant / \n",
"1-Vehicle / 4-Car / 9-Music video / 70-Driving / \n",
"5-Dance / \n",
"10-Animal / 25-Toy / 1152-Plush / 1212-Stuffed toy / 1497-Wool / \n",
"1-Vehicle / 62-Train / 110-Album / \n",
"0-Games / 5-Dance / 49-School / 191-Cheerleading / \n",
"0-Games / 2-Video game / 6-Animation / 15-Cartoon / 877-Hatsune Miku: Project DIVA / 1648-Hatsune Miku: Project DIVA F / 2682-Hatsune Miku: Project DIVA F 2nd / \n",
"9-Music video / 14-Guitar / 15-Cartoon / 24-String instrument / \n",
"66-Bollywood / \n",
"766-President of the United States / \n",
"0-Games / 2-Video game / 2431-Metal Slug / \n",
"14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"6-Animation / 15-Cartoon / 243-Sonic the Hedgehog / 251-Sonic the Hedgehog / \n",
"1-Vehicle / 3-Concert / 11-Motorsport / 27-Motorcycle / 67-Cycling / 69-Bicycle / 350-Circus / \n",
"12-Food / 26-Cooking / 32-Recipe / 179-Kitchen / 184-Vegetable / 1439-Refrigerator / 2614-Lettuce / \n",
"3-Concert / 5-Dance / 16-Performance art / \n",
"30-Drummer / 39-Drums / 44-Drums / 128-Cymbal / 146-Snare drum / \n",
"3-Concert / \n",
"93-Comedy / \n",
"5-Dance / 16-Performance art / \n",
"5-Dance / \n",
"1-Vehicle / 11-Motorsport / \n",
"12-Food / 391-Chicken meat / 944-Hamburger / 2943-KFC / \n",
"75-Wrestling / \n",
"5-Dance / \n",
"0-Games / 8-Football / 21-Stadium / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 1028-Devil May Cry / 2336-Devil May Cry 3: Dante's Awakening / \n",
"0-Games / 2-Video game / 19-PC game / 34-Action-adventure game / 43-Call of Duty / 118-Grand Theft Auto V / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / \n",
"1-Vehicle / 11-Motorsport / 135-Drifting / 1887-Tricycle / \n",
"1-Vehicle / 84-Snow / 120-Winter / 223-Weather / 1145-Winter storm / \n",
"318-Church / \n",
"121-Photography / 155-Camera / 566-Digital camera / 629-Camera lens / 721-Digital SLR / 3328-Canon EOS 7D / \n",
"10-Animal / 12-Food / 274-Meat / 763-Snake / \n",
"3-Concert / 5-Dance / \n",
"3-Concert / 7-Musician / 14-Guitar / 30-Drummer / 39-Drums / 44-Drums / \n",
"110-Album / \n",
"0-Games / 1-Vehicle / 2-Video game / 4-Car / 34-Action-adventure game / 297-Grand Theft Auto IV / 658-Grand Theft Auto: The Lost and Damned / \n",
"626-Puzzle / \n",
"0-Games / 2-Video game / 23-Mobile phone / 29-Smartphone / 37-Gadget / \n",
"0-Games / 1-Vehicle / 2-Video game / 124-Tractor / 127-Xbox 360 / 174-Farm / 314-Xbox One / 715-Farming Simulator / \n",
"79-American football / \n",
"1163-Carpet / 2547-Staples Center / \n",
"2-Video game / 55-Video game console / 127-Xbox 360 / 181-Xbox / \n",
"0-Games / 2-Video game / 43-Call of Duty / 111-PlayStation 3 / 127-Xbox 360 / 194-Call of Duty: Modern Warfare 2 / 302-Call of Duty 4: Modern Warfare / \n",
"121-Photography / \n",
"9-Music video / 549-Defense of the Ancients / \n",
"12-Food / 26-Cooking / 32-Recipe / 58-Cuisine / 274-Meat / 620-Barbecue / 688-Beef / 1115-Steak / \n",
"36-Piano / 110-Album / \n",
"0-Games / 2-Video game / 37-Gadget / 55-Video game console / 292-Handheld game console / 317-PlayStation / 362-PlayStation Portable / 682-PlayStation Vita / 2260-Monster Hunter Portable 3rd / \n",
"0-Games / 942-Silkroad Online / \n",
"9-Music video / \n",
"0-Games / 2-Video game / 19-PC game / 1861-Smite / 4525-Hi-Rez Studios / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 184-Vegetable / 236-Baking / 475-Cheese / 485-Dough / 662-Pizza / 671-Tomato / 752-Pasta / 1230-Italian food / 1381-Garlic / 3364-Basil / \n",
"0-Games / 1-Vehicle / 2-Video game / 4-Car / 19-PC game / 34-Action-adventure game / 118-Grand Theft Auto V / \n",
"10-Animal / 42-Fishing / 91-Fish / 147-River / 225-Lake / 268-Recreational fishing / 896-Largemouth bass / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"1-Vehicle / 27-Motorcycle / 206-Shoe / 757-Boot / \n",
"12-Food / 26-Cooking / 184-Vegetable / 348-Fruit / \n",
"110-Album / 311-Street Fighter / 461-Street Fighter IV / 2239-Street Fighter II: The World Warrior / 2468-M. Bison / 4579-Street Fighter Alpha 3 / \n",
"364-Sitcom / \n",
"1-Vehicle / 11-Motorsport / 17-Racing / 27-Motorcycle / 172-Motocross / \n",
"3-Concert / \n",
"223-Weather / 259-Fire / 365-Fireworks / \n",
"20-Trailer / 1710-Pirates of the Caribbean / \n",
"257-Counter-Strike / \n",
"273-News program / \n",
"7-Musician / 14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"23-Mobile phone / 29-Smartphone / 452-Watch / 627-Clock / 1260-Smartwatch / 3727-Android Wear / \n",
"3-Concert / 5-Dance / 31-Disc jockey / 68-Lighting / 133-Nightclub / \n",
"98-Festival / \n",
"15-Cartoon / 1696-Raven / \n",
"6-Animation / \n",
"0-Games / 779-Point Blank / \n",
"1-Vehicle / 11-Motorsport / 17-Racing / 27-Motorcycle / 172-Motocross / \n",
"1621-Ork / \n",
"14-Guitar / \n",
"31-Disc jockey / \n",
"9-Music video / 28-Fashion / \n",
"10-Animal / 18-Outdoor recreation / 22-Nature / 42-Fishing / 91-Fish / 147-River / 225-Lake / 268-Recreational fishing / 478-Fishing rod / \n",
"3-Concert / 7-Musician / 14-Guitar / 39-Drums / \n",
"6-Animation / 20-Trailer / \n",
"23-Mobile phone / 29-Smartphone / 72-iPhone / \n",
"938-Steel / \n",
"3-Concert / 5-Dance / 16-Performance art / \n",
"5-Dance / \n",
"1-Vehicle / 4-Car / 74-Truck / 137-Tree / 1093-Loader / 1611-Garbage truck / \n",
"8-Football / \n",
"0-Games / 8-Football / 21-Stadium / 163-Running / \n",
"22-Nature / 1531-Sunrise / \n",
"25-Toy / \n",
"9-Music video / \n",
"0-Games / 2-Video game / 199-Wii / \n",
"372-Playing card / 746-Card manipulation / \n",
"3-Concert / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 101-Telephone / \n",
"0-Games / 2-Video game / 1007-Portal / 1274-Portal 2 / \n",
"205-Newscaster / 223-Weather / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 57-Race track / 135-Drifting / \n",
"45-Cosmetics / 56-Hair / 193-Eye liner / 195-Mascara / 196-Eye / 218-Eyelash / 282-Skin / 291-Rouge / 385-Face / 436-Concealer / \n",
"1-Vehicle / 88-Machine / 249-Heavy equipment / 817-Bulldozer / 822-Crane / \n",
"93-Comedy / 355-Comedian / \n",
"3-Concert / 5-Dance / 16-Performance art / \n",
"12-Food / \n",
"83-Skateboarding / 108-Skateboard / 351-Jumping / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 362-PlayStation Portable / 1125-God of War / 1432-Kratos / \n",
"9-Music video / \n",
"3-Concert / \n",
"2-Video game / 319-Robot / \n",
"1975-Amiga / \n",
"1899-E-book / \n",
"3442-Résumé / \n",
"1-Vehicle / 25-Toy / 62-Train / 1279-Plarail / 1685-Toy train / \n",
"3-Concert / 5-Dance / 16-Performance art / 95-Talent show / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 14-Guitar / 24-String instrument / 30-Drummer / 44-Drums / \n",
"0-Games / 2-Video game / 79-American football / 90-Sports game / 561-Madden NFL / \n",
"1-Vehicle / 12-Food / 666-Concrete / \n",
"18-Outdoor recreation / 59-Winter sport / 112-Ice skating / 703-Roller skating / 1937-Inline skates / 2747-Inline skating / \n",
"30-Drummer / 39-Drums / 44-Drums / 128-Cymbal / 146-Snare drum / 2073-Sabian / \n",
"3-Concert / 16-Performance art / 68-Lighting / \n",
"12-Food / \n",
"20-Trailer / \n",
"0-Games / \n",
"5-Dance / \n",
"23-Mobile phone / 37-Gadget / 47-Personal computer / 145-Tablet computer / 189-iPad / 1459-Magazine / \n",
"7-Musician / 13-Musical ensemble / 38-Orchestra / 250-Parade / 371-Firefighter / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 72-iPhone / 272-iPod / \n",
"5-Dance / 31-Disc jockey / \n",
"14-Guitar / 36-Piano / 53-Keyboard / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 72-iPhone / 101-Telephone / 155-Camera / 178-Samsung Galaxy / 899-Samsung Galaxy S III / 3648-Samsung Galaxy S III Mini / \n",
"0-Games / 2-Video game / 116-Pokémon / 186-Pokémon / 1204-Pokémon Ruby and Sapphire / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 650-Convertible / 1455-Cadillac / \n",
"9-Music video / \n",
"5-Dance / 16-Performance art / \n",
"60-Basketball / \n",
"36-Piano / 53-Keyboard / 104-Pianist / 107-Musical keyboard / \n",
"47-Personal computer / 145-Tablet computer / 228-Laptop / 2581-Microsoft Surface / \n",
"468-Biology / 925-Brain / \n",
"12-Food / 26-Cooking / 662-Pizza / \n",
"1-Vehicle / 67-Cycling / 69-Bicycle / 520-Bicycle frame / 645-Road bicycle / \n",
"1-Vehicle / 266-Helicopter / 3602-Mini-Z / \n",
"3-Concert / 7-Musician / \n",
"0-Games / 2-Video game / 1178-Quest / 1196-Dragon Age / 1735-Dragon Age: Inquisition / 2700-Dragon Age II / \n",
"98-Festival / 253-Carnival / \n",
"9-Music video / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 201-Grand Theft Auto: San Andreas / 1069-Easter egg / \n",
"3-Concert / 68-Lighting / \n",
"0-Games / 2-Video game / 831-Plants vs. Zombies / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 25-Toy / 40-Road / 57-Race track / 92-Radio-controlled model / 202-Radio-controlled car / 270-Sedan / \n",
"4463-Santana Lopez / \n",
"0-Games / 88-Machine / 535-Slot machine / \n",
"7-Musician / 14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"0-Games / 756-Tibia / \n",
"23-Mobile phone / 29-Smartphone / 47-Personal computer / 72-iPhone / 145-Tablet computer / 189-iPad / 272-iPod / 921-Grand Theft Auto: Vice City / \n",
"28-Fashion / 45-Cosmetics / 56-Hair / 583-Wig / 607-Lace / \n",
"0-Games / 8-Football / 54-Highlight film / 79-American football / 813-Running back / \n",
"6-Animation / 15-Cartoon / 123-Naruto / 801-Inuyasha / \n",
"0-Games / 535-Slot machine / 3153-Freddy Krueger / \n",
"1535-Prize / \n",
"61-Art / 110-Album / \n",
"28-Fashion / 45-Cosmetics / 56-Hair / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 57-Race track / \n",
"12-Food / 26-Cooking / 52-Dish / 58-Cuisine / 215-Eating / 1218-Japanese Cuisine / \n",
"0-Games / 2-Video game / 541-Black-and-white / 1446-Game Boy / \n",
"222-Surfing / 815-Surfboard / \n",
"1-Vehicle / 12-Food / 124-Tractor / 156-Agriculture / 174-Farm / 249-Heavy equipment / 617-Maize / 1473-Forage harvester / \n",
"13-Musical ensemble / \n",
"3-Concert / 7-Musician / 30-Drummer / 39-Drums / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 41-Sports car / 344-Coupé / 618-Fiat Automobiles / \n",
"0-Games / 159-World of Warcraft / 171-Warcraft / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 14-Guitar / 49-School / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / \n",
"0-Games / 2-Video game / 23-Mobile phone / 29-Smartphone / 809-Logo / \n",
"155-Camera / \n",
"2-Video game / 34-Action-adventure game / 1396-Lara Croft / \n",
"30-Drummer / 39-Drums / 44-Drums / 128-Cymbal / 146-Snare drum / \n",
"0-Games / 2-Video game / 35-Minecraft / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 41-Sports car / 344-Coupé / 2052-Infiniti / \n",
"61-Art / \n",
"1-Vehicle / 4-Car / 154-Wheel / 158-Tire / 493-Rim / \n",
"0-Games / 2-Video game / 1631-PlanetSide 2 / 2026-PlanetSide / \n",
"85-Combat / 117-Boxing / 699-Kick / \n",
"8-Football / 595-Champion / \n",
"5-Dance / \n",
"5-Dance / 209-University / \n",
"14-Guitar / 24-String instrument / \n",
"7-Musician / 13-Musical ensemble / 46-Choir / \n",
"0-Games / 2-Video game / 851-Digimon / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / \n",
"66-Bollywood / \n",
"0-Games / 211-RuneScape / 635-Achievement / \n",
"59-Winter sport / 112-Ice skating / 262-Ice / 316-Figure skating / 833-Ice skate / \n",
"3-Concert / 7-Musician / \n",
"0-Games / 2-Video game / 35-Minecraft / 365-Fireworks / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 57-Race track / 378-Drag racing / \n",
"98-Festival / \n",
"322-Mixtape / \n",
"8-Football / \n",
"12-Food / 26-Cooking / 32-Recipe / 274-Meat / \n",
"22-Nature / 2493-The Hobbit / \n",
"5-Dance / 115-Ballet / 776-Kindergarten / \n",
"25-Toy / 140-LEGO / 264-Star Wars / 852-Lego Star Wars / \n",
"5-Dance / 4336-Angkor Wat / \n",
"0-Games / 2-Video game / 19-PC game / 43-Call of Duty / 315-Knife / 422-Call of Duty: Ghosts / 4485-Warhawk / \n",
"85-Combat / 1832-Hand-to-hand combat / \n",
"3-Concert / 7-Musician / \n",
"6-Animation / 15-Cartoon / \n",
"9-Music video / 125-Television / \n",
"410-Teacher / 745-Writing / \n",
"67-Cycling / 69-Bicycle / 220-Mountain bike / 284-Mountain biking / 351-Jumping / \n",
"12-Food / 367-Drink / 612-Beer / 3126-Ale / \n",
"3-Concert / 7-Musician / 30-Drummer / \n",
"3-Concert / 7-Musician / \n",
"28-Fashion / 45-Cosmetics / \n",
"0-Games / 2-Video game / 20-Trailer / 413-PlayStation 2 / 4226-Neo / \n",
"6-Animation / 15-Cartoon / 1732-Oni / \n",
"0-Games / 33-Weapon / 3786-Killing Floor / \n",
"557-Salad / \n",
"1-Vehicle / 18-Outdoor recreation / 22-Nature / 67-Cycling / 69-Bicycle / 197-Trail / 220-Mountain bike / 284-Mountain biking / 3665-Giant Bicycles / \n",
"9-Music video / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"3-Concert / 5-Dance / \n",
"2-Video game / 25-Toy / 229-Halo / \n",
"0-Games / 8-Football / \n",
"325-Vampire / 725-Human / \n",
"0-Games / 162-Medicine / 468-Biology / \n",
"3-Concert / \n",
"1-Vehicle / 50-Aircraft / 82-Airplane / 139-Airport / \n",
"28-Fashion / 56-Hair / 106-Hairstyle / 405-Braid / \n",
"398-The Legend of Zelda / \n",
"7-Musician / 36-Piano / 150-Violin / 376-Flute / \n",
"2183-Stardoll / \n",
"12-Food / 26-Cooking / 32-Recipe / 179-Kitchen / 367-Drink / 985-Strawberry / 1174-Smoothie / \n",
"125-Television / \n",
"20-Trailer / 1177-Spider / \n",
"925-Brain / \n",
"9-Music video / \n",
"1-Vehicle / 575-Traffic / \n",
"6-Animation / 15-Cartoon / 342-Windows Media Video / \n",
"3-Concert / 7-Musician / 14-Guitar / \n",
"14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"0-Games / 2-Video game / 257-Counter-Strike / 1109-Counter-Strike Online / \n",
"0-Games / 51-Strategy video game / 373-Clash of Clans / 3067-Boom Beach / \n",
"736-ARMA / 1034-ARMA 2 / \n",
"66-Bollywood / \n",
"7-Musician / 13-Musical ensemble / 38-Orchestra / \n",
"22-Nature / 559-Night / 739-Guild Wars / 839-Guild Wars 2 / \n",
"282-Skin / 385-Face / \n",
"28-Fashion / 45-Cosmetics / 56-Hair / 106-Hairstyle / \n",
"3242-Seeley Booth / \n",
"8-Football / 12-Food / 58-Cuisine / 944-Hamburger / \n",
"10-Animal / 22-Nature / 86-Plant / 136-Gardening / 540-Insect / 642-Leaf / \n",
"0-Games / 8-Football / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 680-Subaru / 1286-Subaru / \n",
"1-Vehicle / 4-Car / 118-Grand Theft Auto V / 201-Grand Theft Auto: San Andreas / \n",
"0-Games / 8-Football / 54-Highlight film / \n",
"83-Skateboarding / 108-Skateboard / 154-Wheel / \n",
"2653-Wedding ring / \n",
"6-Animation / 15-Cartoon / 1281-Couch / \n",
"0-Games / 35-Minecraft / 775-Bee / \n",
"0-Games / 1401-The Witcher 3: Wild Hunt / \n",
"0-Games / 51-Strategy video game / 165-League of Legends / \n",
"0-Games / 2-Video game / 19-PC game / 118-Grand Theft Auto V / 192-Battlefield / 383-Battlefield 4 / \n",
"0-Games / 278-GoPro / 463-Cricket / \n",
"110-Album / \n",
"1-Vehicle / 4-Car / 88-Machine / 309-Computer hardware / 602-Central processing unit / \n",
"1-Vehicle / 4-Car / 70-Driving / 94-Dashcam / 212-Highway / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 111-PlayStation 3 / 118-Grand Theft Auto V / \n",
"1-Vehicle / 4-Car / 114-Engine / 154-Wheel / 234-Ford / 918-Brake / 2899-Ford Explorer / \n",
"1-Vehicle / 50-Aircraft / 82-Airplane / 92-Radio-controlled model / 149-Model aircraft / 160-Radio-controlled aircraft / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 1221-MINI / 1418-Model car / \n",
"5-Dance / 44-Drums / 98-Festival / 250-Parade / \n",
"0-Games / 2-Video game / 89-Comics / 737-Joker / 1392-Injustice: Gods Among Us / \n",
"29-Smartphone / 1366-LG Optimus series / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 784-Honey / 1022-Chili pepper / \n",
"1-Vehicle / 87-Boat / 92-Radio-controlled model / 342-Windows Media Video / 3311-Hovercraft / \n",
"121-Photography / 155-Camera / 721-Digital SLR / \n",
"6-Animation / 248-Hotel / 369-Resort / 468-Biology / \n",
"3-Concert / 7-Musician / 24-String instrument / 842-Harp / \n",
"5-Dance / 16-Performance art / 115-Ballet / 191-Cheerleading / \n",
"6-Animation / \n",
"593-Moon / 654-Sun / \n",
"6-Animation / 15-Cartoon / \n",
"1-Vehicle / 4-Car / 40-Road / 70-Driving / 94-Dashcam / 212-Highway / 2020-Toyota 86 / \n",
"0-Games / 51-Strategy video game / 373-Clash of Clans / \n",
"22-Nature / 164-Beach / \n",
"512-Printing / \n",
"6-Animation / 97-Drawing / 183-Manga / 269-Comic book / 396-One Piece / \n",
"1-Vehicle / 88-Machine / 249-Heavy equipment / 283-Rock / 976-Demolition / 1995-Hammer / \n",
"0-Games / 772-Bowling / 930-Ten-pin bowling / \n",
"3-Concert / 68-Lighting / \n",
"6-Animation / 10-Animal / 15-Cartoon / 80-Horse / 290-Pony / 343-My Little Pony / \n",
"9-Music video / \n",
"1-Vehicle / 4-Car / 1074-Hyundai / 4199-Hyundai Sonata / \n",
"1-Vehicle / 27-Motorcycle / 154-Wheel / 158-Tire / 918-Brake / 4170-Honda Shadow / \n",
"3-Concert / \n",
"3-Concert / \n",
"14-Guitar / 49-School / 63-Acoustic guitar / \n",
"66-Bollywood / \n",
"1507-Calligraphy / \n",
"3676-Dance Central 3 / \n",
"0-Games / 1-Vehicle / 2-Video game / 4-Car / 34-Action-adventure game / 201-Grand Theft Auto: San Andreas / 858-Carl Johnson / \n",
"6-Animation / 97-Drawing / \n",
"0-Games / 8-Football / 54-Highlight film / 79-American football / \n",
"6-Animation / 15-Cartoon / 20-Trailer / 185-Dragon Ball / \n",
"2582-Split / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 728-Dirt track racing / \n",
"144-Amusement park / 248-Hotel / 369-Resort / \n",
"2001-Harry Potter and the Deathly Hallows / \n",
"6-Animation / 15-Cartoon / \n",
"10-Animal / 22-Nature / 132-Bird / 280-Wildlife / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 109-Cooking show / 184-Vegetable / 210-Cookware and bakeware / 239-Roasting / 1295-Stir frying / 2959-Wok / \n",
"3-Concert / 68-Lighting / \n",
"0-Games / 2-Video game / 1895-Werewolf / 2054-Diablo II / \n",
"6-Animation / \n",
"5-Dance / 59-Winter sport / 112-Ice skating / 126-The Walt Disney Company / 262-Ice / 316-Figure skating / \n",
"46-Choir / \n",
"110-Album / \n",
"8-Football / \n",
"0-Games / 2-Video game / 19-PC game / 788-Juggling / 4130-Rumble Fighter / \n",
"1-Vehicle / 4-Car / 64-Transport / 371-Firefighter / 796-Fire engine / 1020-Emergency vehicle / \n",
"1-Vehicle / 4-Car / 245-BMW / 4393-BMW 5 Series (F10) / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 47-Personal computer / 72-iPhone / 145-Tablet computer / 189-iPad / 1150-iPad 2 / 1504-iPad 3 / \n",
"23-Mobile phone / 29-Smartphone / \n",
"4359-Anno / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 16-Performance art / \n",
"0-Games / 51-Strategy video game / 165-League of Legends / \n",
"8-Football / \n",
"1-Vehicle / 50-Aircraft / 82-Airplane / 151-Landing / 157-Jet aircraft / 388-Fighter aircraft / \n",
"18-Outdoor recreation / 144-Amusement park / 321-Roller coaster / 334-Amusement ride / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 2375-Ney / \n",
"1-Vehicle / 23-Mobile phone / \n",
"45-Cosmetics / 173-Eye shadow / 1534-H&M / \n",
"1-Vehicle / 4-Car / 64-Transport / 286-Bus / \n",
"3-Concert / 427-Trumpet / \n",
"45-Cosmetics / 276-Room / 402-Jewellery / \n",
"5-Dance / 296-Saxophone / \n",
"5-Dance / 250-Parade / \n",
"365-Fireworks / \n",
"3-Concert / \n",
"31-Disc jockey / \n",
"5-Dance / \n",
"1-Vehicle / 17-Racing / 18-Outdoor recreation / 67-Cycling / 69-Bicycle / 163-Running / 220-Mountain bike / 284-Mountain biking / 570-Road bicycle racing / 645-Road bicycle / 674-Marathon / \n",
"18-Outdoor recreation / 22-Nature / 42-Fishing / 85-Combat / 137-Tree / 147-River / 351-Jumping / 1449-Rubber band / 1921-Bungee jumping / \n",
"5-Dance / \n",
"22-Nature / \n",
"3-Concert / 7-Musician / 30-Drummer / \n",
"5-Dance / 16-Performance art / 115-Ballet / 153-Dress / 1253-Ballet Dancer / \n",
"0-Games / 2-Video game / 19-PC game / 241-Fighting game / 764-The King of Fighters / 2354-The King of Fighters 2002 / \n",
"5-Dance / 78-Wedding / \n",
"3-Concert / 68-Lighting / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / \n",
"44-Drums / \n",
"943-Stove / \n",
"66-Bollywood / \n",
"246-Furniture / 920-Chair / 2669-Leather crafting / 3329-Upholstery / \n",
"1862-Corel / \n",
"126-The Walt Disney Company / \n",
"0-Games / 8-Football / 54-Highlight film / 79-American football / \n",
"121-Photography / \n",
"3-Concert / 7-Musician / \n",
"6-Animation / 20-Trailer / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"3-Concert / \n",
"3-Concert / 7-Musician / 24-String instrument / \n",
"0-Games / 2-Video game / 55-Video game console / 581-PlayStation / \n",
"89-Comics / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 16-Performance art / 38-Orchestra / 98-Festival / \n",
"1-Vehicle / 12-Food / 124-Tractor / 156-Agriculture / 174-Farm / 249-Heavy equipment / 931-Combine Harvester / \n",
"3-Concert / 133-Nightclub / 150-Violin / 988-Ibiza / \n",
"1-Vehicle / 84-Snow / 120-Winter / 155-Camera / 522-Helmet / 1323-Helmet camera / \n",
"260-Prayer / \n",
"7-Musician / 24-String instrument / 150-Violin / \n",
"0-Games / 2-Video game / 90-Sports game / 180-FIFA 15 / 561-Madden NFL / \n",
"9-Music video / \n",
"6-Animation / 15-Cartoon / \n",
"110-Album / \n",
"0-Games / 2-Video game / 19-PC game / 1861-Smite / \n",
"3-Concert / 7-Musician / 36-Piano / 107-Musical keyboard / \n",
"7-Musician / 14-Guitar / 24-String instrument / 361-Flamenco / 2380-Flamenco guitar / \n",
"10-Animal / 537-Zoo / 1198-Birth / \n",
"3-Concert / 16-Performance art / \n",
"1-Vehicle / 4-Car / 41-Sports car / 231-Supercar / 569-Lamborghini / 1418-Model car / 2225-Lamborghini Murciélago / \n",
"3-Concert / 7-Musician / \n",
"9-Music video / \n",
"1-Vehicle / 87-Boat / \n",
"23-Mobile phone / 29-Smartphone / 72-iPhone / 516-Headphones / 832-Headset / \n",
"96-Soldier / 99-Christmas / 466-Gift / 1107-United States Navy / \n",
"283-Rock / 1001-Gemstone / \n",
"1-Vehicle / 4-Car / 88-Machine / 114-Engine / \n",
"1-Vehicle / 4-Car / 17-Racing / 57-Race track / 3602-Mini-Z / \n",
"726-Clarinet / \n",
"1-Vehicle / 4-Car / \n",
"23-Mobile phone / 616-The Bible / \n",
"56-Hair / 61-Art / 457-Knitting / 653-Thread / 1536-Woven fabric / \n",
"10-Animal / 80-Horse / 247-Stallion / 289-Dressage / \n",
"1078-Installation art / 1678-Foam / \n",
"49-School / 476-Textile / 1479-Quilt / \n",
"10-Animal / 80-Horse / 247-Stallion / \n",
"1-Vehicle / 4-Car / 552-Honda Civic / \n",
"1-Vehicle / 4-Car / 40-Road / 94-Dashcam / 234-Ford / 473-Ford Mustang / 1285-Parking / \n",
"1-Vehicle / 4-Car / 41-Sports car / \n",
"2-Video game / 192-Battlefield / 374-Battlefield 3 / 2948-Dogfight / 3450-Battlefield 1942 / \n",
"3-Concert / \n",
"45-Cosmetics / 173-Eye shadow / 193-Eye liner / 195-Mascara / 196-Eye / 218-Eyelash / 291-Rouge / \n",
"155-Camera / 278-GoPro / \n",
"0-Games / 2-Video game / 19-PC game / 43-Call of Duty / 134-Call of Duty: Black Ops / 138-Call of Duty: Black Ops II / 431-Call of Duty: Advanced Warfare / 1248-Call of Duty: Zombies / \n",
"827-Pump / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 345-Loudspeaker / 1221-MINI / 1565-Logitech / 3285-Boombox / \n",
"3-Concert / 16-Performance art / 46-Choir / \n",
"85-Combat / 117-Boxing / 399-Kickboxing / 1239-K-1 / \n",
"59-Winter sport / 84-Snow / 120-Winter / 177-Skiing / \n",
"18-Outdoor recreation / 163-Running / \n",
"3-Concert / 5-Dance / 13-Musical ensemble / 16-Performance art / 38-Orchestra / \n",
"3-Concert / 7-Musician / 36-Piano / 53-Keyboard / 104-Pianist / \n",
"9-Music video / \n",
"4171-Air Force 1 / \n",
"467-Chipmunk / 846-Squirrel / \n",
"13-Musical ensemble / 820-Bagpipes / 1646-Pipe band / \n",
"3522-ProSieben / \n",
"0-Games / 2-Video game / 43-Call of Duty / 431-Call of Duty: Advanced Warfare / \n",
"3-Concert / \n",
"1-Vehicle / 4-Car / 64-Transport / 74-Truck / 250-Parade / 371-Firefighter / 796-Fire engine / 1020-Emergency vehicle / \n",
"0-Games / 2-Video game / 77-Arena / 211-RuneScape / \n",
"0-Games / 2-Video game / 35-Minecraft / \n",
"296-Saxophone / 1006-Tenor saxophone / \n",
"0-Games / 2-Video game / 33-Weapon / 441-The Elder Scrolls / 3418-The Elder Scrolls III: Morrowind / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 14-Guitar / 30-Drummer / 39-Drums / 44-Drums / \n",
"339-Architecture / \n",
"0-Games / 2-Video game / 6-Animation / 15-Cartoon / 723-Pig / 1426-Treasure / \n",
"0-Games / 2-Video game / 229-Halo / 644-Halo: Reach / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 201-Grand Theft Auto: San Andreas / \n",
"45-Cosmetics / 235-Lipstick / 1012-Lip gloss / \n",
"414-Computer monitor / \n",
"1-Vehicle / 4-Car / 277-Sport utility vehicle / 1806-GMC / 2683-GMC / \n",
"6-Animation / \n",
"20-Trailer / 2096-The Hobbit / 2493-The Hobbit / 3792-Dwarf / \n",
"341-Sketch comedy / \n",
"10-Animal / 48-Pet / 71-Dog / 1252-Dog agility / \n",
"5-Dance / \n",
"0-Games / 8-Football / 1338-Devil / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 76-Ball / \n",
"0-Games / 2-Video game / 19-PC game / 43-Call of Duty / 118-Grand Theft Auto V / 422-Call of Duty: Ghosts / \n",
"0-Games / 35-Minecraft / 1151-Temple / \n",
"3-Concert / \n",
"28-Fashion / 757-Boot / 1481-Sunglasses / 2326-Royal Air Force / \n",
"9-Music video / \n",
"66-Bollywood / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 101-Telephone / \n",
"320-Super Smash Bros. / 335-Nintendo 3DS / 443-Wii U / 697-Super Smash Bros. for Nintendo 3DS and Wii U / 3050-Mewtwo / \n",
"20-Trailer / 294-Batman / \n",
"18-Outdoor recreation / 59-Winter sport / 84-Snow / 120-Winter / 177-Skiing / 305-Ski / \n",
"20-Trailer / \n",
"0-Games / 75-Wrestling / \n",
"283-Rock / 353-Wall / \n",
"12-Food / 26-Cooking / 184-Vegetable / 215-Eating / 944-Hamburger / \n",
"3-Concert / \n",
"6-Animation / 15-Cartoon / 243-Sonic the Hedgehog / 251-Sonic the Hedgehog / \n",
"358-Rugby football / \n",
"5-Dance / 16-Performance art / 95-Talent show / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 57-Race track / \n",
"3-Concert / 38-Orchestra / \n",
"3-Concert / 7-Musician / \n",
"1-Vehicle / 87-Boat / 225-Lake / 498-Motorboat / \n",
"12-Food / 32-Recipe / 226-Dessert / 232-Cake / 236-Baking / 295-Chocolate / 578-Cookie / 1322-Biscuit / \n",
"47-Personal computer / 131-Computer / 309-Computer hardware / 957-Desktop computer / 1045-Computer case / \n",
"33-Weapon / 315-Knife / \n",
"5-Dance / 16-Performance art / 115-Ballet / \n",
"35-Minecraft / \n",
"1-Vehicle / 83-Skateboarding / 108-Skateboard / \n",
"9-Music video / \n",
"131-Computer / 189-iPad / \n",
"3-Concert / 5-Dance / 49-School / 130-Gymnastics / 191-Cheerleading / \n",
"1-Vehicle / 250-Parade / \n",
"9-Music video / 594-Music festival / \n",
"25-Toy / 140-LEGO / \n",
"9-Music video / \n",
"0-Games / 8-Football / \n",
"3-Concert / 13-Musical ensemble / 49-School / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 73-Ball / \n",
"155-Camera / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 73-Ball / 76-Ball / 81-Athlete / 90-Sports game / \n",
"5-Dance / 49-School / \n",
"737-Joker / \n",
"8-Football / 21-Stadium / 60-Basketball / 77-Arena / \n",
"1-Vehicle / 62-Train / 103-Rail transport / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 46-Choir / \n",
"1-Vehicle / 62-Train / 74-Truck / 2655-Vehicle horn / \n",
"75-Wrestling / 977-WWE '13 / \n",
"67-Cycling / 69-Bicycle / 86-Plant / 136-Gardening / 520-Bicycle frame / \n",
"110-Album / \n",
"3-Concert / \n",
"9-Music video / \n",
"5-Dance / 16-Performance art / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 43-Call of Duty / 96-Soldier / 134-Call of Duty: Black Ops / 216-Call of Duty: Modern Warfare 3 / 302-Call of Duty 4: Modern Warfare / \n",
"6-Animation / 15-Cartoon / \n",
"0-Games / 2-Video game / 19-PC game / 1412-Half-Life / \n",
"2383-White House / \n",
"1-Vehicle / 4-Car / 11-Motorsport / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 72-iPhone / 101-Telephone / 527-Battery / 1159-Battery charger / 2163-Rechargeable battery / \n",
"5-Dance / 361-Flamenco / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"1-Vehicle / 4-Car / 212-Highway / 1415-Kia Motors / \n",
"10-Animal / 48-Pet / 91-Fish / 258-Aquarium / \n",
"14-Guitar / 39-Drums / 44-Drums / 521-Metal / \n",
"3-Concert / 68-Lighting / 98-Festival / 3285-Boombox / \n",
"0-Games / 2-Video game / 6-Animation / 849-Tales / 3415-Tales of Vesperia / \n",
"3808-Goggles / \n",
"12-Food / 827-Pump / 1616-Electrical wiring / \n",
"5-Dance / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 109-Cooking show / 184-Vegetable / \n",
"0-Games / 159-World of Warcraft / 171-Warcraft / \n",
"45-Cosmetics / 436-Concealer / \n",
"3-Concert / \n",
"3-Concert / \n",
"9-Music video / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 40-Road / 57-Race track / 378-Drag racing / \n",
"1-Vehicle / 18-Outdoor recreation / 42-Fishing / 87-Boat / \n",
"28-Fashion / 45-Cosmetics / 56-Hair / 106-Hairstyle / 195-Mascara / 218-Eyelash / 235-Lipstick / 291-Rouge / \n",
"7-Musician / 36-Piano / 53-Keyboard / 104-Pianist / \n",
"3-Concert / \n",
"5-Dance / 16-Performance art / \n",
"0-Games / 2-Video game / 1091-Saints Row / 1695-Saints Row: The Third / \n",
"398-The Legend of Zelda / 541-Black-and-white / 1228-The Legend of Zelda: Ocarina of Time / \n",
"6-Animation / 15-Cartoon / 4276-Toaster / \n",
"2532-Pan flute / \n",
"0-Games / 2-Video game / 19-PC game / 51-Strategy video game / 382-Dota 2 / 549-Defense of the Ancients / 1378-Multiplayer online battle arena / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 30-Drummer / 230-Brass instrument / \n",
"5-Dance / \n",
"810-Village / \n",
"0-Games / 211-RuneScape / \n",
"98-Festival / 102-Building / 2341-Fine art / \n",
"6-Animation / 15-Cartoon / \n",
"1-Vehicle / 4-Car / 154-Wheel / 234-Ford / 918-Brake / 2256-Bearing / 3684-Ford Bronco / \n",
"12-Food / 3117-Match / \n",
"5-Dance / \n",
"309-Computer hardware / \n",
"0-Games / 35-Minecraft / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"6-Animation / \n",
"56-Hair / 99-Christmas / 106-Hairstyle / 405-Braid / \n",
"2799-Ys / \n",
"46-Choir / \n",
"25-Toy / \n",
"6-Animation / 34-Action-adventure game / 264-Star Wars / \n",
"1880-Marinera / \n",
"1249-Rainbow Loom / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 46-Choir / 318-Church / \n",
"0-Games / 211-RuneScape / 1178-Quest / \n",
"3-Concert / 5-Dance / 153-Dress / \n",
"1-Vehicle / 74-Truck / \n",
"8-Football / \n",
"12-Food / 26-Cooking / 32-Recipe / 179-Kitchen / \n",
"14-Guitar / 24-String instrument / \n",
"18-Outdoor recreation / 163-Running / 674-Marathon / \n",
"0-Games / 2-Video game / 33-Weapon / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 43-Call of Duty / 96-Soldier / 216-Call of Duty: Modern Warfare 3 / \n",
"3-Concert / 7-Musician / 30-Drummer / 39-Drums / 44-Drums / \n",
"5-Dance / 16-Performance art / \n",
"1-Vehicle / 11-Motorsport / 27-Motorcycle / 608-Wheelie / 927-Supermoto / \n",
"0-Games / 772-Bowling / 930-Ten-pin bowling / \n",
"0-Games / \n",
"12-Food / 612-Beer / 720-Wine / \n",
"6-Animation / 547-Bleach / \n",
"1-Vehicle / 4-Car / 901-Mitsubishi / 1461-Electric car / 2304-Electric vehicle / \n",
"144-Amusement park / 248-Hotel / 369-Resort / \n",
"3-Concert / 5-Dance / 16-Performance art / 95-Talent show / \n",
"6-Animation / 15-Cartoon / 89-Comics / 183-Manga / 185-Dragon Ball / 244-Goku / \n",
"0-Games / 8-Football / 54-Highlight film / 79-American football / \n",
"0-Games / 59-Winter sport / 112-Ice skating / 142-Hockey / 312-Ice rink / \n",
"0-Games / 523-Cattle / 626-Puzzle / \n",
"61-Art / 587-Clay / 970-Monkey / \n",
"5-Dance / 44-Drums / \n",
"0-Games / 8-Football / 21-Stadium / 77-Arena / \n",
"86-Plant / \n",
"3-Concert / 16-Performance art / 46-Choir / \n",
"1-Vehicle / 4-Car / 207-Exhaust system / 299-Volkswagen Passenger Cars / 1761-Volkswagen Jetta / 3155-Volkswagen Golf Mk4 / \n",
"25-Toy / 626-Puzzle / 631-Cube / 740-Rubik's Cube / \n",
"0-Games / 19-PC game / 159-World of Warcraft / 171-Warcraft / \n",
"6-Animation / 9-Music video / 97-Drawing / 408-Sketch / \n",
"0-Games / 8-Football / 21-Stadium / 54-Highlight film / 65-Kick / 77-Arena / \n",
"3-Concert / 7-Musician / 30-Drummer / \n",
"0-Games / 8-Football / 110-Album / \n",
"7-Musician / 13-Musical ensemble / 38-Orchestra / \n",
"5-Dance / 36-Piano / 53-Keyboard / 763-Snake / \n",
"0-Games / 1-Vehicle / 50-Aircraft / 92-Radio-controlled model / 149-Model aircraft / 160-Radio-controlled aircraft / 266-Helicopter / 392-Radio-controlled helicopter / \n",
"6-Animation / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / 230-Brass instrument / \n",
"3-Concert / 46-Choir / 49-School / 776-Kindergarten / \n",
"3-Concert / 7-Musician / 30-Drummer / \n",
"31-Disc jockey / \n",
"3-Concert / 7-Musician / 16-Performance art / 98-Festival / 594-Music festival / \n",
"5-Dance / \n",
"0-Games / 2-Video game / 1505-Ultimate Marvel vs. Capcom 3 / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"22-Nature / \n",
"28-Fashion / 153-Dress / 390-Model / 495-Runway / \n",
"1-Vehicle / 4-Car / 41-Sports car / 231-Supercar / 569-Lamborghini / \n",
"25-Toy / \n",
"208-Wood / 981-Sculpture / 1346-Wood carving / 1480-Astronaut / \n",
"222-Surfing / 233-Ocean / \n",
"0-Games / 2-Video game / 51-Strategy video game / 356-Star / 373-Clash of Clans / \n",
"56-Hair / 123-Naruto / 583-Wig / \n",
"3-Concert / 16-Performance art / \n",
"8-Football / 9-Music video / \n",
"12-Food / 26-Cooking / 32-Recipe / 58-Cuisine / 3288-Mustard / \n",
"5-Dance / \n",
"12-Food / 786-Juice / 1081-Lemon / 3598-Lime / \n",
"12-Food / 32-Recipe / 2588-Mayonnaise / \n",
"122-Weight training / 204-Gym / 214-Muscle / \n",
"15-Cartoon / 185-Dragon Ball / 244-Goku / 418-Gohan / \n",
"0-Games / 2-Video game / 6-Animation / 514-Tekken / 797-Tekken / 1470-Tekken Tag Tournament 2 / \n",
"30-Drummer / 39-Drums / 44-Drums / 1953-Roland V-Drums / \n",
"9-Music video / 66-Bollywood / \n",
"2-Video game / \n",
"25-Toy / 75-Wrestling / 164-Beach / 435-Action figure / \n",
"6-Animation / 439-Barbie / \n",
"31-Disc jockey / \n",
"3-Concert / 7-Musician / 16-Performance art / 30-Drummer / \n",
"7-Musician / 14-Guitar / 24-String instrument / 100-Electric guitar / \n",
"12-Food / 617-Maize / \n",
"46-Choir / 99-Christmas / \n",
"1-Vehicle / 4-Car / 154-Wheel / \n",
"5-Dance / \n",
"0-Games / 2-Video game / 1681-Mu Online / \n",
"45-Cosmetics / 466-Gift / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 96-Soldier / 257-Counter-Strike / \n",
"8-Football / 358-Rugby football / \n",
"130-Gymnastics / 1417-Balance beam / 2745-Beam / \n",
"1621-Ork / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 14-Guitar / 24-String instrument / 30-Drummer / 39-Drums / \n",
"0-Games / 379-Touhou Project / 3849-Perfect Cherry Blossom / \n",
"6-Animation / 3588-Berserk / \n",
"1-Vehicle / 283-Rock / 1409-Missile / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 41-Sports car / 70-Driving / 207-Exhaust system / 270-Sedan / 344-Coupé / 349-Chevrolet / 406-Chevrolet / 947-Muffler / 1752-Chevrolet SS / \n",
"6-Animation / 123-Naruto / \n",
"10-Animal / 18-Outdoor recreation / 80-Horse / 247-Stallion / 438-Mare / 3383-Filly / \n",
"163-Running / \n",
"89-Comics / \n",
"12-Food / \n",
"66-Bollywood / \n",
"3-Concert / 68-Lighting / 77-Arena / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 274-Meat / \n",
"12-Food / \n",
"6-Animation / 185-Dragon Ball / 244-Goku / \n",
"12-Food / 424-Chicken / 597-Soup / \n",
"1-Vehicle / 4-Car / 1194-Automotive lighting / \n",
"6-Animation / 15-Cartoon / 61-Art / 251-Sonic the Hedgehog / \n",
"9-Music video / 3401-Aggro Berlin / \n",
"23-Mobile phone / 29-Smartphone / 101-Telephone / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 43-Call of Duty / 96-Soldier / 194-Call of Duty: Modern Warfare 2 / \n",
"5-Dance / 31-Disc jockey / \n",
"36-Piano / 53-Keyboard / 104-Pianist / 107-Musical keyboard / 237-Final Fantasy / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 35-Minecraft / 1007-Portal / \n",
"61-Art / 238-Nail / 300-Nail art / 338-Manicure / \n",
"1-Vehicle / 4-Car / 1134-Taxicab / \n",
"9-Music video / \n",
"5-Dance / 66-Bollywood / \n",
"3-Concert / 68-Lighting / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 41-Sports car / 70-Driving / 207-Exhaust system / 231-Supercar / \n",
"7-Musician / 13-Musical ensemble / 98-Festival / 203-Marching band / \n",
"18-Outdoor recreation / 137-Tree / 148-Painting / 303-Paint / 2124-Spray painting / 2853-Aerosol paint / 3188-Camouflage / \n",
"0-Games / 1991-Rappelz / \n",
"1-Vehicle / 2-Video game / 4-Car / 1333-Wangan Midnight / \n",
"18-Outdoor recreation / 83-Skateboarding / 108-Skateboard / 556-Skateboarding trick / \n",
"3-Concert / 31-Disc jockey / 133-Nightclub / \n",
"12-Food / 184-Vegetable / 348-Fruit / 671-Tomato / 1193-Orange / 2676-Citrus / 3207-Peel / 3293-Vegetable carving / \n",
"23-Mobile phone / 29-Smartphone / \n",
"5-Dance / 6-Animation / 16-Performance art / 126-The Walt Disney Company / 576-Walt Disney World / 1139-Belle / 1265-Kyle Kingson / \n",
"0-Games / 372-Playing card / 791-Magic: The Gathering / 1784-Goblin / \n",
"9-Music video / \n",
"1338-Devil / \n",
"1-Vehicle / 4-Car / 70-Driving / 94-Dashcam / \n",
"0-Games / 2-Video game / 453-Pro Evolution Soccer / 3526-Pro Evolution Soccer 2009 / \n",
"20-Trailer / 66-Bollywood / \n",
"5-Dance / 59-Winter sport / 112-Ice skating / \n",
"7-Musician / 14-Guitar / 24-String instrument / 63-Acoustic guitar / 1936-Bouzouki / \n",
"3-Concert / 7-Musician / \n",
"0-Games / 2-Video game / 8-Football / 54-Highlight film / 90-Sports game / 111-PlayStation 3 / 453-Pro Evolution Soccer / \n",
"31-Disc jockey / \n",
"61-Art / 148-Painting / \n",
"1-Vehicle / 4-Car / 40-Road / 94-Dashcam / 2850-Chauffeur / \n",
"28-Fashion / 56-Hair / 106-Hairstyle / 340-Afro-textured hair / 919-Hair conditioner / \n",
"56-Hair / \n",
"0-Games / 8-Football / 90-Sports game / 453-Pro Evolution Soccer / \n",
"0-Games / 2-Video game / 19-PC game / 33-Weapon / 43-Call of Duty / 216-Call of Duty: Modern Warfare 3 / \n",
"3214-Black Widow / \n",
"0-Games / 710-Handball / \n",
"7-Musician / 14-Guitar / 24-String instrument / 63-Acoustic guitar / \n",
"0-Games / 10-Animal / 51-Strategy video game / 550-Dinosaur / 1718-Spore / \n",
"0-Games / 75-Wrestling / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"14-Guitar / 63-Acoustic guitar / \n",
"1-Vehicle / 4-Car / 70-Driving / 1321-Škoda Auto / 2690-Škoda Octavia / \n",
"0-Games / 8-Football / 79-American football / \n",
"5-Dance / \n",
"31-Disc jockey / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 86-Plant / 137-Tree / 167-Four-wheel drive / 198-Off-road vehicle / 360-Jeep / 565-Mud bogging / \n",
"18-Outdoor recreation / 83-Skateboarding / 108-Skateboard / 221-Skatepark / \n",
"31-Disc jockey / \n",
"1-Vehicle / 22-Nature / 50-Aircraft / 92-Radio-controlled model / 149-Model aircraft / 160-Radio-controlled aircraft / 266-Helicopter / 392-Radio-controlled helicopter / \n",
"9-Music video / 14-Guitar / 24-String instrument / 63-Acoustic guitar / 4484-Nikon D7000 / \n",
"1-Vehicle / 67-Cycling / 69-Bicycle / 220-Mountain bike / 3611-Folding bicycle / \n",
"45-Cosmetics / \n",
"37-Gadget / 832-Headset / \n",
"10-Animal / 42-Fishing / 91-Fish / 275-Diving / 288-Underwater / 381-Underwater diving / \n",
"0-Games / 2-Video game / 159-World of Warcraft / 171-Warcraft / \n",
"3-Concert / 7-Musician / 38-Orchestra / 219-Accordion / \n",
"12-Food / 612-Beer / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 27-Motorcycle / 57-Race track / 853-Dune buggy / \n",
"0-Games / 60-Basketball / 77-Arena / 168-Basketball moves / \n",
"208-Wood / 303-Paint / 400-Woodturning / 462-Table / 673-Saw / 2548-Table saw / \n",
"7-Musician / 13-Musical ensemble / 38-Orchestra / 150-Violin / 230-Brass instrument / 1574-French horn / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 24-String instrument / 670-Banjo / \n",
"0-Games / 2-Video game / 19-PC game / 43-Call of Duty / 96-Soldier / 194-Call of Duty: Modern Warfare 2 / \n",
"141-Microsoft Windows / \n",
"1-Vehicle / 6-Animation / 50-Aircraft / 82-Airplane / 129-Aviation / \n",
"49-School / 99-Christmas / \n",
"60-Basketball / 168-Basketball moves / \n",
"5-Dance / 889-Swing / \n",
"1-Vehicle / 62-Train / 64-Transport / 103-Rail transport / 119-Locomotive / 143-Railroad car / 161-Train station / \n",
"6-Animation / 15-Cartoon / 477-Transformers / 2186-Megatron / 3398-Transformers: Generation 1 / \n",
"10-Animal / 48-Pet / 166-Cat / 346-Kitten / \n",
"0-Games / 35-Minecraft / 3331-Quarry / \n",
"12-Food / 26-Cooking / 32-Recipe / 36-Piano / 53-Keyboard / 109-Cooking show / 215-Eating / 236-Baking / \n",
"8-Football / \n",
"61-Art / 447-Sand / 3282-Sand art and play / 3550-Sand animation / \n",
"5-Dance / 78-Wedding / 259-Fire / \n",
"8-Football / \n",
"102-Building / 113-House / 170-Home improvement / 276-Room / 580-Bedroom / \n",
"5-Dance / 16-Performance art / 115-Ballet / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 46-Choir / 318-Church / 870-Chapel / \n",
"18-Outdoor recreation / 42-Fishing / 548-Parachuting / 573-Paragliding / 769-Red Bull / \n",
"3822-Mold / \n",
"38-Orchestra / \n",
"497-Macintosh / 819-World Wide Web / \n",
"6-Animation / \n",
"5-Dance / \n",
"170-Home improvement / 652-Bathroom / \n",
"6-Animation / 20-Trailer / \n",
"1-Vehicle / 4-Car / 88-Machine / 114-Engine / \n",
"1-Vehicle / 18-Outdoor recreation / \n",
"1-Vehicle / 11-Motorsport / 172-Motocross / \n",
"3-Concert / 708-Mermaid / \n",
"0-Games / 2-Video game / 43-Call of Duty / 704-Call of Duty: World at War / \n",
"0-Games / 8-Football / 21-Stadium / 65-Kick / 76-Ball / 77-Arena / 81-Athlete / \n",
"8-Football / 696-T-shirt / 2693-Puma SE / \n",
"0-Games / 2-Video game / 33-Weapon / 96-Soldier / 257-Counter-Strike / \n",
"13-Musical ensemble / 46-Choir / \n",
"28-Fashion / 45-Cosmetics / 676-Handbag / 785-Jeans / \n",
"2-Video game / 141-Microsoft Windows / 2302-Dying Light / \n",
"3-Concert / 7-Musician / 16-Performance art / \n",
"840-Museum / \n",
"6-Animation / \n",
"3-Concert / 7-Musician / 30-Drummer / \n",
"356-Star / \n",
"37-Gadget / 47-Personal computer / 309-Computer hardware / 700-Hard disk drive / \n",
"3-Concert / 7-Musician / 14-Guitar / \n",
"47-Personal computer / 58-Cuisine / 145-Tablet computer / \n",
"162-Medicine / \n",
"162-Medicine / 209-University / \n",
"163-Running / \n",
"1-Vehicle / 59-Winter sport / 84-Snow / 120-Winter / 177-Skiing / 305-Ski / \n",
"6-Animation / \n",
"9-Music video / \n",
"42-Fishing / 825-Stream / \n",
"136-Gardening / 585-Lawn / 2437-String trimmer / \n",
"1-Vehicle / 18-Outdoor recreation / 22-Nature / 40-Road / 67-Cycling / 69-Bicycle / 86-Plant / 137-Tree / 197-Trail / 213-Forest / 1887-Tricycle / 3713-Recumbent bicycle / \n",
"3-Concert / 7-Musician / \n",
"1264-Head / \n",
"28-Fashion / 45-Cosmetics / 56-Hair / \n",
"36-Piano / 150-Violin / 401-Cello / \n",
"1-Vehicle / 27-Motorcycle / 2242-Pillow / \n",
"2887-Swing / \n",
"3-Concert / 5-Dance / 16-Performance art / 38-Orchestra / 68-Lighting / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 155-Camera / 566-Digital camera / \n",
"481-Kayak / 949-Rowing / 1090-Canoe / 1305-Canoeing / 1372-Paddle / 3858-Canoe Slalom / \n",
"0-Games / 2-Video game / 47-Personal computer / 456-Go-kart / 518-Mario Kart / 2901-Mario Kart 64 / \n",
"0-Games / 2-Video game / 19-PC game / 51-Strategy video game / 382-Dota 2 / 549-Defense of the Ancients / \n",
"10-Animal / 18-Outdoor recreation / 80-Horse / 247-Stallion / 289-Dressage / 351-Jumping / 404-Livestock / \n",
"83-Skateboarding / 1105-Fingerboard / \n",
"28-Fashion / 153-Dress / 2392-Blouse / \n",
"209-University / 3503-Doctor / \n",
"3-Concert / \n",
"22-Nature / \n",
"0-Games / 2-Video game / 20-Trailer / 379-Touhou Project / 518-Mario Kart / \n",
"1-Vehicle / 18-Outdoor recreation / 86-Plant / 124-Tractor / 156-Agriculture / 174-Farm / 252-Tractor pulling / \n",
"0-Games / 6-Animation / 15-Cartoon / 323-Toddler / \n",
"0-Games / 2-Video game / 19-PC game / 35-Minecraft / \n",
"2-Video game / 243-Sonic the Hedgehog / \n",
"9-Music video / \n",
"9-Music video / \n",
"5-Dance / 350-Circus / \n",
"242-Paper / 370-Origami / 466-Gift / 469-Bag / \n",
"2-Video game / 20-Trailer / 426-Resident Evil / \n",
"419-Earth / \n",
"18-Outdoor recreation / 163-Running / 674-Marathon / \n",
"1-Vehicle / 4-Car / 2616-Chevrolet S-10 / \n",
"812-Plastic / 2913-Drawer / \n",
"0-Games / 2-Video game / 19-PC game / 47-Personal computer / 192-Battlefield / 374-Battlefield 3 / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 41-Sports car / 154-Wheel / 234-Ford / 349-Chevrolet / 406-Chevrolet / 473-Ford Mustang / 647-Muscle car / 761-Chevrolet Camaro / 952-Chevrolet Corvette / \n",
"10-Animal / 71-Dog / \n",
"28-Fashion / 45-Cosmetics / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 14-Guitar / 24-String instrument / 30-Drummer / 39-Drums / 44-Drums / \n",
"159-World of Warcraft / \n",
"10-Animal / 22-Nature / 155-Camera / 280-Wildlife / 846-Squirrel / 4388-Canon EOS 650D / \n",
"122-Weight training / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 27-Motorcycle / 57-Race track / 67-Cycling / 69-Bicycle / 2133-Road racing / \n",
"12-Food / 18-Outdoor recreation / \n",
"0-Games / 2-Video game / 19-PC game / 35-Minecraft / \n",
"0-Games / 2-Video game / 4-Car / 34-Action-adventure game / 118-Grand Theft Auto V / \n",
"5-Dance / \n",
"293-Slide show / 444-Family / \n",
"6-Animation / 97-Drawing / 148-Painting / 3411-Digital painting / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 135-Drifting / \n",
"504-Web page / 819-World Wide Web / \n",
"318-Church / \n",
"1-Vehicle / 92-Radio-controlled model / 149-Model aircraft / 160-Radio-controlled aircraft / 266-Helicopter / 392-Radio-controlled helicopter / \n",
"79-American football / 122-Weight training / 204-Gym / 532-Barbell / \n",
"3-Concert / 68-Lighting / \n",
"3-Concert / 16-Performance art / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 70-Driving / 2518-Dirt 3 / \n",
"342-Windows Media Video / 548-Parachuting / 4334-Paratrooper / \n",
"9-Music video / \n",
"0-Games / 2-Video game / 8-Football / 76-Ball / 90-Sports game / 453-Pro Evolution Soccer / 1719-Pro Evolution Soccer 2015 / \n",
"1794-Curtain / \n",
"0-Games / 8-Football / 79-American football / \n",
"15-Cartoon / 123-Naruto / 183-Manga / \n",
"10-Animal / 18-Outdoor recreation / 22-Nature / 42-Fishing / 91-Fish / 132-Bird / 147-River / 268-Recreational fishing / 478-Fishing rod / \n",
"31-Disc jockey / \n",
"110-Album / \n",
"3-Concert / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 17-Racing / 41-Sports car / 1763-Lotus Cars / \n",
"23-Mobile phone / 29-Smartphone / 47-Personal computer / 72-iPhone / 131-Computer / \n",
"102-Building / 248-Hotel / \n",
"1-Vehicle / 85-Combat / 259-Fire / 262-Ice / 371-Firefighter / 1245-Alarm device / \n",
"1-Vehicle / 11-Motorsport / 17-Racing / 27-Motorcycle / 172-Motocross / \n",
"7-Musician / 14-Guitar / 24-String instrument / \n",
"3-Concert / 7-Musician / 30-Drummer / 39-Drums / \n",
"661-Stretching / \n",
"28-Fashion / 206-Shoe / 333-Nike; Inc. / \n",
"273-News program / \n",
"1-Vehicle / 92-Radio-controlled model / 149-Model aircraft / 160-Radio-controlled aircraft / 393-Unmanned aerial vehicle / 604-Quadcopter / \n",
"5-Dance / 430-Ballroom dance / 678-Latin dance / 889-Swing / \n",
"0-Games / 5-Dance / 79-American football / 191-Cheerleading / \n",
"410-Teacher / \n",
"117-Boxing / \n",
"59-Winter sport / 112-Ice skating / 142-Hockey / \n",
"9-Music video / \n",
"9-Music video / \n",
"689-Harmonica / 810-Village / \n",
"0-Games / 8-Football / 21-Stadium / 54-Highlight film / 65-Kick / 76-Ball / 77-Arena / \n",
"9-Music video / 347-Climbing / 2080-Cup / 3686-The North Face / \n",
"1-Vehicle / 4-Car / 3679-Super GT / 4612-Sepang International Circuit / \n",
"12-Food / 2397-Cabbage / \n",
"1-Vehicle / 27-Motorcycle / 105-Motorcycling / \n",
"8-Football / \n",
"5-Dance / 16-Performance art / 769-Red Bull / \n",
"2077-Chemical reaction / \n",
"0-Games / 2-Video game / 159-World of Warcraft / 171-Warcraft / \n",
"0-Games / 8-Football / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 74-Truck / 375-Pickup truck / 2683-GMC / \n",
"0-Games / 2-Video game / 803-DC Comics / 1515-DC Universe Online / \n",
"0-Games / 2-Video game / 19-PC game / 35-Minecraft / \n",
"10-Animal / 48-Pet / 71-Dog / 1084-Bulldog / 2438-French Bulldog / \n",
"3-Concert / 7-Musician / 16-Performance art / 38-Orchestra / \n",
"89-Comics / 396-One Piece / 749-Figurine / \n",
"0-Games / 2-Video game / 217-Hunting / 596-Monster Hunter / 2181-Monster Hunter 4 / \n",
"8-Football / \n",
"30-Drummer / 39-Drums / 44-Drums / 128-Cymbal / 146-Snare drum / \n",
"3-Concert / 7-Musician / 13-Musical ensemble / 38-Orchestra / \n",
"66-Bollywood / \n",
"5-Dance / 49-School / 191-Cheerleading / \n",
"0-Games / 2-Video game / 229-Halo / 470-Halo 3 / 2002-Halo 3: ODST / \n",
"0-Games / 2-Video game / 33-Weapon / 96-Soldier / 192-Battlefield / 383-Battlefield 4 / \n",
"0-Games / 2-Video game / 33-Weapon / 1394-Alliance of Valiant Arms / \n",
"5-Dance / \n",
"123-Naruto / \n",
"3-Concert / 7-Musician / \n",
"0-Games / 2-Video game / 19-PC game / 43-Call of Duty / 134-Call of Duty: Black Ops / 216-Call of Duty: Modern Warfare 3 / \n",
"1-Vehicle / \n",
"0-Games / 1-Vehicle / 2-Video game / 19-PC game / 33-Weapon / 96-Soldier / 734-DayZ / 736-ARMA / 1034-ARMA 2 / \n",
"0-Games / 54-Highlight film / 60-Basketball / 168-Basketball moves / \n",
"372-Playing card / 746-Card manipulation / \n",
"533-Oil / 536-Glass / 1510-Liquid / 4325-Detergent / \n",
"9-Music video / \n",
"0-Games / 379-Touhou Project / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 184-Vegetable / 688-Beef / \n",
"5-Dance / 98-Festival / \n",
"10-Animal / 166-Cat / 970-Monkey / \n",
"1004-Samurai / \n",
"0-Games / 19-PC game / 51-Strategy video game / 165-League of Legends / \n",
"3-Concert / \n",
"0-Games / 2-Video game / 237-Final Fantasy / \n",
"6-Animation / 15-Cartoon / 251-Sonic the Hedgehog / \n",
"4175-8 mm film / \n",
"23-Mobile phone / 29-Smartphone / 932-Microsoft Lumia / \n",
"196-Eye / 402-Jewellery / 2167-Pendant / \n",
"6-Animation / 15-Cartoon / 379-Touhou Project / \n",
"12-Food / 720-Wine / 1720-Grape / 4470-Winery / \n",
"36-Piano / 53-Keyboard / 107-Musical keyboard / 265-Electronic keyboard / 1909-Analog synthesizer / \n",
"75-Wrestling / 1042-Jumbotron / \n",
"31-Disc jockey / \n",
"31-Disc jockey / \n",
"133-Nightclub / \n",
"10-Animal / 20-Trailer / 48-Pet / 71-Dog / 298-Television advertisement / \n",
"457-Knitting / 546-Stitch / 653-Thread / \n",
"10-Animal / 25-Toy / 71-Dog / 319-Robot / \n",
"5-Dance / 16-Performance art / 3831-Supreme / \n",
"83-Skateboarding / 108-Skateboard / 221-Skatepark / \n",
"56-Hair / 607-Lace / \n",
"47-Personal computer / 309-Computer hardware / 513-Computer keyboard / \n",
"0-Games / 2-Video game / 34-Action-adventure game / 201-Grand Theft Auto: San Andreas / \n",
"530-Cue sports / 568-Pool / \n",
"1-Vehicle / 4-Car / 74-Truck / 259-Fire / 371-Firefighter / 2414-Firefighting / \n",
"45-Cosmetics / \n",
"1601-Social media / \n",
"2-Video game / 961-Sega Genesis / 1135-Dolphin / 2638-Master System / 3980-Sega CD / \n",
"1-Vehicle / 2-Video game / 20-Trailer / 34-Action-adventure game / 118-Grand Theft Auto V / 297-Grand Theft Auto IV / 658-Grand Theft Auto: The Lost and Damned / \n",
"12-Food / 26-Cooking / 32-Recipe / 52-Dish / 58-Cuisine / 597-Soup / 2234-Avocado / \n",
"8-Football / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 101-Telephone / 4128-Nokia N97 / \n",
"5-Dance / 1240-Mambo / \n",
"0-Games / 326-Metin2 / \n",
"33-Weapon / \n",
"3-Concert / 7-Musician / 30-Drummer / \n",
"22-Nature / \n",
"905-Flash Video / 2808-Printed circuit board / \n",
"37-Gadget / 47-Personal computer / 228-Laptop / 414-Computer monitor / \n",
"20-Trailer / 298-Television advertisement / 1059-Movie theater / \n",
"8-Football / 21-Stadium / 77-Arena / \n",
"3-Concert / \n",
"22-Nature / \n",
"246-Furniture / 251-Sonic the Hedgehog / 276-Room / \n",
"0-Games / 10-Animal / 384-Horse racing / \n",
"2532-Pan flute / \n",
"8-Football / 9-Music video / 76-Ball / \n",
"5-Dance / 892-Aerobics / \n",
"8-Football / \n",
"5-Dance / \n",
"1-Vehicle / 4-Car / 11-Motorsport / 41-Sports car / 761-Chevrolet Camaro / 947-Muffler / \n",
"5-Dance / 95-Talent show / \n",
"68-Lighting / 121-Photography / 721-Digital SLR / \n",
"31-Disc jockey / 2193-CDJ / \n",
"93-Comedy / \n",
"0-Games / 8-Football / 21-Stadium / 81-Athlete / \n",
"7-Musician / 13-Musical ensemble / 46-Choir / \n",
"6-Animation / 15-Cartoon / 851-Digimon / \n",
"3-Concert / \n",
"1-Vehicle / 88-Machine / 363-Soil / \n",
"78-Wedding / \n",
"0-Games / 2-Video game / 19-PC game / 34-Action-adventure game / 35-Minecraft / \n",
"7-Musician / 14-Guitar / 24-String instrument / 100-Electric guitar / \n",
"7-Musician / 13-Musical ensemble / 46-Choir / 689-Harmonica / \n",
"10-Animal / 86-Plant / 91-Fish / 184-Vegetable / 258-Aquarium / 1258-Goldfish / \n",
"8-Football / 110-Album / \n",
"9-Music video / \n",
"0-Games / 19-PC game / 35-Minecraft / \n",
"3-Concert / 16-Performance art / \n",
"9-Music video / \n",
"23-Mobile phone / 29-Smartphone / 37-Gadget / 42-Fishing / 72-iPhone / 189-iPad / 730-Final Fantasy VII / \n",
"1222-Fountain / \n",
"1-Vehicle / 4-Car / 70-Driving / \n",
"5-Dance / 16-Performance art / \n",
"5-Dance / 16-Performance art / 95-Talent show / \n",
"122-Weight training / 204-Gym / \n",
"111-PlayStation 3 / 243-Sonic the Hedgehog / 4019-Sonic the Hedgehog CD / \n",
"12-Food / 122-Weight training / 214-Muscle / 215-Eating / \n",
"10-Animal / 48-Pet / 132-Bird / 598-Parrot / \n",
"6-Animation / \n",
"242-Paper / 370-Origami / \n",
"0-Games / 47-Personal computer / 504-Web page / \n",
"75-Wrestling / \n",
"0-Games / 20-Trailer / 33-Weapon / 3708-Dead Space 3 / \n",
"0-Games / 2-Video game / 686-Diablo III / 872-Diablo / \n",
"14-Guitar / 24-String instrument / 1063-Ink / \n",
"271-Tank / \n",
"946-Baseball park / \n",
"3-Concert / 5-Dance / 7-Musician / 13-Musical ensemble / 46-Choir / 98-Festival / 318-Church / \n",
"6-Animation / \n",
"1-Vehicle / 4-Car / 27-Motorcycle / 114-Engine / 1036-Moped / \n",
"3-Concert / 13-Musical ensemble / \n",
"0-Games / 2-Video game / 192-Battlefield / 383-Battlefield 4 / \n",
"150-Violin / 545-Quartet (ensemble) / 1002-String quartet / \n",
"93-Comedy / \n",
"1-Vehicle / 4-Car / 40-Road / 70-Driving / 94-Dashcam / 155-Camera / 212-Highway / \n",
"9-Music video / \n",
"1-Vehicle / 4-Car / 124-Tractor / 363-Soil / 459-Plough / \n",
"6-Animation / 123-Naruto / \n",
gitextract_1ok4slma/
├── .gitignore
├── .gitmodules
├── LICENSE
├── README.md
├── eda/
│ ├── explore.ipynb
│ └── vertical.tsv
├── youtube-8m-ensemble/
│ ├── .vimrc
│ ├── CONTRIBUTING.md
│ ├── LICENSE
│ ├── README.md
│ ├── __init__.py
│ ├── all_ensemble_models/
│ │ ├── .vimrc
│ │ ├── __init__.py
│ │ ├── attention_linear_model.py
│ │ ├── attention_linmatrix_model.py
│ │ ├── attention_matrix_model.py
│ │ ├── attention_moe_matrix_model.py
│ │ ├── attention_moe_model.py
│ │ ├── attention_rectified_linear_model.py
│ │ ├── deep_combine_chain_model.py
│ │ ├── input_moe_model.py
│ │ ├── linear_regression_model.py
│ │ ├── logistic_model.py
│ │ ├── matrix_regression_model.py
│ │ ├── mean_model.py
│ │ ├── moe_model.py
│ │ └── nonunit_matrix_regression_model.py
│ ├── average_precision_calculator.py
│ ├── check_distillation.py
│ ├── check_video_id.py
│ ├── check_video_id_match.py
│ ├── cloudml-gpu-distributed.yaml
│ ├── cloudml-gpu.yaml
│ ├── data_augmentation.py
│ ├── ensemble_command.example
│ ├── ensemble_level_models.py
│ ├── ensemble_scripts/
│ │ ├── .vimrc
│ │ ├── after_submission_no1.conf
│ │ ├── after_submission_no2.conf
│ │ ├── after_submission_no3.conf
│ │ ├── after_submission_no4.conf
│ │ ├── auto-preensemble-deep_combine_chain_model.sh
│ │ ├── auto-preensemble-matrix_model.sh
│ │ ├── check-video_id.sh
│ │ ├── check-video_id_match.sh
│ │ ├── combine-tfrecords-frame-v2.sh
│ │ ├── combine-tfrecords-frame.sh
│ │ ├── combine-tfrecords-video-v2.sh
│ │ ├── combine-tfrecords-video.sh
│ │ ├── ensemble_no1.conf
│ │ ├── ensemble_no10.conf
│ │ ├── ensemble_no11.conf
│ │ ├── ensemble_no12.conf
│ │ ├── ensemble_no13.conf
│ │ ├── ensemble_no14.conf
│ │ ├── ensemble_no15.conf
│ │ ├── ensemble_no16.conf
│ │ ├── ensemble_no17.conf
│ │ ├── ensemble_no18.conf
│ │ ├── ensemble_no19.conf
│ │ ├── ensemble_no2.conf
│ │ ├── ensemble_no20.conf
│ │ ├── ensemble_no21.conf
│ │ ├── ensemble_no3.conf
│ │ ├── ensemble_no4.conf
│ │ ├── ensemble_no5.conf
│ │ ├── ensemble_no6.conf
│ │ ├── ensemble_no7.conf
│ │ ├── ensemble_no8.conf
│ │ ├── ensemble_no9.conf
│ │ ├── eval-attention_linear_model.sh
│ │ ├── eval-attention_linmatrix_model.sh
│ │ ├── eval-attention_matrix_model.sh
│ │ ├── eval-attention_moe_matrix_model.sh
│ │ ├── eval-attention_moe_model.sh
│ │ ├── eval-attention_rectified_linear_model.sh
│ │ ├── eval-deep_combine_chain_model.sh
│ │ ├── eval-input_moe_model.sh
│ │ ├── eval-linear_model.sh
│ │ ├── eval-matrix_model.sh
│ │ ├── eval-mean_model.sh
│ │ ├── eval-moe_model.sh
│ │ ├── eval-nonunit_matrix_model.sh
│ │ ├── explore-mean_model.log
│ │ ├── explore-mean_model.sh
│ │ ├── final_submission.conf
│ │ ├── infer-attention_linear_model.sh
│ │ ├── infer-attention_linmatrix_model.sh
│ │ ├── infer-attention_matrix_model.sh
│ │ ├── infer-attention_moe_matrix_model.sh
│ │ ├── infer-attention_moe_model.sh
│ │ ├── infer-attention_rectified_linear_model.sh
│ │ ├── infer-linear_model.sh
│ │ ├── infer-matrix_model.sh
│ │ ├── infer-mean_model.sh
│ │ ├── infer-moe_model.sh
│ │ ├── make-bagging-of-ensembles.sh
│ │ ├── make-virtual-groups.sh
│ │ ├── preensemble-attention_matrix_model.sh
│ │ ├── preensemble-matrix_model.sh
│ │ ├── preensemble-mean_model.sh
│ │ ├── train-attention_linear_model.sh
│ │ ├── train-attention_linmatrix_model.sh
│ │ ├── train-attention_matrix_model.sh
│ │ ├── train-attention_moe_matrix_model.sh
│ │ ├── train-attention_moe_model.sh
│ │ ├── train-attention_rectified_linear_model.sh
│ │ ├── train-deep_combine_chain_model.sh
│ │ ├── train-input_moe_model.sh
│ │ ├── train-linear_model.sh
│ │ ├── train-matrix_model.sh
│ │ ├── train-matrix_model_lr.sh
│ │ ├── train-mean_model.sh
│ │ ├── train-moe_model.sh
│ │ └── train-nonunit_matrix_model.sh
│ ├── eval.py
│ ├── eval_util.py
│ ├── feature_transform.py
│ ├── inference-combine-tfrecords-frame.py
│ ├── inference-combine-tfrecords-video.py
│ ├── inference-pre-ensemble.py
│ ├── inference.py
│ ├── losses.py
│ ├── mean_average_precision_calculator.py
│ ├── model_selection_scripts/
│ │ ├── .vimrc
│ │ ├── extend-step-mean_model.sh
│ │ ├── get_extend_candidates.py
│ │ ├── get_patterns.py
│ │ ├── get_top_k.py
│ │ └── greedy-selection-mean_model.sh
│ ├── model_utils.py
│ ├── models.py
│ ├── readers.py
│ ├── top_k_scripts/
│ │ ├── eval-attention_matrix_model.sh
│ │ ├── infer-attention_matrix_model.sh
│ │ ├── preensemble-attention_matrix_model.sh
│ │ ├── run_top_k.sh
│ │ └── train-attention_matrix_model.sh
│ ├── train.py
│ ├── training_utils/
│ │ ├── del.py
│ │ ├── sample_conf.py
│ │ ├── sample_freq.py
│ │ └── select.py
│ └── utils.py
├── youtube-8m-wangheda/
│ ├── .vimrc
│ ├── CONTRIBUTING.md
│ ├── LICENSE
│ ├── README.md
│ ├── __init__.py
│ ├── all_data_augmentation/
│ │ ├── __init__.py
│ │ ├── clipping_augmenter.py
│ │ ├── default_augmenter.py
│ │ ├── half_augmenter.py
│ │ ├── half_video_augmenter.py
│ │ └── noise_augmenter.py
│ ├── all_feature_transform/
│ │ ├── __init__.py
│ │ ├── avg_transformer.py
│ │ ├── default_transformer.py
│ │ ├── engineer_transformer.py
│ │ ├── identical_transformer.py
│ │ └── resolution_transformer.py
│ ├── all_frame_models/
│ │ ├── .vimrc
│ │ ├── __init__.py
│ │ ├── bilstm_model.py
│ │ ├── biunilstm_model.py
│ │ ├── cnn_deep_combine_chain_model.py
│ │ ├── cnn_kmax_model.py
│ │ ├── cnn_lstm_memory_model.py
│ │ ├── cnn_lstm_memory_multitask_model.py
│ │ ├── cnn_lstm_memory_normalization_model.py
│ │ ├── cnn_model.py
│ │ ├── dbof_model.py
│ │ ├── deep_cnn_deep_combine_chain_model.py
│ │ ├── deep_lstm_model.py
│ │ ├── distillchain_cnn_deep_combine_chain_model.py
│ │ ├── distillchain_lstm_attention_max_pooling_model.py
│ │ ├── distillchain_lstm_cnn_deep_combine_chain_model.py
│ │ ├── distillchain_lstm_memory_deep_combine_chain_model.py
│ │ ├── distillchain_lstm_parallel_finaloutput_model.py
│ │ ├── distillchain_multiscale_cnn_lstm_model.py
│ │ ├── frame_seg_model.py
│ │ ├── framehop_lstm_memory_deep_combine_chain_model.py
│ │ ├── framehop_lstm_memory_model.py
│ │ ├── gru_pooling_model.py
│ │ ├── gru_with_pooling_model.py
│ │ ├── layernorm_lstm_memory_model.py
│ │ ├── logistic_model.py
│ │ ├── lstm_advanced_model.py
│ │ ├── lstm_attention_lstm_model.py
│ │ ├── lstm_attention_max_pooling_model.py
│ │ ├── lstm_attention_model.py
│ │ ├── lstm_auxloss_deep_combine_chain_model.py
│ │ ├── lstm_cnn_deep_combine_chain_model.py
│ │ ├── lstm_divided_model.py
│ │ ├── lstm_look_back_model.py
│ │ ├── lstm_memory_chain_model.py
│ │ ├── lstm_memory_deep_chain_model.py
│ │ ├── lstm_memory_input_chain_model.py
│ │ ├── lstm_memory_model.py
│ │ ├── lstm_memory_multitask_model.py
│ │ ├── lstm_memory_normalization_model.py
│ │ ├── lstm_memory_parallel_chain_model.py
│ │ ├── lstm_model.py
│ │ ├── lstm_multi_attention_model.py
│ │ ├── lstm_multi_pooling_model.py
│ │ ├── lstm_parallel_finaloutput_model.py
│ │ ├── lstm_parallel_memory_model.py
│ │ ├── lstm_parallel_model.py
│ │ ├── lstm_pooling_model.py
│ │ ├── lstm_positional_attention_max_pooling_model.py
│ │ ├── lstm_with_mean_input_model.py
│ │ ├── lstm_with_pooling_model.py
│ │ ├── mm_lstm_memory_model.py
│ │ ├── multi_view_cnn_deep_combine_chain_model.py
│ │ ├── multires_lstm_memory_deep_combine_chain_model.py
│ │ ├── multiscale_cnn_lstm_model.py
│ │ ├── positional_cnn_deep_combine_chain_model.py
│ │ ├── progressive_attention_lstm_model.py
│ │ └── wide_and_deep_model.py
│ ├── all_video_models/
│ │ ├── .vimrc
│ │ ├── __init__.py
│ │ ├── chain_main_relu_moe_model.py
│ │ ├── chain_moe_model.py
│ │ ├── chain_support_relu_moe_model.py
│ │ ├── deep_chain_model.py
│ │ ├── deep_combine_chain_model.py
│ │ ├── distillchain_deep_combine_chain_model.py
│ │ ├── hidden_chain_model.py
│ │ ├── hidden_combine_chain_model.py
│ │ ├── logistic_model.py
│ │ ├── mlp_moe_model.py
│ │ ├── moe_model.py
│ │ ├── multitask_divergence_deep_combine_chain_model.py
│ │ ├── multitask_divergence_moe_model.py
│ │ ├── multitask_moe_model.py
│ │ ├── shortcut_chain_support_relu_moe_model.py
│ │ └── stage2_logistic_model.py
│ ├── average_precision_calculator.py
│ ├── bagging_scripts/
│ │ ├── cnn-deep-combine-chain-bagging.sh
│ │ ├── distillation-video-dcc-bagging.sh
│ │ ├── lstmattention8max-bagging.sh
│ │ ├── lstmparalleloutput-bagging.sh
│ │ └── video-deep-combine-chain-bagging.sh
│ ├── boosting_scripts/
│ │ ├── cnn-deep-combine-chain-boosting.sh
│ │ ├── distillation-cnn-dcc-boosting.sh
│ │ ├── distillation-lstmcnn-dcc-boosting.sh
│ │ ├── distillation-lstmparalleloutput-boosting.sh
│ │ ├── distillation-multilstm-dcc-boosting.sh
│ │ ├── distillation-multiscale-cnn-lstm-boosting.sh
│ │ ├── distillation-positional-lstmattention8max-boosting.sh
│ │ ├── distillation-video-dcc-boosting.sh
│ │ ├── lstmattention8max-boosting-weightclip.sh
│ │ ├── lstmparalleloutput-boosting-weightclip.sh
│ │ ├── video-deep-combine-chain-boosting-discardhopeless.sh
│ │ ├── video-deep-combine-chain-boosting-weightclip.sh
│ │ └── video-deep-combine-chain-boosting.sh
│ ├── cascade_scripts/
│ │ ├── distillchain-v2-hybridchain.sh
│ │ ├── distillchain-v2-hybridchain2.sh
│ │ └── distillchain-v2-videochain.sh
│ ├── cloudml-gpu-distributed.yaml
│ ├── cloudml-gpu.yaml
│ ├── data_augmentation.py
│ ├── data_augmentation_scripts/
│ │ ├── eval-chaining-video.sh
│ │ ├── run-chaining-cnn.sh
│ │ ├── run-chaining-lstm.sh
│ │ ├── run-chaining-video.sh
│ │ ├── run-multiple-attention-pooling-positional-embedding.sh
│ │ ├── run-multiscale-cnn-lstm-model.sh
│ │ └── run-parallel-lstm-memory.sh
│ ├── eval.py
│ ├── eval.sh
│ ├── eval_scripts/
│ │ ├── eval-att-lstm.sh
│ │ ├── eval-att.sh
│ │ ├── eval-bi-uni-lstm.sh
│ │ ├── eval-chain-model-relu.sh
│ │ ├── eval-chain-model-suprelu.sh
│ │ ├── eval-chain-moe-0.4.sh
│ │ ├── eval-chain-moe-freq.sh
│ │ ├── eval-chain-moe-suprelu-vert+freq.sh
│ │ ├── eval-chain-moe.sh
│ │ ├── eval-cnn-deep-combine-chain.sh
│ │ ├── eval-cnn-model.sh
│ │ ├── eval-dbof.sh
│ │ ├── eval-deep-cnn-deep-combine-chain.sh
│ │ ├── eval-distill-video-dcc-noise-scene1.sh
│ │ ├── eval-distill-video-dcc-noise-scene2.sh
│ │ ├── eval-distillchain-cnn-dcc.sh
│ │ ├── eval-distillchain-lstmcnn.sh
│ │ ├── eval-distillchain-lstmparalleloutput.sh
│ │ ├── eval-distillchain-multilstm.sh
│ │ ├── eval-distillchain-v2-boostinglstmparalleloutput.sh
│ │ ├── eval-distillchain-v2-lstmattention8max.sh
│ │ ├── eval-distillchain-v2-lstmcnn.sh
│ │ ├── eval-distillchain-v2-lstmparalleloutput.sh
│ │ ├── eval-distillchain-v2-multilstm.sh
│ │ ├── eval-distillchain-v2-multiscale-cnnlstm.sh
│ │ ├── eval-distillchain-v2-video-dcc.sh
│ │ ├── eval-distillchain-video-dcc.sh
│ │ ├── eval-frame-seg.sh
│ │ ├── eval-framehop-lstmmem.sh
│ │ ├── eval-layer-chain-moe8-freq.sh
│ │ ├── eval-layer-moe-vert.sh
│ │ ├── eval-lstm-attention-8max.sh
│ │ ├── eval-lstm-cnn-deep-combine-chain.sh
│ │ ├── eval-lstm-look-back.sh
│ │ ├── eval-lstm-positional-attention-8max.sh
│ │ ├── eval-lstmmem-augmenter.sh
│ │ ├── eval-lstmmem-chain-freq.sh
│ │ ├── eval-lstmmem-chain.sh
│ │ ├── eval-lstmmem-cnnlstm.sh
│ │ ├── eval-lstmmem-deep-chain.sh
│ │ ├── eval-lstmmem-deep-combine-chain-length.sh
│ │ ├── eval-lstmmem-dropout.sh
│ │ ├── eval-lstmmem-feature.sh
│ │ ├── eval-lstmmem-input-chain.sh
│ │ ├── eval-lstmmem-input-noise.sh
│ │ ├── eval-lstmmem-l2norm.sh
│ │ ├── eval-lstmmem-layernorm.sh
│ │ ├── eval-lstmmem-lowres.sh
│ │ ├── eval-lstmmem-no-transform.sh
│ │ ├── eval-lstmmem-noise.sh
│ │ ├── eval-lstmmem-parallel.sh
│ │ ├── eval-lstmmem-shortcut-chain-freq.sh
│ │ ├── eval-lstmmem2048.sh
│ │ ├── eval-lstmmemory-audio.sh
│ │ ├── eval-lstmmemory-layer1.sh
│ │ ├── eval-lstmmemory.sh
│ │ ├── eval-lstmoutput-parallel.sh
│ │ ├── eval-mem.sh
│ │ ├── eval-mm-lstm.sh
│ │ ├── eval-moe-baseline.sh
│ │ ├── eval-moe-batchagreement1.sh
│ │ ├── eval-moe-batchagreement2.sh
│ │ ├── eval-moe-batchagreement3.sh
│ │ ├── eval-moe-model.sh
│ │ ├── eval-moe-topk-batchagreement1.sh
│ │ ├── eval-moe-topk-batchagreement2.sh
│ │ ├── eval-moe-topk-batchagreement3.sh
│ │ ├── eval-multi-lstmmem-deep-chain.sh
│ │ ├── eval-multi-view-cnn-deep-combine-chain.sh
│ │ ├── eval-multires-lstm-deep-combine-chain.sh
│ │ ├── eval-multitask-ce.sh
│ │ ├── eval-multitask.sh
│ │ ├── eval-positional-cnn-dcc.sh
│ │ ├── eval-stage2-logistic.sh
│ │ ├── eval-stage2-moe.sh
│ │ ├── eval-video-deep-chain.sh
│ │ ├── eval-video-deep-combine-addnoise.sh
│ │ ├── eval-video-deep-combine-chain-dropout.sh
│ │ ├── eval-video-deep-combine-chain-labelsmoothing.sh
│ │ ├── eval-video-deep-combine-chain-noise.sh
│ │ ├── eval-video-deep-combine-chain.sh
│ │ ├── eval-video-distillchain-video-dcc.sh
│ │ ├── eval-video-divergence-chain-model.sh
│ │ ├── eval-video-divergence-moe-model.sh
│ │ ├── eval-video-hidden-chain.sh
│ │ ├── eval-video-hidden-combine-chain.sh
│ │ ├── eval-video-logistic.sh
│ │ ├── eval-video-moe.sh
│ │ ├── eval-video-pairwise.sh
│ │ └── eval-video-verydeep-combine-chain.sh
│ ├── eval_util.py
│ ├── feature_transform.py
│ ├── frame_level_models.py
│ ├── infer_scripts/
│ │ ├── infer-attentionlstm_moe4.sh
│ │ ├── infer-biunilstm1024_moe8.sh
│ │ ├── infer-cnn_deep_combine_chain.sh
│ │ ├── infer-cnn_lstmmemory1024_moe8.sh
│ │ ├── infer-cnn_model.sh
│ │ ├── infer-dbof.sh
│ │ ├── infer-deep_cnn_deep_combine.sh
│ │ ├── infer-deeplstm1024_layer6_moe4.sh
│ │ ├── infer-distill_video_dcc.sh
│ │ ├── infer-distillation-cnn-dcc.sh
│ │ ├── infer-distillation-lstmattention8max.sh
│ │ ├── infer-distillation-lstmgate.sh
│ │ ├── infer-distillation-video-dcc.sh
│ │ ├── infer-distillation.sh
│ │ ├── infer-distillchain-cnn-dcc.sh
│ │ ├── infer-distillchain-lstmcnn.sh
│ │ ├── infer-distillchain-lstmparalleloutput.sh
│ │ ├── infer-distillchain-v2-boost-lstmparalleloutput.sh
│ │ ├── infer-distillchain-v2-lstmattention8max.sh
│ │ ├── infer-distillchain-v2-lstmcnn.sh
│ │ ├── infer-distillchain-v2-lstmparalleloutput.sh
│ │ ├── infer-distillchain-v2-multilstm.sh
│ │ ├── infer-distillchain-v2-multiscal-cnnlstm.sh
│ │ ├── infer-distillchain-v2-video-dcc.sh
│ │ ├── infer-frame_seg.sh
│ │ ├── infer-framehop_lstm.sh
│ │ ├── infer-lstm_attention8_max.sh
│ │ ├── infer-lstm_cnn_deep_combine_chain.sh
│ │ ├── infer-lstmattlstm1024_moe8.sh
│ │ ├── infer-lstmmemory-audio.sh
│ │ ├── infer-lstmmemory-layer1.sh
│ │ ├── infer-lstmmemory1024_deep_combine_chain_add_length.sh
│ │ ├── infer-lstmmemory1024_moe8.sh
│ │ ├── infer-lstmmemory2048_moe4.sh
│ │ ├── infer-lstmparallelmemory1024_moe8.sh
│ │ ├── infer-lstmparalleloutput1024_moe8.sh
│ │ ├── infer-model_input.sh
│ │ ├── infer-multilstmmemory1024_moe4_deep_chain.sh
│ │ ├── infer-multires_lstm_deep_combine_chain.sh
│ │ ├── infer-positional-lstmattention8max.sh
│ │ ├── infer-video-distillchain-video-dcc.sh
│ │ ├── infer-video_group_moe4_noise0.2_layer4_elu.sh
│ │ ├── infer-video_logistic.sh
│ │ ├── infer-video_moe16_model.sh
│ │ └── infer-video_very_deep_combine_chain.sh
│ ├── inference-layer.py
│ ├── inference-pre-ensemble-get-input.py
│ ├── inference-pre-ensemble-with-predictions.py
│ ├── inference-pre-ensemble.py
│ ├── inference-sample-error-analysis.py
│ ├── inference-sample-error.py
│ ├── inference-stage1.py
│ ├── inference.py
│ ├── losses.py
│ ├── mean_average_precision_calculator.py
│ ├── model_utils.py
│ ├── models.py
│ ├── readers.py
│ ├── train-with-predictions.py
│ ├── train-with-rebuild.py
│ ├── train.py
│ ├── training_scripts/
│ │ ├── run-cascade-75-chaining-cnn.sh
│ │ ├── run-cascade-75-chaining-lstm-cnn.sh
│ │ ├── run-cascade-75-chaining-lstm.sh
│ │ ├── run-cascade-75-chaining-parallel-lstm.sh
│ │ ├── run-cascade-75-chaining-video.sh
│ │ ├── run-cascade-75-multiple-attention-pooling.sh
│ │ ├── run-cascade-76-chaining-cnn.sh
│ │ ├── run-cascade-76-chaining-lstm-cnn.sh
│ │ ├── run-cascade-76-chaining-lstm.sh
│ │ ├── run-cascade-76-chaining-video.sh
│ │ ├── run-cascade-76-multiple-attention-pooling.sh
│ │ ├── run-cascade-76-multiscale-cnn-lstm.sh
│ │ ├── run-cascade-76-parallel-lstm-boosting.sh
│ │ ├── run-cascade-76-parallel-lstm.sh
│ │ ├── run-chaining-cnn.sh
│ │ ├── run-chaining-deep-cnn.sh
│ │ ├── run-chaining-lstm-cnn.sh
│ │ ├── run-chaining-lstm.sh
│ │ ├── run-chaining-multi-resolution-lstm.sh
│ │ ├── run-chaining-shared-lstm.sh
│ │ ├── run-chaining-video.sh
│ │ ├── run-cnn-lstm.sh
│ │ ├── run-cnn-model.sh
│ │ ├── run-lstm-memory-cell1024.sh
│ │ ├── run-lstm-memory-cell2048.sh
│ │ ├── run-multiple-attention-pooling-positional-embedding.sh
│ │ ├── run-multiscale-cnn-lstm-model.sh
│ │ ├── run-parallel-lstm-memory.sh
│ │ ├── run-parallel-lstm-output.sh
│ │ └── run-temporal-pooling-lstm.sh
│ ├── training_utils/
│ │ ├── del.py
│ │ ├── human_readable_error_analysis.py
│ │ ├── reweight_sample_freq.py
│ │ ├── sample_freq.py
│ │ ├── select.py
│ │ └── video_original_boosting_error_analysis.py
│ ├── utils.py
│ └── video_level_models.py
└── youtube-8m-zhangteng/
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── YM_framemean.py
├── YM_labels_matrix.py
├── YM_labels_model.py
├── YM_labels_vocab.py
├── YM_readframefeature.py
├── __init__.py
├── average_precision_calculator.py
├── cloudml-gpu-distributed.yaml
├── cloudml-gpu.yaml
├── eval.py
├── eval_autoencoder.py
├── eval_distill.py
├── eval_embedding.py
├── eval_scripts/
│ ├── eval-distillchain_cnndcc_layer2moe4.sh
│ ├── eval-distillchain_lstm_extend_moe8.sh
│ ├── eval-distillchain_lstm_gate_moe8.sh
│ ├── eval-distillchain_lstm_gate_moe8_v2.sh
│ ├── eval-distillchain_lstm_glu2_moe8_v2.sh
│ ├── eval-distillchain_lstm_moe8.sh
│ ├── eval-distillchain_lstm_moe8_v2.sh
│ ├── eval-distillchain_lstm_multiscale2layer_moe8.sh
│ ├── eval-distillchain_lstm_multiscale4layer_moe8.sh
│ ├── eval-distillchain_video_norm_moe8_local.sh
│ ├── eval-distillsplit_lstm_gate_moe8.sh
│ ├── eval-lstm2_attention8_max.sh
│ ├── eval-lstm_attention8_max.sh
│ ├── eval-lstm_gate_multiscale4_moe4.sh
│ ├── eval-lstm_multiscale4_moe4.sh
│ ├── eval-lstm_random_moe8.sh
│ ├── eval-lstm_shortlayers_moe8.sh
│ ├── eval-lstmbiglu_1024_moe8.sh
│ ├── eval-lstmgate1024_moe8.sh
│ ├── eval-lstmglu2_1024_moe8.sh
│ ├── eval-video_knowledge_combine_chain.sh
│ ├── eval-video_notzero_combine_chain.sh
│ ├── eval-video_relabel_combine_chain.sh
│ └── eval-video_softmax_combine_chain.sh
├── eval_util.py
├── frame_level_models.py
├── infer_scripts/
│ ├── infer-distillchain_cnndcc_layer2moe4.sh
│ ├── infer-distillchain_cnndcc_layer2moe4_ensemble.sh
│ ├── infer-distillchain_lstm_extend_moe8.sh
│ ├── infer-distillchain_lstm_gate_moe8.sh
│ ├── infer-distillchain_lstm_gate_moe8_v2.sh
│ ├── infer-distillchain_lstm_glu2_moe8_v2.sh
│ ├── infer-distillchain_lstm_moe8.sh
│ ├── infer-distillchain_lstm_moe8_v2.sh
│ ├── infer-distillchain_lstm_multiscale2layer_moe8.sh
│ ├── infer-distillchain_lstm_multiscale4layer_moe8.sh
│ ├── infer-distillchain_video_norm_moe8.sh
│ ├── infer-distillchain_video_norm_moe8_local.sh
│ ├── infer-distillsplit_lstm_gate_moe8.sh
│ ├── infer-lstm2_attention8_max.sh
│ ├── infer-lstm_attention8_max.sh
│ ├── infer-lstm_attention_max_mean.sh
│ ├── infer-lstm_gate_multiscale4_moe4.sh
│ ├── infer-lstm_multiscale4_moe4.sh
│ ├── infer-lstm_random_mean_moe8.sh
│ ├── infer-lstm_shortlayers_moe8.sh
│ ├── infer-lstmbiglu_1024_moe8.sh
│ ├── infer-lstmgate1024_moe8.sh
│ ├── infer-lstmglu2_1024_moe8.sh
│ ├── infer-video_notzero_combine_chain.sh
│ └── infer-video_relabel_combine_chain.sh
├── inference-pre-ensemble-distill.py
├── inference-pre-ensemble.py
├── inference.py
├── inference_autoencoder.py
├── inference_embedding.py
├── inference_test.py
├── inference_with_rebuild.py
├── labels_autoencoder.py
├── labels_embedding.py
├── labels_rbm.py
├── losses.py
├── losses_embedding.py
├── mean_average_precision_calculator.py
├── model_utils.py
├── models.py
├── readers.py
├── rnn_residual.py
├── train-with-rebuild.py
├── train.py
├── train_autoencoder.py
├── train_embedding.py
├── train_ensemble.py
├── train_scripts/
│ ├── run-attention-pooling-lstm-a.sh
│ ├── run-attention-pooling-lstm-s.sh
│ ├── run-attention-pooling-lstm2lstm.sh
│ ├── run-attention-pooling.sh
│ ├── run-bilstm-a.sh
│ ├── run-cascade-76-lstm-a.sh
│ ├── run-cascade-76-lstm-s.sh
│ ├── run-cascade-76-lstm.sh
│ ├── run-cascade-attention-pooling.sh
│ ├── run-cascade-chaining-cnn-layer2.sh
│ ├── run-cascade-chaining-video-normalize.sh
│ ├── run-cascade-lstm-s-split.sh
│ ├── run-cascade-lstm-s.sh
│ ├── run-cascade-lstm.sh
│ ├── run-cascade-multiscale-cnn-lstm-laery4.sh
│ ├── run-cascade-multiscale-cnn-lstm-layer2.sh
│ ├── run-chaining-video-add-confident.sh
│ ├── run-chaining-video-infrequent-softmax.sh
│ ├── run-chaining-video-normal.sh
│ ├── run-chaining-video-vertical.sh
│ ├── run-lstm-random-augmentation.sh
│ ├── run-lstm-s.sh
│ ├── run-multiscale-cnn-lstm.sh
│ └── run-temporal-segment-lstm.sh
├── training_utils/
│ ├── del.py
│ └── select.py
├── utils.py
├── video_level_models.py
└── writers.py
SYMBOL INDEX (1145 symbols across 171 files)
FILE: youtube-8m-ensemble/all_ensemble_models/attention_linear_model.py
class AttentionLinearModel (line 9) | class AttentionLinearModel(models.BaseModel):
method create_model (line 11) | def create_model(self,
FILE: youtube-8m-ensemble/all_ensemble_models/attention_linmatrix_model.py
class AttentionLinmatrixModel (line 9) | class AttentionLinmatrixModel(models.BaseModel):
method create_model (line 11) | def create_model(self,
FILE: youtube-8m-ensemble/all_ensemble_models/attention_matrix_model.py
class AttentionMatrixModel (line 9) | class AttentionMatrixModel(models.BaseModel):
method create_model (line 11) | def create_model(self,
FILE: youtube-8m-ensemble/all_ensemble_models/attention_moe_matrix_model.py
class AttentionMoeMatrixModel (line 9) | class AttentionMoeMatrixModel(models.BaseModel):
method create_model (line 11) | def create_model(self,
method relu (line 68) | def relu(self, model_input, relu_cells,
FILE: youtube-8m-ensemble/all_ensemble_models/attention_moe_model.py
class AttentionMoeModel (line 9) | class AttentionMoeModel(models.BaseModel):
method create_model (line 11) | def create_model(self,
method relu (line 44) | def relu(self, model_input, relu_cells,
FILE: youtube-8m-ensemble/all_ensemble_models/attention_rectified_linear_model.py
class AttentionRectifiedLinearModel (line 9) | class AttentionRectifiedLinearModel(models.BaseModel):
method create_model (line 11) | def create_model(self,
FILE: youtube-8m-ensemble/all_ensemble_models/deep_combine_chain_model.py
class DeepCombineChainModel (line 9) | class DeepCombineChainModel(models.BaseModel):
method create_model (line 11) | def create_model(self,
method sub_moe (line 72) | def sub_moe(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-ensemble/all_ensemble_models/input_moe_model.py
class InputMoeModel (line 9) | class InputMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self,
FILE: youtube-8m-ensemble/all_ensemble_models/linear_regression_model.py
class LinearRegressionModel (line 9) | class LinearRegressionModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, origi...
FILE: youtube-8m-ensemble/all_ensemble_models/logistic_model.py
class LogisticModel (line 9) | class LogisticModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, origi...
FILE: youtube-8m-ensemble/all_ensemble_models/matrix_regression_model.py
class MatrixRegressionModel (line 9) | class MatrixRegressionModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, origi...
FILE: youtube-8m-ensemble/all_ensemble_models/mean_model.py
class MeanModel (line 9) | class MeanModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, **unused_params):
FILE: youtube-8m-ensemble/all_ensemble_models/moe_model.py
class MoeModel (line 9) | class MoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self,
FILE: youtube-8m-ensemble/all_ensemble_models/nonunit_matrix_regression_model.py
class NonunitMatrixRegressionModel (line 9) | class NonunitMatrixRegressionModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, origi...
FILE: youtube-8m-ensemble/average_precision_calculator.py
class AveragePrecisionCalculator (line 61) | class AveragePrecisionCalculator(object):
method __init__ (line 64) | def __init__(self, top_n=None):
method heap_size (line 84) | def heap_size(self):
method num_accumulated_positives (line 89) | def num_accumulated_positives(self):
method accumulate (line 93) | def accumulate(self, predictions, actuals, num_positives=None):
method clear (line 134) | def clear(self):
method peek_ap_at_n (line 139) | def peek_ap_at_n(self):
method ap (line 158) | def ap(predictions, actuals):
method ap_at_n (line 180) | def ap_at_n(predictions, actuals, n=20, total_num_positives=None):
method _shuffle (line 248) | def _shuffle(predictions, actuals):
method _zero_one_normalize (line 256) | def _zero_one_normalize(predictions, epsilon=1e-7):
FILE: youtube-8m-ensemble/check_distillation.py
function find_class_by_name (line 48) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 54) | def get_input_evaluation_tensors(reader,
function build_graph (line 76) | def build_graph(all_readers,
function check_loop (line 121) | def check_loop(model_input, video_id_equal, input_distance, label_distan...
function check_video_id (line 168) | def check_video_id():
function main (line 212) | def main(unused_argv):
FILE: youtube-8m-ensemble/check_video_id.py
function find_class_by_name (line 61) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 67) | def get_input_evaluation_tensors(reader,
function build_graph (line 89) | def build_graph(all_readers,
function check_loop (line 122) | def check_loop(video_id_equal, input_distance, all_patterns):
function check_video_id (line 166) | def check_video_id():
function main (line 208) | def main(unused_argv):
FILE: youtube-8m-ensemble/check_video_id_match.py
function find_class_by_name (line 61) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 67) | def get_input_evaluation_tensors(reader,
function build_graph (line 89) | def build_graph(all_readers,
function check_loop (line 124) | def check_loop(video_id_mismatch, input_distance, actual_batch_size, all...
function check_video_id (line 184) | def check_video_id():
function main (line 229) | def main(unused_argv):
FILE: youtube-8m-ensemble/eval.py
function find_class_by_name (line 61) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 67) | def get_input_evaluation_tensors(reader,
function build_graph (line 89) | def build_graph(all_readers,
function evaluation_loop (line 158) | def evaluation_loop(video_id_batch, prediction_batch, label_batch, loss,
function evaluate (line 258) | def evaluate():
function main (line 318) | def main(unused_argv):
FILE: youtube-8m-ensemble/eval_util.py
function flatten (line 24) | def flatten(l):
function calculate_hit_at_one (line 28) | def calculate_hit_at_one(predictions, actuals):
function calculate_recall_at_n (line 45) | def calculate_recall_at_n(predictions, actuals, n):
function calculate_precision_at_equal_recall_rate (line 74) | def calculate_precision_at_equal_recall_rate(predictions, actuals):
function calculate_gap (line 102) | def calculate_gap(predictions, actuals, top_k=20):
function top_k_by_class (line 123) | def top_k_by_class(predictions, labels, k=20):
function top_k_triplets (line 159) | def top_k_triplets(predictions, labels, k=20):
class EvaluationMetrics (line 167) | class EvaluationMetrics(object):
method __init__ (line 170) | def __init__(self, num_class, top_k):
method accumulate (line 189) | def accumulate(self, predictions, labels, loss):
method get (line 223) | def get(self):
method clear (line 247) | def clear(self):
FILE: youtube-8m-ensemble/inference-combine-tfrecords-frame.py
function find_class_by_name (line 59) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 64) | def get_input_data_tensors(reader,
function build_graph (line 84) | def build_graph(input_reader, input_data_pattern,
function inference_loop (line 118) | def inference_loop(video_ids_batch, labels_batch, rgbs_batch, audios_bat...
function write_to_record (line 236) | def write_to_record(video_ids, video_labels, video_rgbs, video_audios, v...
function get_output_feature (line 250) | def get_output_feature(video_id, video_label, video_rgb, video_audio, vi...
function main (line 265) | def main(unused_argv):
FILE: youtube-8m-ensemble/inference-combine-tfrecords-video.py
function find_class_by_name (line 56) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 61) | def get_input_data_tensors(reader,
function build_graph (line 81) | def build_graph(input_reader, input_data_pattern,
function inference_loop (line 118) | def inference_loop(video_ids_batch, labels_batch, inputs_batch, predicti...
function write_to_record (line 219) | def write_to_record(video_ids, video_labels, video_inputs, video_predict...
function get_output_feature (line 231) | def get_output_feature(video_id, video_label, video_input, video_predict...
function main (line 262) | def main(unused_argv):
FILE: youtube-8m-ensemble/inference-pre-ensemble.py
function find_class_by_name (line 64) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 69) | def get_input_data_tensors(reader,
function build_graph (line 89) | def build_graph(all_readers,
function inference_loop (line 155) | def inference_loop(video_id_batch, prediction_batch,
function write_to_record (line 255) | def write_to_record(video_ids, video_labels, video_features, filenum, nu...
function get_output_feature (line 265) | def get_output_feature(video_id, video_label, video_feature, feature_nam...
function main (line 274) | def main(unused_argv):
FILE: youtube-8m-ensemble/inference.py
function format_lines (line 58) | def format_lines(video_ids, predictions, top_k):
function find_class_by_name (line 69) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 75) | def get_input_data_tensors(reader,
function build_graph (line 96) | def build_graph(all_readers,
function inference_loop (line 155) | def inference_loop(video_id_batch, prediction_batch, label_batch,
function inference (line 209) | def inference():
function main (line 257) | def main(unused_argv):
FILE: youtube-8m-ensemble/losses.py
function smoothing (line 46) | def smoothing(labels):
class BaseLoss (line 56) | class BaseLoss(object):
method calculate_loss (line 59) | def calculate_loss(self, unused_predictions, unused_labels, **unused_p...
class CrossEntropyLoss (line 76) | class CrossEntropyLoss(BaseLoss):
method calculate_loss (line 80) | def calculate_loss(self, predictions, labels, weights=None, **unused_p...
class HingeLoss (line 98) | class HingeLoss(BaseLoss):
method calculate_loss (line 106) | def calculate_loss(self, predictions, labels, b=1.0, **unused_params):
class SoftmaxLoss (line 116) | class SoftmaxLoss(BaseLoss):
method calculate_loss (line 128) | def calculate_loss(self, predictions, labels, **unused_params):
class MultiTaskLoss (line 142) | class MultiTaskLoss(BaseLoss):
method calculate_loss (line 145) | def calculate_loss(self, unused_predictions, unused_labels, **unused_p...
method get_support (line 148) | def get_support(self, labels, support_type=None):
class MultiTaskCrossEntropyLoss (line 185) | class MultiTaskCrossEntropyLoss(MultiTaskLoss):
method calculate_loss (line 188) | def calculate_loss(self, predictions, support_predictions, labels, **u...
FILE: youtube-8m-ensemble/mean_average_precision_calculator.py
class MeanAveragePrecisionCalculator (line 44) | class MeanAveragePrecisionCalculator(object):
method __init__ (line 48) | def __init__(self, num_class):
method accumulate (line 71) | def accumulate(self, predictions, actuals, num_positives=None):
method clear (line 95) | def clear(self):
method is_empty (line 99) | def is_empty(self):
method peek_map_at_n (line 103) | def peek_map_at_n(self):
FILE: youtube-8m-ensemble/model_utils.py
function SampleRandomSequence (line 23) | def SampleRandomSequence(model_input, num_frames, num_samples):
function SampleRandomFrames (line 51) | def SampleRandomFrames(model_input, num_frames, num_samples):
function FramePooling (line 72) | def FramePooling(frames, method, **unused_params):
FILE: youtube-8m-ensemble/models.py
class BaseModel (line 17) | class BaseModel(object):
method create_model (line 20) | def create_model(self, unused_model_input, **unused_params):
FILE: youtube-8m-ensemble/readers.py
function resize_axis (line 23) | def resize_axis(tensor, axis, new_size, fill_value=0):
class BaseReader (line 44) | class BaseReader(object):
method prepare_reader (line 47) | def prepare_reader(self, unused_filename_queue):
class EnsembleReader (line 52) | class EnsembleReader(BaseReader):
method __init__ (line 54) | def __init__(self,
method prepare_reader (line 67) | def prepare_reader(self, filename_queue, batch_size=1024):
class EnsembleFrameReader (line 93) | class EnsembleFrameReader(BaseReader):
method __init__ (line 95) | def __init__(self,
method get_video_matrix (line 110) | def get_video_matrix(self,
method prepare_reader (line 120) | def prepare_reader(self, filename_queue):
FILE: youtube-8m-ensemble/train.py
function get_input_data_tensors (line 113) | def get_input_data_tensors(reader,
function find_class_by_name (line 137) | def find_class_by_name(name, modules):
function get_video_weights_array (line 142) | def get_video_weights_array():
function optional_assign_weights (line 148) | def optional_assign_weights(sess, weights_input, weights_assignment):
function get_video_weights (line 156) | def get_video_weights(video_id_batch):
function build_graph (line 175) | def build_graph(all_readers,
class Trainer (line 353) | class Trainer(object):
method __init__ (line 356) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 375) | def run(self, start_new_model=False):
method start_server_if_distributed (line 488) | def start_server_if_distributed(self):
method remove_training_directory (line 505) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 518) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 538) | def recover_model(self, meta_filename):
method build_model (line 543) | def build_model(self):
class ParameterServer (line 591) | class ParameterServer(object):
method __init__ (line 594) | def __init__(self, cluster, task):
method run (line 606) | def run(self):
function start_server (line 615) | def start_server(cluster, task):
function task_as_string (line 638) | def task_as_string(task):
function main (line 641) | def main(unused_argv):
FILE: youtube-8m-ensemble/utils.py
function Dequantize (line 23) | def Dequantize(feat_vector, max_quantized_value=2, min_quantized_value=-2):
function MakeSummary (line 41) | def MakeSummary(name, value):
function AddGlobalStepSummary (line 50) | def AddGlobalStepSummary(summary_writer,
function AddEpochSummary (line 94) | def AddEpochSummary(summary_writer,
function GetListOfFeatureNamesAndSizes (line 140) | def GetListOfFeatureNamesAndSizes(feature_names, feature_sizes):
function clip_gradient_norms (line 164) | def clip_gradient_norms(gradients_to_variables, max_norm):
FILE: youtube-8m-wangheda/all_data_augmentation/clipping_augmenter.py
class ClippingAugmenter (line 6) | class ClippingAugmenter:
method augment (line 8) | def augment(self, model_input_raw, num_frames, labels_batch, **unused_...
FILE: youtube-8m-wangheda/all_data_augmentation/default_augmenter.py
class DefaultAugmenter (line 6) | class DefaultAugmenter:
method augment (line 8) | def augment(self, model_input_raw, num_frames, labels_batch, **unused_...
FILE: youtube-8m-wangheda/all_data_augmentation/half_augmenter.py
class HalfAugmenter (line 6) | class HalfAugmenter:
method augment (line 8) | def augment(self, model_input_raw, num_frames, labels_batch, **unused_...
FILE: youtube-8m-wangheda/all_data_augmentation/half_video_augmenter.py
class HalfVideoAugmenter (line 6) | class HalfVideoAugmenter:
method augment (line 8) | def augment(self, model_input_raw, num_frames, labels_batch, **unused_...
method frame_augment (line 19) | def frame_augment(self, model_input_raw, num_frames, labels_batch, **u...
FILE: youtube-8m-wangheda/all_data_augmentation/noise_augmenter.py
class NoiseAugmenter (line 6) | class NoiseAugmenter:
method augment (line 8) | def augment(self, model_input_raw, num_frames, labels_batch, **unused_...
FILE: youtube-8m-wangheda/all_feature_transform/avg_transformer.py
class AvgTransformer (line 3) | class AvgTransformer:
method transform (line 4) | def transform(self, model_input_raw, num_frames, **unused_params):
FILE: youtube-8m-wangheda/all_feature_transform/default_transformer.py
class DefaultTransformer (line 4) | class DefaultTransformer:
method transform (line 5) | def transform(self, model_input_raw, num_frames, **unused_params):
FILE: youtube-8m-wangheda/all_feature_transform/engineer_transformer.py
class EngineerTransformer (line 7) | class EngineerTransformer:
method transform (line 9) | def transform(self, model_input_raw, num_frames, **unused_params):
method mask (line 26) | def mask(self, model_input_raw, num_frames):
method avg (line 41) | def avg(self, model_input_raw, num_frames, mask):
method std (line 51) | def std(self, model_input_raw, num_frames, mask):
method diff (line 56) | def diff(self, model_input_raw, num_frames, mask):
FILE: youtube-8m-wangheda/all_feature_transform/identical_transformer.py
class IdenticalTransformer (line 4) | class IdenticalTransformer:
method transform (line 5) | def transform(self, model_input_raw, num_frames, **unused_params):
FILE: youtube-8m-wangheda/all_feature_transform/resolution_transformer.py
class ResolutionTransformer (line 6) | class ResolutionTransformer:
method resolution (line 7) | def resolution(self, model_input_raw, num_frames):
method transform (line 25) | def transform(self, model_input_raw, num_frames, **unused_params):
FILE: youtube-8m-wangheda/all_frame_models/bilstm_model.py
class BiLstmModel (line 13) | class BiLstmModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/biunilstm_model.py
class BiUniLstmModel (line 13) | class BiUniLstmModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/cnn_deep_combine_chain_model.py
class CnnDeepCombineChainModel (line 10) | class CnnDeepCombineChainModel(models.BaseModel):
method cnn (line 13) | def cnn(self,
method create_model (line 42) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 90) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 121) | def get_mask(self, max_frames, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/cnn_kmax_model.py
class CnnKmaxModel (line 13) | class CnnKmaxModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
FILE: youtube-8m-wangheda/all_frame_models/cnn_lstm_memory_model.py
class CnnLstmMemoryModel (line 13) | class CnnLstmMemoryModel(models.BaseModel):
method cnn (line 15) | def cnn(self,
method create_model (line 42) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/cnn_lstm_memory_multitask_model.py
class CnnLstmMemoryMultiTaskModel (line 13) | class CnnLstmMemoryMultiTaskModel(models.BaseModel):
method cnn (line 15) | def cnn(self,
method create_model (line 42) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
method get_mask (line 105) | def get_mask(self, max_frames, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/cnn_lstm_memory_normalization_model.py
class CnnLstmMemoryNormalizationModel (line 13) | class CnnLstmMemoryNormalizationModel(models.BaseModel):
method cnn (line 15) | def cnn(self,
method create_model (line 43) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
method layer_normalize (line 85) | def layer_normalize(self, input_raw, epsilon=1e-8):
method l2_normalize (line 93) | def l2_normalize(self, input_raw, epsilon=1e-8):
method identical (line 98) | def identical(self, input_raw, epsilon=1e-8):
FILE: youtube-8m-wangheda/all_frame_models/cnn_model.py
class CnnModel (line 10) | class CnnModel(models.BaseModel):
method cnn (line 13) | def cnn(self,
method create_model (line 42) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 54) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_frame_models/dbof_model.py
class DbofModel (line 13) | class DbofModel(models.BaseModel):
method create_model (line 36) | def create_model(self,
FILE: youtube-8m-wangheda/all_frame_models/deep_cnn_deep_combine_chain_model.py
class DeepCnnDeepCombineChainModel (line 10) | class DeepCnnDeepCombineChainModel(models.BaseModel):
method cnn (line 13) | def cnn(self,
method deep_cnn (line 42) | def deep_cnn(self,
method create_model (line 65) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 124) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 160) | def get_mask(self, max_frames, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/deep_lstm_model.py
class DeepLstmModel (line 13) | class DeepLstmModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/distillchain_cnn_deep_combine_chain_model.py
class DistillchainCnnDeepCombineChainModel (line 10) | class DistillchainCnnDeepCombineChainModel(models.BaseModel):
method cnn (line 13) | def cnn(self,
method create_model (line 42) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 105) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 136) | def get_mask(self, max_frames, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/distillchain_lstm_attention_max_pooling_model.py
class DistillchainLstmAttentionMaxPoolingModel (line 10) | class DistillchainLstmAttentionMaxPoolingModel(models.BaseModel):
method create_model (line 13) | def create_model(self, model_input, vocab_size, num_frames,
method sub_moe (line 87) | def sub_moe(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_frame_models/distillchain_lstm_cnn_deep_combine_chain_model.py
class DistillchainLstmCnnDeepCombineChainModel (line 10) | class DistillchainLstmCnnDeepCombineChainModel(models.BaseModel):
method cnn (line 13) | def cnn(self,
method create_model (line 42) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 110) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 141) | def get_mask(self, max_frames, num_frames):
method lstmoutput (line 156) | def lstmoutput(self, model_input, vocab_size, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/distillchain_lstm_memory_deep_combine_chain_model.py
class DistillchainLstmMemoryDeepCombineChainModel (line 13) | class DistillchainLstmMemoryDeepCombineChainModel(models.BaseModel):
method create_model (line 16) | def create_model(self, model_input, vocab_size, num_frames,
method sub_lstm (line 82) | def sub_lstm(self, model_input, num_frames, lstm_size, number_of_layer...
method sub_moe (line 100) | def sub_moe(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_frame_models/distillchain_lstm_parallel_finaloutput_model.py
class DistillchainLstmParallelFinaloutputModel (line 13) | class DistillchainLstmParallelFinaloutputModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames,
FILE: youtube-8m-wangheda/all_frame_models/distillchain_multiscale_cnn_lstm_model.py
class DistillchainMultiscaleCnnLstmModel (line 10) | class DistillchainMultiscaleCnnLstmModel(models.BaseModel):
method cnn (line 12) | def cnn(self,
method moe (line 47) | def moe(self,model_input,
method rnn (line 86) | def rnn(self, model_input, lstm_size, num_frames,
method create_model (line 99) | def create_model(self, model_input, vocab_size, num_frames,
FILE: youtube-8m-wangheda/all_frame_models/frame_seg_model.py
class FrameSegModel (line 13) | class FrameSegModel(models.BaseModel):
method create_model (line 15) | def create_model(self,
method frame_mean (line 49) | def frame_mean(self, model_input, frame_start,
FILE: youtube-8m-wangheda/all_frame_models/framehop_lstm_memory_deep_combine_chain_model.py
class FramehopLstmMemoryDeepCombineChainModel (line 13) | class FramehopLstmMemoryDeepCombineChainModel(models.BaseModel):
method lstm (line 16) | def lstm(self, model_input, vocab_size, num_frames, sub_scope="",
method create_model (line 59) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 114) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_length_code (line 151) | def get_length_code(self, num_frames):
method resolution (line 161) | def resolution(self, model_input_raw, num_frames, resolution, method="...
FILE: youtube-8m-wangheda/all_frame_models/framehop_lstm_memory_model.py
class FramehopLstmMemoryModel (line 13) | class FramehopLstmMemoryModel(models.BaseModel):
method lstm (line 16) | def lstm(self, model_input, vocab_size, num_frames, sub_scope="",
method create_model (line 59) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method get_length_code (line 96) | def get_length_code(self, num_frames):
method resolution (line 106) | def resolution(self, model_input_raw, num_frames, resolution, method="...
FILE: youtube-8m-wangheda/all_frame_models/gru_pooling_model.py
class GruPoolingModel (line 13) | class GruPoolingModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/gru_with_pooling_model.py
class GruWithPoolingModel (line 13) | class GruWithPoolingModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/layernorm_lstm_memory_model.py
class LayerNormLstmMemoryModel (line 13) | class LayerNormLstmMemoryModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames,
FILE: youtube-8m-wangheda/all_frame_models/logistic_model.py
class FrameLevelLogisticModel (line 13) | class FrameLevelLogisticModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_advanced_model.py
class LstmAdvancedModel (line 13) | class LstmAdvancedModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_attention_lstm_model.py
class LstmAttentionLstmModel (line 13) | class LstmAttentionLstmModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_attention_max_pooling_model.py
class LstmAttentionMaxPoolingModel (line 10) | class LstmAttentionMaxPoolingModel(models.BaseModel):
method create_model (line 13) | def create_model(self, model_input, vocab_size, num_frames,
method sub_moe (line 70) | def sub_moe(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_frame_models/lstm_attention_model.py
class LstmAttentionModel (line 13) | class LstmAttentionModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_auxloss_deep_combine_chain_model.py
class LstmAuxlossDeepCombineChainModel (line 13) | class LstmAuxlossDeepCombineChainModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 100) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_frame_models/lstm_cnn_deep_combine_chain_model.py
class LstmCnnDeepCombineChainModel (line 10) | class LstmCnnDeepCombineChainModel(models.BaseModel):
method cnn (line 13) | def cnn(self,
method create_model (line 42) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 94) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 125) | def get_mask(self, max_frames, num_frames):
method lstmoutput (line 140) | def lstmoutput(self, model_input, vocab_size, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/lstm_divided_model.py
class LstmDividedModel (line 13) | class LstmDividedModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_look_back_model.py
class LstmLookBackModel (line 13) | class LstmLookBackModel(models.BaseModel):
method shift (line 15) | def shift(self,
method create_model (line 32) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_memory_chain_model.py
class LstmMemoryChainModel (line 13) | class LstmMemoryChainModel(models.BaseModel):
method create_model (line 16) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_memory_deep_chain_model.py
class LstmMemoryDeepChainModel (line 13) | class LstmMemoryDeepChainModel(models.BaseModel):
method create_model (line 16) | def create_model(self, model_input, vocab_size, num_frames,
method sub_lstm (line 57) | def sub_lstm(self, model_input, num_frames, lstm_size, number_of_layer...
method sub_moe (line 75) | def sub_moe(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_frame_models/lstm_memory_input_chain_model.py
class LstmMemoryInputChainModel (line 13) | class LstmMemoryInputChainModel(models.BaseModel):
method create_model (line 16) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
FILE: youtube-8m-wangheda/all_frame_models/lstm_memory_model.py
class LstmMemoryModel (line 13) | class LstmMemoryModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames,
FILE: youtube-8m-wangheda/all_frame_models/lstm_memory_multitask_model.py
class LstmMemoryMultitaskModel (line 13) | class LstmMemoryMultitaskModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_memory_normalization_model.py
class LstmMemoryNormalizationModel (line 13) | class LstmMemoryNormalizationModel(models.BaseModel):
method create_model (line 16) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
method layer_normalize (line 57) | def layer_normalize(self, input_raw, epsilon=1e-8):
method l2_normalize (line 65) | def l2_normalize(self, input_raw, epsilon=1e-8):
method identical (line 70) | def identical(self, input_raw, epsilon=1e-8):
FILE: youtube-8m-wangheda/all_frame_models/lstm_memory_parallel_chain_model.py
class LstmMemoryParallelChainModel (line 13) | class LstmMemoryParallelChainModel(models.BaseModel):
method create_model (line 16) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_model.py
class LstmModel (line 13) | class LstmModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_multi_attention_model.py
class LstmMultiAttentionModel (line 13) | class LstmMultiAttentionModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_multi_pooling_model.py
class LstmMultiPoolingModel (line 13) | class LstmMultiPoolingModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_parallel_finaloutput_model.py
class LstmParallelFinaloutputModel (line 13) | class LstmParallelFinaloutputModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_parallel_memory_model.py
class LstmParallelMemoryModel (line 13) | class LstmParallelMemoryModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_parallel_model.py
class LstmParallelModel (line 13) | class LstmParallelModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_pooling_model.py
class LstmPoolingModel (line 13) | class LstmPoolingModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_positional_attention_max_pooling_model.py
class LstmPositionalAttentionMaxPoolingModel (line 10) | class LstmPositionalAttentionMaxPoolingModel(models.BaseModel):
method create_model (line 13) | def create_model(self, model_input, vocab_size, num_frames,
method get_positional_embedding (line 73) | def get_positional_embedding(self, model_input, num_frames, l2_penalty...
method get_mean_input (line 82) | def get_mean_input(self, model_input, num_frames):
method sub_moe (line 89) | def sub_moe(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_frame_models/lstm_with_mean_input_model.py
class LstmWithMeanInputModel (line 13) | class LstmWithMeanInputModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/lstm_with_pooling_model.py
class LstmWithPoolingModel (line 13) | class LstmWithPoolingModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/mm_lstm_memory_model.py
class MatchingMatrixLstmMemoryModel (line 13) | class MatchingMatrixLstmMemoryModel(models.BaseModel):
method matching_matrix (line 15) | def matching_matrix(self,
method create_model (line 50) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/multi_view_cnn_deep_combine_chain_model.py
class MultiViewCnnDeepCombineChainModel (line 10) | class MultiViewCnnDeepCombineChainModel(models.BaseModel):
method cnn (line 12) | def cnn(self,
method create_model (line 41) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method multiview (line 89) | def multiview(self, cnn_output, axis=1):
method sub_model (line 96) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 127) | def get_mask(self, max_frames, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/multires_lstm_memory_deep_combine_chain_model.py
class MultiresLstmMemoryDeepCombineChainModel (line 13) | class MultiresLstmMemoryDeepCombineChainModel(models.BaseModel):
method lstm (line 16) | def lstm(self, model_input, vocab_size, num_frames, sub_scope="", **un...
method create_model (line 48) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 102) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_length_code (line 139) | def get_length_code(self, num_frames):
method resolution (line 149) | def resolution(self, model_input_raw, num_frames, resolution):
FILE: youtube-8m-wangheda/all_frame_models/multiscale_cnn_lstm_model.py
class MultiscaleCnnLstmModel (line 10) | class MultiscaleCnnLstmModel(models.BaseModel):
method cnn (line 12) | def cnn(self,
method moe (line 47) | def moe(self,model_input,
method rnn (line 86) | def rnn(self, model_input, lstm_size, num_frames,
method create_model (line 99) | def create_model(self, model_input, vocab_size, num_frames,
FILE: youtube-8m-wangheda/all_frame_models/positional_cnn_deep_combine_chain_model.py
class PositionalCnnDeepCombineChainModel (line 10) | class PositionalCnnDeepCombineChainModel(models.BaseModel):
method cnn (line 13) | def cnn(self,
method add_positional_embedding (line 42) | def add_positional_embedding(self, model_input, num_frames, l2_penalty...
method gated_linear_unit (line 52) | def gated_linear_unit(self, input1, input2):
method create_model (line 55) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 112) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 143) | def get_mask(self, max_frames, num_frames):
FILE: youtube-8m-wangheda/all_frame_models/progressive_attention_lstm_model.py
class ProgressiveAttentionLstmModel (line 13) | class ProgressiveAttentionLstmModel(models.BaseModel):
method create_model (line 15) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
FILE: youtube-8m-wangheda/all_frame_models/wide_and_deep_model.py
class WideAndDeepModel (line 14) | class WideAndDeepModel(models.BaseModel):
method create_model (line 16) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
FILE: youtube-8m-wangheda/all_video_models/chain_main_relu_moe_model.py
class ChainMainReluMoeModel (line 9) | class ChainMainReluMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 27) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/chain_moe_model.py
class ChainMoeModel (line 9) | class ChainMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 20) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/chain_support_relu_moe_model.py
class ChainSupportReluMoeModel (line 9) | class ChainSupportReluMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 27) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/deep_chain_model.py
class DeepChainModel (line 9) | class DeepChainModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 40) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_length_code (line 71) | def get_length_code(self, num_frames):
FILE: youtube-8m-wangheda/all_video_models/deep_combine_chain_model.py
class DeepCombineChainModel (line 9) | class DeepCombineChainModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 51) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/distillchain_deep_combine_chain_model.py
class DistillchainDeepCombineChainModel (line 9) | class DistillchainDeepCombineChainModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 62) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/hidden_chain_model.py
class HiddenChainModel (line 9) | class HiddenChainModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 35) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/hidden_combine_chain_model.py
class HiddenCombineChainModel (line 9) | class HiddenCombineChainModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 35) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/logistic_model.py
class LogisticModel (line 9) | class LogisticModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, origi...
FILE: youtube-8m-wangheda/all_video_models/mlp_moe_model.py
class MlpMoeModel (line 9) | class MlpMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self,
FILE: youtube-8m-wangheda/all_video_models/moe_model.py
class MoeModel (line 9) | class MoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self,
FILE: youtube-8m-wangheda/all_video_models/multitask_divergence_deep_combine_chain_model.py
class MultiTaskDivergenceDeepCombineChainModel (line 9) | class MultiTaskDivergenceDeepCombineChainModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_chain_model (line 29) | def sub_chain_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 82) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/multitask_divergence_moe_model.py
class MultiTaskDivergenceMoeModel (line 9) | class MultiTaskDivergenceMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 29) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/multitask_moe_model.py
class MultiTaskMoeModel (line 9) | class MultiTaskMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 19) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/shortcut_chain_support_relu_moe_model.py
class ShortcutChainSupportReluMoeModel (line 9) | class ShortcutChainSupportReluMoeModel(models.BaseModel):
method create_model (line 12) | def create_model(self, model_input, vocab_size, num_mixtures=None,
method sub_model (line 28) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
FILE: youtube-8m-wangheda/all_video_models/stage2_logistic_model.py
class Stage2LogisticModel (line 9) | class Stage2LogisticModel(models.BaseModel):
method create_model (line 10) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, origi...
FILE: youtube-8m-wangheda/average_precision_calculator.py
class AveragePrecisionCalculator (line 61) | class AveragePrecisionCalculator(object):
method __init__ (line 64) | def __init__(self, top_n=None):
method heap_size (line 84) | def heap_size(self):
method num_accumulated_positives (line 89) | def num_accumulated_positives(self):
method accumulate (line 93) | def accumulate(self, predictions, actuals, num_positives=None):
method clear (line 134) | def clear(self):
method peek_ap_at_n (line 139) | def peek_ap_at_n(self):
method ap (line 158) | def ap(predictions, actuals):
method ap_at_n (line 180) | def ap_at_n(predictions, actuals, n=20, total_num_positives=None):
method _shuffle (line 248) | def _shuffle(predictions, actuals):
method _zero_one_normalize (line 256) | def _zero_one_normalize(predictions, epsilon=1e-7):
FILE: youtube-8m-wangheda/eval.py
function find_class_by_name (line 87) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 93) | def get_input_evaluation_tensors(reader,
function build_graph (line 115) | def build_graph(reader,
function evaluation_loop (line 208) | def evaluation_loop(video_id_batch, prediction_batch, label_batch, loss,
function evaluate (line 325) | def evaluate():
function main (line 387) | def main(unused_argv):
FILE: youtube-8m-wangheda/eval_util.py
function flatten (line 24) | def flatten(l):
function calculate_hit_at_one (line 28) | def calculate_hit_at_one(predictions, actuals):
function calculate_recall_at_n (line 45) | def calculate_recall_at_n(predictions, actuals, n):
function calculate_precision_at_equal_recall_rate (line 74) | def calculate_precision_at_equal_recall_rate(predictions, actuals):
function calculate_gap (line 102) | def calculate_gap(predictions, actuals, top_k=20):
function top_k_by_class (line 123) | def top_k_by_class(predictions, labels, k=20):
function top_k_triplets (line 159) | def top_k_triplets(predictions, labels, k=20):
class EvaluationMetrics (line 167) | class EvaluationMetrics(object):
method __init__ (line 170) | def __init__(self, num_class, top_k):
method accumulate (line 189) | def accumulate(self, predictions, labels, loss):
method get (line 223) | def get(self):
method clear (line 247) | def clear(self):
FILE: youtube-8m-wangheda/inference-layer.py
function format_lines (line 74) | def format_lines(video_ids, predictions, top_k):
function get_input_data_tensors (line 88) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function get_output_feature (line 123) | def get_output_feature(video_id, labels, feature_dict):
function write_to_record (line 132) | def write_to_record(id_batch, label_batch, feature_dict, filenum, num_ex...
function inference (line 143) | def inference(reader, train_dir, data_pattern, out_file_location, batch_...
function main (line 225) | def main(unused_argv):
FILE: youtube-8m-wangheda/inference-pre-ensemble-get-input.py
function get_input_data_tensors (line 134) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function inference (line 170) | def inference(reader, model_checkpoint_path, data_pattern, out_file_loca...
function write_to_record (line 242) | def write_to_record(id_batch, label_batch, input_batch, filenum, num_exa...
function get_output_feature (line 252) | def get_output_feature(video_id, labels, features, feature_names):
function main (line 261) | def main(unused_argv):
FILE: youtube-8m-wangheda/inference-pre-ensemble-with-predictions.py
function find_class_by_name (line 91) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 96) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function build_graph (line 132) | def build_graph(reader,
function inference (line 212) | def inference(saver, model_checkpoint_path, out_file_location, batch_siz...
function write_to_record (line 300) | def write_to_record(id_batch, label_batch, predictions, filenum, num_exa...
function get_output_feature (line 310) | def get_output_feature(video_id, labels, features, feature_names):
function main (line 319) | def main(unused_argv):
FILE: youtube-8m-wangheda/inference-pre-ensemble.py
function find_class_by_name (line 91) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 96) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function build_graph (line 132) | def build_graph(reader,
function inference (line 203) | def inference(saver, model_checkpoint_path, out_file_location, batch_siz...
function write_to_record (line 291) | def write_to_record(id_batch, label_batch, predictions, filenum, num_exa...
function get_output_feature (line 301) | def get_output_feature(video_id, labels, features, feature_names):
function main (line 310) | def main(unused_argv):
FILE: youtube-8m-wangheda/inference-sample-error-analysis.py
function format_lines (line 74) | def format_lines(video_ids, predictions, labels, top_k):
function get_input_data_tensors (line 99) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function inference (line 134) | def inference(reader, train_dir, data_pattern, out_file_location, batch_...
function main (line 199) | def main(unused_argv):
FILE: youtube-8m-wangheda/inference-sample-error.py
function find_class_by_name (line 85) | def find_class_by_name(name, modules):
function format_lines (line 90) | def format_lines(video_ids, predictions, labels):
function get_input_data_tensors (line 100) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function build_graph (line 137) | def build_graph(reader,
function inference (line 193) | def inference(saver, train_dir, out_file_location, batch_size, top_k):
function main (line 258) | def main(unused_argv):
FILE: youtube-8m-wangheda/inference-stage1.py
function format_lines (line 72) | def format_lines(video_ids, predictions, top_k):
function get_input_data_tensors (line 86) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function get_output_feature (line 121) | def get_output_feature(video_id, labels, feature_dict):
function write_to_record (line 130) | def write_to_record(id_batch, label_batch, feature_dict, filenum, num_ex...
function inference (line 141) | def inference(reader, train_dir, data_pattern, out_file_location, batch_...
function main (line 222) | def main(unused_argv):
FILE: youtube-8m-wangheda/inference.py
function format_lines (line 76) | def format_lines(video_ids, predictions, top_k):
function get_input_data_tensors (line 90) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function inference (line 125) | def inference(reader, train_dir, data_pattern, out_file_location, batch_...
function main (line 190) | def main(unused_argv):
FILE: youtube-8m-wangheda/losses.py
function smoothing (line 46) | def smoothing(labels):
class BaseLoss (line 56) | class BaseLoss(object):
method calculate_loss (line 59) | def calculate_loss(self, unused_predictions, unused_labels, **unused_p...
class WeightedCrossEntropyLoss (line 76) | class WeightedCrossEntropyLoss(BaseLoss):
method calculate_loss (line 81) | def calculate_loss(self, predictions, labels, **unused_params):
class MeanSquareErrorLoss (line 96) | class MeanSquareErrorLoss(BaseLoss):
method calculate_loss (line 100) | def calculate_loss(self, predictions, labels, **unused_params):
class CrossEntropyLoss (line 110) | class CrossEntropyLoss(BaseLoss):
method calculate_loss (line 114) | def calculate_loss(self, predictions, labels, weights=None, **unused_p...
class HingeLoss (line 132) | class HingeLoss(BaseLoss):
method calculate_loss (line 140) | def calculate_loss(self, predictions, labels, b=1.0, **unused_params):
class PairwiseHingeLoss (line 150) | class PairwiseHingeLoss(BaseLoss):
method calculate_loss (line 151) | def calculate_loss(self, predictions, labels, margin=0.2, adaptive=3.0...
class MixedLoss (line 182) | class MixedLoss(BaseLoss):
method calculate_loss (line 183) | def calculate_loss(self, predictions, labels, margin=0.2, adaptive=3, ...
class SoftmaxLoss (line 190) | class SoftmaxLoss(BaseLoss):
method calculate_loss (line 202) | def calculate_loss(self, predictions, labels, **unused_params):
class MultiTaskLoss (line 216) | class MultiTaskLoss(BaseLoss):
method calculate_loss (line 219) | def calculate_loss(self, unused_predictions, unused_labels, **unused_p...
method get_support (line 222) | def get_support(self, labels, support_type=None):
class MultiTaskCrossEntropyAndSoftmaxLoss (line 260) | class MultiTaskCrossEntropyAndSoftmaxLoss(MultiTaskLoss):
method calculate_loss (line 263) | def calculate_loss(self, predictions, support_predictions, labels, **u...
class MultiTaskCrossEntropyLoss (line 271) | class MultiTaskCrossEntropyLoss(MultiTaskLoss):
method calculate_loss (line 274) | def calculate_loss(self, predictions, support_predictions, labels, **u...
class BatchAgreementCrossEntropyLoss (line 281) | class BatchAgreementCrossEntropyLoss(BaseLoss):
method calculate_loss (line 284) | def calculate_loss(self, predictions, labels, **unused_params):
class TopKBatchAgreementCrossEntropyLoss (line 322) | class TopKBatchAgreementCrossEntropyLoss(BaseLoss):
method calculate_loss (line 325) | def calculate_loss(self, predictions, labels, topk=20, **unused_params):
class MultiTaskDivergenceCrossEntropyLoss (line 358) | class MultiTaskDivergenceCrossEntropyLoss(MultiTaskLoss):
method calculate_loss (line 361) | def calculate_loss(self, predictions, support_predictions, labels, **u...
class MultiTaskDivergenceCrossEntropyAndMSELoss (line 386) | class MultiTaskDivergenceCrossEntropyAndMSELoss(MultiTaskLoss):
method calculate_loss (line 389) | def calculate_loss(self, predictions, support_predictions, labels, **u...
FILE: youtube-8m-wangheda/mean_average_precision_calculator.py
class MeanAveragePrecisionCalculator (line 44) | class MeanAveragePrecisionCalculator(object):
method __init__ (line 48) | def __init__(self, num_class):
method accumulate (line 71) | def accumulate(self, predictions, actuals, num_positives=None):
method clear (line 95) | def clear(self):
method is_empty (line 99) | def is_empty(self):
method peek_map_at_n (line 103) | def peek_map_at_n(self):
FILE: youtube-8m-wangheda/model_utils.py
function SampleRandomSequence (line 23) | def SampleRandomSequence(model_input, num_frames, num_samples):
function SampleRandomFrames (line 51) | def SampleRandomFrames(model_input, num_frames, num_samples):
function FramePooling (line 72) | def FramePooling(frames, method, **unused_params):
FILE: youtube-8m-wangheda/models.py
class BaseModel (line 17) | class BaseModel(object):
method create_model (line 20) | def create_model(self, unused_model_input, **unused_params):
FILE: youtube-8m-wangheda/readers.py
function resize_axis (line 21) | def resize_axis(tensor, axis, new_size, fill_value=0):
class BaseReader (line 58) | class BaseReader(object):
method prepare_reader (line 61) | def prepare_reader(self, unused_filename_queue):
class YT8MAggregatedFeatureReader (line 66) | class YT8MAggregatedFeatureReader(BaseReader):
method __init__ (line 74) | def __init__(self,
method prepare_reader (line 94) | def prepare_reader(self, filename_queue, batch_size=1024):
class YT8MFrameFeatureReader (line 127) | class YT8MFrameFeatureReader(BaseReader):
method __init__ (line 136) | def __init__(self,
method get_video_matrix (line 159) | def get_video_matrix(self,
method prepare_reader (line 189) | def prepare_reader(self,
class YT8MAggregatedDistillationFeatureReader (line 261) | class YT8MAggregatedDistillationFeatureReader(BaseReader):
method __init__ (line 269) | def __init__(self,
method prepare_reader (line 289) | def prepare_reader(self, filename_queue, batch_size=1024):
class YT8MFrameDistillationFeatureReader (line 323) | class YT8MFrameDistillationFeatureReader(BaseReader):
method __init__ (line 332) | def __init__(self,
method get_video_matrix (line 355) | def get_video_matrix(self,
method prepare_reader (line 385) | def prepare_reader(self,
FILE: youtube-8m-wangheda/train-with-predictions.py
function validate_class_name (line 138) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 166) | def get_input_data_tensors(reader,
function find_class_by_name (line 208) | def find_class_by_name(name, modules):
function get_video_weights_array (line 213) | def get_video_weights_array():
function optional_assign_weights (line 219) | def optional_assign_weights(sess, weights_input, weights_assignment):
function get_video_weights (line 227) | def get_video_weights(video_id_batch):
function build_graph (line 246) | def build_graph(reader,
class Trainer (line 480) | class Trainer(object):
method __init__ (line 483) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 502) | def run(self, start_new_model=False):
method start_server_if_distributed (line 622) | def start_server_if_distributed(self):
method remove_training_directory (line 639) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 652) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 672) | def recover_model(self, meta_filename):
method build_model (line 677) | def build_model(self):
class ParameterServer (line 739) | class ParameterServer(object):
method __init__ (line 742) | def __init__(self, cluster, task):
method run (line 754) | def run(self):
function start_server (line 763) | def start_server(cluster, task):
function task_as_string (line 786) | def task_as_string(task):
function main (line 789) | def main(unused_argv):
FILE: youtube-8m-wangheda/train-with-rebuild.py
function validate_class_name (line 133) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 161) | def get_input_data_tensors(reader,
function find_class_by_name (line 206) | def find_class_by_name(name, modules):
function get_video_weights_array (line 211) | def get_video_weights_array():
function optional_assign_weights (line 217) | def optional_assign_weights(sess, weights_input, weights_assignment):
function get_video_weights (line 225) | def get_video_weights(video_id_batch):
function build_graph (line 244) | def build_graph(reader,
class Trainer (line 451) | class Trainer(object):
method __init__ (line 454) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 473) | def run(self, start_new_model=False):
method start_server_if_distributed (line 592) | def start_server_if_distributed(self):
method remove_training_directory (line 609) | def remove_training_directory(self, train_dir):
method get_latest_checkpoint (line 622) | def get_latest_checkpoint(self, start_new_model, train_dir):
method build_model (line 636) | def build_model(self):
class ParameterServer (line 687) | class ParameterServer(object):
method __init__ (line 690) | def __init__(self, cluster, task):
method run (line 702) | def run(self):
function start_server (line 711) | def start_server(cluster, task):
function task_as_string (line 734) | def task_as_string(task):
function main (line 737) | def main(unused_argv):
FILE: youtube-8m-wangheda/train.py
function validate_class_name (line 139) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 167) | def get_input_data_tensors(reader,
function find_class_by_name (line 212) | def find_class_by_name(name, modules):
function get_video_weights_array (line 217) | def get_video_weights_array():
function optional_assign_weights (line 223) | def optional_assign_weights(sess, weights_input, weights_assignment):
function get_video_weights (line 231) | def get_video_weights(video_id_batch):
function get_weights_by_predictions (line 250) | def get_weights_by_predictions(labels_batch, predictions):
function build_graph (line 262) | def build_graph(reader,
class Trainer (line 482) | class Trainer(object):
method __init__ (line 485) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 504) | def run(self, start_new_model=False):
method start_server_if_distributed (line 624) | def start_server_if_distributed(self):
method remove_training_directory (line 641) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 654) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 674) | def recover_model(self, meta_filename):
method build_model (line 679) | def build_model(self):
class ParameterServer (line 731) | class ParameterServer(object):
method __init__ (line 734) | def __init__(self, cluster, task):
method run (line 746) | def run(self):
function start_server (line 755) | def start_server(cluster, task):
function task_as_string (line 778) | def task_as_string(task):
function main (line 781) | def main(unused_argv):
FILE: youtube-8m-wangheda/training_utils/human_readable_error_analysis.py
function csv_line (line 13) | def csv_line(str_list):
FILE: youtube-8m-wangheda/utils.py
function Dequantize (line 23) | def Dequantize(feat_vector, max_quantized_value=2, min_quantized_value=-2):
function MakeSummary (line 41) | def MakeSummary(name, value):
function AddGlobalStepSummary (line 50) | def AddGlobalStepSummary(summary_writer,
function AddEpochSummary (line 94) | def AddEpochSummary(summary_writer,
function GetListOfFeatureNamesAndSizes (line 140) | def GetListOfFeatureNamesAndSizes(feature_names, feature_sizes):
function clip_gradient_norms (line 164) | def clip_gradient_norms(gradients_to_variables, max_norm):
FILE: youtube-8m-zhangteng/YM_framemean.py
function get_frame_input_feature (line 15) | def get_frame_input_feature(input_file):
function get_video_input_feature (line 42) | def get_video_input_feature(input_file):
function get_output_files (line 59) | def get_output_files(features, feature_names):
function get_output_feature (line 67) | def get_output_feature(video_id, labels, features, feature_names):
function read_batch_files (line 76) | def read_batch_files(q,files):
class myThread (line 123) | class myThread(threading.Thread):
method __init__ (line 124) | def __init__(self, threadID, name, files, q):
method run (line 130) | def run(self):
function main (line 135) | def main():
FILE: youtube-8m-zhangteng/YM_labels_matrix.py
function get_frame_input_feature (line 12) | def get_frame_input_feature(input_file):
function get_video_input_feature (line 39) | def get_video_input_feature(input_file):
function get_output_feature (line 57) | def get_output_feature(video_id, labels, features, feature_names):
function main (line 66) | def main():
FILE: youtube-8m-zhangteng/YM_labels_model.py
function main (line 10) | def main():
FILE: youtube-8m-zhangteng/YM_labels_vocab.py
function main (line 10) | def main():
FILE: youtube-8m-zhangteng/YM_readframefeature.py
function get_frame_input_feature (line 12) | def get_frame_input_feature(input_file):
function get_video_input_feature (line 40) | def get_video_input_feature(input_file):
function get_output_feature (line 57) | def get_output_feature(video_id, labels, features, feature_names):
function main (line 66) | def main():
FILE: youtube-8m-zhangteng/average_precision_calculator.py
class AveragePrecisionCalculator (line 61) | class AveragePrecisionCalculator(object):
method __init__ (line 64) | def __init__(self, top_n=None):
method heap_size (line 84) | def heap_size(self):
method num_accumulated_positives (line 89) | def num_accumulated_positives(self):
method accumulate (line 93) | def accumulate(self, predictions, actuals, num_positives=None):
method clear (line 134) | def clear(self):
method peek_ap_at_n (line 139) | def peek_ap_at_n(self):
method ap (line 158) | def ap(predictions, actuals):
method ap_at_n (line 180) | def ap_at_n(predictions, actuals, n=20, total_num_positives=None):
method _shuffle (line 248) | def _shuffle(predictions, actuals):
method _zero_one_normalize (line 256) | def _zero_one_normalize(predictions, epsilon=1e-7):
FILE: youtube-8m-zhangteng/eval.py
function find_class_by_name (line 80) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 86) | def get_input_evaluation_tensors(reader,
function build_graph (line 125) | def build_graph(reader,
function evaluation_loop (line 183) | def evaluation_loop(video_id_batch, prediction_batch, label_batch, loss,
function evaluate (line 289) | def evaluate():
function main (line 341) | def main(unused_argv):
FILE: youtube-8m-zhangteng/eval_autoencoder.py
function find_class_by_name (line 74) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 80) | def get_input_evaluation_tensors(reader,
function build_graph (line 119) | def build_graph(reader,
function evaluation_loop (line 168) | def evaluation_loop(video_id_batch, prediction_batch, label_batch, loss,
function evaluate (line 273) | def evaluate():
function main (line 325) | def main(unused_argv):
FILE: youtube-8m-zhangteng/eval_distill.py
function find_class_by_name (line 90) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 96) | def get_input_evaluation_tensors(reader,
function build_graph (line 134) | def build_graph(reader1,
function evaluation_loop (line 202) | def evaluation_loop(video_id_batch, unused_id_batch, prediction_batch, l...
function evaluate (line 326) | def evaluate():
function main (line 385) | def main(unused_argv):
FILE: youtube-8m-zhangteng/eval_embedding.py
function find_class_by_name (line 75) | def find_class_by_name(name, modules):
function get_input_evaluation_tensors (line 81) | def get_input_evaluation_tensors(reader,
function build_graph (line 120) | def build_graph(reader,
function evaluation_loop (line 169) | def evaluation_loop(video_id_batch, prediction_batch, label_batch, loss,
function evaluate (line 274) | def evaluate():
function main (line 326) | def main(unused_argv):
FILE: youtube-8m-zhangteng/eval_util.py
function flatten (line 24) | def flatten(l):
function calculate_hit_at_one (line 28) | def calculate_hit_at_one(predictions, actuals):
function calculate_precision_at_equal_recall_rate (line 45) | def calculate_precision_at_equal_recall_rate(predictions, actuals):
function calculate_gap (line 72) | def calculate_gap(predictions, actuals, top_k=20):
function top_k_by_class (line 93) | def top_k_by_class(predictions, labels, k=20):
function top_k_triplets (line 129) | def top_k_triplets(predictions, labels, k=20):
class EvaluationMetrics (line 137) | class EvaluationMetrics(object):
method __init__ (line 140) | def __init__(self, num_class, top_k):
method accumulate (line 159) | def accumulate(self, predictions, labels, loss):
method get (line 193) | def get(self):
method clear (line 217) | def clear(self):
FILE: youtube-8m-zhangteng/frame_level_models.py
class FrameLevelLogisticModel (line 60) | class FrameLevelLogisticModel(models.BaseModel):
method create_model (line 62) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class DbofModel (line 96) | class DbofModel(models.BaseModel):
method create_model (line 119) | def create_model(self,
class Dbof3mModel (line 208) | class Dbof3mModel(models.BaseModel):
method sub_moe (line 230) | def sub_moe(self,
method create_model (line 269) | def create_model(self,
class batch_norm (line 299) | class batch_norm(object):
method __init__ (line 301) | def __init__(self, epsilon=1e-5, momentum=0.99, name="batch_norm"):
method __call__ (line 309) | def __call__(self, x, train=True):
class LstmVisionModel (line 336) | class LstmVisionModel(models.BaseModel):
method create_model (line 339) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 398) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmGluModel (line 466) | class LstmGluModel(models.BaseModel):
method create_model (line 469) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 530) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmGlu2Model (line 631) | class LstmGlu2Model(models.BaseModel):
method create_model (line 634) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 695) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmGlu2MultilayerModel (line 794) | class LstmGlu2MultilayerModel(models.BaseModel):
method create_model (line 797) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method rnn_gate (line 826) | def rnn_gate(self, model_input, lstm_size, num_frames, l2_penalty=1e-8...
class LstmBigluModel (line 887) | class LstmBigluModel(models.BaseModel):
method create_model (line 890) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_forward_recurrent_unit (line 975) | def create_forward_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
method create_backward_recurrent_unit (line 1034) | def create_backward_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
method sub_moe (line 1104) | def sub_moe(self,model_input,
class LstmBiglu2Model (line 1160) | class LstmBiglu2Model(models.BaseModel):
method create_model (line 1163) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_forward_recurrent_unit (line 1255) | def create_forward_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
method create_backward_recurrent_unit (line 1354) | def create_backward_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
method sub_moe (line 1465) | def sub_moe(self,model_input,
class LstmGateModel (line 1521) | class LstmGateModel(models.BaseModel):
method create_model (line 1524) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 1583) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmGateMultilayerModel (line 1642) | class LstmGateMultilayerModel(models.BaseModel):
method create_model (line 1645) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method rnn_gate (line 1674) | def rnn_gate(self, model_input, lstm_size, num_frames, l2_penalty=1e-8...
method create_recurrent_unit (line 1733) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmQuickMemoryModel (line 1792) | class LstmQuickMemoryModel(models.BaseModel):
method create_model (line 1795) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 1847) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmLinearOutputModel (line 1948) | class LstmLinearOutputModel(models.BaseModel):
method create_model (line 1951) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 2010) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmLinearOutput2Model (line 2069) | class LstmLinearOutput2Model(models.BaseModel):
method create_model (line 2072) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 2131) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmNoOutputModel (line 2190) | class LstmNoOutputModel(models.BaseModel):
method create_model (line 2193) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
method create_recurrent_unit (line 2252) | def create_recurrent_unit(self,emb_dim,hidden_dim,l2_penalty):
class LstmModel (line 2299) | class LstmModel(models.BaseModel):
method create_model (line 2301) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmDiffModel (line 2341) | class LstmDiffModel(models.BaseModel):
method create_model (line 2343) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmFramesModel (line 2396) | class LstmFramesModel(models.BaseModel):
method sub_moe (line 2398) | def sub_moe(self,model_input,
method calculate_loss (line 2455) | def calculate_loss(self, predictions, labels, **unused_params):
method create_model (line 2465) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmFrames2Model (line 2536) | class LstmFrames2Model(models.BaseModel):
method sub_moe (line 2538) | def sub_moe(self,model_input,
method calculate_loss (line 2594) | def calculate_loss(self, predictions, labels, **unused_params):
method create_model (line 2604) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmFrames3Model (line 2678) | class LstmFrames3Model(models.BaseModel):
method sub_moe (line 2680) | def sub_moe(self,model_input,
method create_model (line 2737) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmMultiscaleModel (line 2827) | class LstmMultiscaleModel(models.BaseModel):
method cnn (line 2829) | def cnn(self,
method sub_moe (line 2864) | def sub_moe(self,model_input,
method rnn (line 2920) | def rnn(self, model_input, lstm_size, num_frames,sub_scope="", **unuse...
method create_model (line 2953) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class LstmMultiscaleDitillChainModel (line 2988) | class LstmMultiscaleDitillChainModel(models.BaseModel):
method cnn (line 2990) | def cnn(self,
method sub_moe (line 3025) | def sub_moe(self,model_input,
method rnn (line 3090) | def rnn(self, model_input, lstm_size, num_frames,sub_scope="", **unuse...
method create_model (line 3123) | def create_model(self, model_input, vocab_size, num_frames, distill_la...
class LstmMultiscale2Model (line 3160) | class LstmMultiscale2Model(models.BaseModel):
method cnn (line 3162) | def cnn(self,
method sub_moe (line 3197) | def sub_moe(self,model_input,
method rnn_gate (line 3254) | def rnn_gate(self, model_input, lstm_size, num_frames, l2_penalty=1e-8...
method rnn_standard (line 3306) | def rnn_standard(self, model_input, lstm_size, num_frames,sub_scope=""...
method create_model (line 3340) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class LstmMultiscale3Model (line 3375) | class LstmMultiscale3Model(models.BaseModel):
method cnn (line 3377) | def cnn(self,
method sub_moe (line 3412) | def sub_moe(self,model_input,
method rnn_glu (line 3469) | def rnn_glu(self, model_input, lstm_size, num_frames, l2_penalty=1e-8,...
method rnn_standard (line 3523) | def rnn_standard(self, model_input, lstm_size, num_frames,sub_scope=""...
method create_model (line 3557) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class LstmMultiscaleRebuildModel (line 3592) | class LstmMultiscaleRebuildModel(models.BaseModel):
method create_model (line 3594) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class LstmLayerModel (line 3629) | class LstmLayerModel(models.BaseModel):
method create_model (line 3631) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmDivideModel (line 3685) | class LstmDivideModel(models.BaseModel):
method create_model (line 3687) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmDivideRebuildModel (line 3756) | class LstmDivideRebuildModel(models.BaseModel):
method create_model (line 3758) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmDivideRebuild2Model (line 3801) | class LstmDivideRebuild2Model(models.BaseModel):
method create_model (line 3803) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmResidualModel (line 3878) | class LstmResidualModel(models.BaseModel):
method create_model (line 3882) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmNoiseModel (line 3925) | class LstmNoiseModel(models.BaseModel):
method create_model (line 3927) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmConditionModel (line 3971) | class LstmConditionModel(models.BaseModel):
method create_model (line 3973) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmRandomModel (line 4017) | class LstmRandomModel(models.BaseModel):
method create_model (line 4019) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class LstmAttentionModel (line 4081) | class LstmAttentionModel(models.BaseModel):
method create_model (line 4083) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmAutoencoderModel (line 4203) | class LstmAutoencoderModel(models.BaseModel):
method create_model (line 4205) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmSoftmaxModel (line 4271) | class LstmSoftmaxModel(models.BaseModel):
method create_model (line 4273) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class AttentionModel (line 4355) | class AttentionModel(models.BaseModel):
method create_model (line 4357) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class CnnGluModel (line 4407) | class CnnGluModel(models.BaseModel):
method cnn (line 4408) | def cnn(self,
method kmax (line 4454) | def kmax(self,
method sub_moe (line 4486) | def sub_moe(self,
method create_model (line 4543) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class CnnLstmModel (line 4574) | class CnnLstmModel(models.BaseModel):
method create_model (line 4576) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class CnnKmaxModel (line 4661) | class CnnKmaxModel(models.BaseModel):
method cnn (line 4663) | def cnn(self,
method sub_moe (line 4699) | def sub_moe(self,
method create_model (line 4756) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class CnnKmaxRebuildModel (line 4789) | class CnnKmaxRebuildModel(models.BaseModel):
method create_model (line 4791) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class CnnWholeModel (line 4822) | class CnnWholeModel(models.BaseModel):
method cnn (line 4824) | def cnn(self,
method sub_moe (line 4872) | def sub_moe(self,
method create_model (line 4929) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class CnnMultiscaleModel (line 4936) | class CnnMultiscaleModel(models.BaseModel):
method cnn (line 4938) | def cnn(self,
method sub_moe (line 4978) | def sub_moe(self,
method create_model (line 5035) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class DeepCnnModel (line 5068) | class DeepCnnModel(models.BaseModel):
method highway (line 5070) | def highway(self, input_1, input_2, size_1, size_2, l2_penalty=1e-8, l...
method conv_block (line 5089) | def conv_block(self, input, out_size, layer, kernalsize=3, l2_penalty=...
method create_model (line 5113) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class LstmExtendModel (line 5169) | class LstmExtendModel(models.BaseModel):
method create_model (line 5171) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmExtendStateModel (line 5236) | class LstmExtendStateModel(models.BaseModel):
method cnn (line 5237) | def cnn(self,
method create_model (line 5266) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmExtendInputModel (line 5336) | class LstmExtendInputModel(models.BaseModel):
method create_model (line 5338) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmExtendCNNModel (line 5403) | class LstmExtendCNNModel(models.BaseModel):
method cnn (line 5405) | def cnn(self,
method create_model (line 5434) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmExtendOrthoModel (line 5503) | class LstmExtendOrthoModel(models.BaseModel):
method create_model (line 5505) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmGateExtendModel (line 5574) | class LstmGateExtendModel(models.BaseModel):
method rnn_gate (line 5576) | def rnn_gate(self, model_input, lstm_size, num_frames, l2_penalty=1e-8...
method create_model (line 5625) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class LstmGluExtendModel (line 5675) | class LstmGluExtendModel(models.BaseModel):
method rnn_gate (line 5677) | def rnn_gate(self, model_input, lstm_size, num_frames, l2_penalty=1e-8...
method create_model (line 5728) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmExtendParallelModel (line 5778) | class LstmExtendParallelModel(models.BaseModel):
method create_model (line 5780) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmExtendCombineModel (line 5852) | class LstmExtendCombineModel(models.BaseModel):
method create_model (line 5854) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class LstmMoeModel (line 5926) | class LstmMoeModel(models.BaseModel):
method create_model (line 5928) | def create_model(self, model_input, vocab_size, num_frames, l2_penalty...
class InputExtendModel (line 5995) | class InputExtendModel(models.BaseModel):
method create_model (line 5997) | def create_model(self, model_input, vocab_size, num_frames,l2_penalty=...
class VideoFrameEvalModel (line 6076) | class VideoFrameEvalModel(models.BaseModel):
method create_model (line 6078) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class FrameAttentionEvalModel (line 6133) | class FrameAttentionEvalModel(models.BaseModel):
method create_model (line 6135) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class FrameRandomEvalModel (line 6182) | class FrameRandomEvalModel(models.BaseModel):
method create_model (line 6184) | def create_model(self, model_input, vocab_size, num_frames, **unused_p...
class CnnDCCDistillChainModel (line 6249) | class CnnDCCDistillChainModel(models.BaseModel):
method cnn (line 6252) | def cnn(self,
method create_model (line 6281) | def create_model(self, model_input, vocab_size, num_frames, num_mixtur...
method sub_model (line 6331) | def sub_model(self, model_input, vocab_size, num_mixtures=None,
method get_mask (line 6371) | def get_mask(self, max_frames, num_frames):
FILE: youtube-8m-zhangteng/inference-pre-ensemble-distill.py
function find_class_by_name (line 91) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 96) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function build_graph (line 132) | def build_graph(reader1,
function inference (line 197) | def inference(video_id_batch, prediction_batch, label_batch, saver, out_...
function write_to_record (line 286) | def write_to_record(id_batch, label_batch, predictions, filenum, num_exa...
function get_output_feature (line 296) | def get_output_feature(video_id, labels, features,feature_names):
function main (line 305) | def main(unused_argv):
FILE: youtube-8m-zhangteng/inference-pre-ensemble.py
function get_input_data_tensors (line 69) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function inference (line 105) | def inference(reader, model_checkpoint_path, data_pattern, out_file_loca...
function write_to_record (line 195) | def write_to_record(id_batch, label_batch, input_batch, predictions, fil...
function get_output_feature (line 205) | def get_output_feature(video_id, labels, features, feature_names):
function main (line 214) | def main(unused_argv):
FILE: youtube-8m-zhangteng/inference.py
function format_lines (line 69) | def format_lines(video_ids, predictions, top_k):
function get_input_data_tensors (line 83) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function inference (line 118) | def inference(reader, train_dir, data_pattern, out_file_location, batch_...
function main (line 178) | def main(unused_argv):
FILE: youtube-8m-zhangteng/inference_autoencoder.py
function format_lines (line 71) | def format_lines(video_ids, predictions, top_k):
function get_input_data_tensors (line 85) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function get_forward_parameters (line 120) | def get_forward_parameters(vocab_size=4716):
function inference (line 140) | def inference(reader,train_dir, data_pattern, out_file_location, batch_s...
function main (line 189) | def main(unused_argv):
FILE: youtube-8m-zhangteng/inference_embedding.py
function find_class_by_name (line 81) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 86) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function build_graph (line 122) | def build_graph(reader,
function inference (line 172) | def inference(video_id_batch, prediction_batch, label_batch, saver, out_...
function main (line 220) | def main(unused_argv):
FILE: youtube-8m-zhangteng/inference_test.py
function format_lines (line 69) | def format_lines(video_ids, predictions, top_k):
function get_input_data_tensors (line 83) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function inference (line 123) | def inference(reader, train_dir, data_pattern, out_file_location, batch_...
function main (line 183) | def main(unused_argv):
FILE: youtube-8m-zhangteng/inference_with_rebuild.py
function find_class_by_name (line 82) | def find_class_by_name(name, modules):
function get_input_data_tensors (line 87) | def get_input_data_tensors(reader, data_pattern, batch_size, num_readers...
function build_graph (line 123) | def build_graph(reader,
function inference (line 180) | def inference(video_id_batch, prediction_batch, label_batch, saver, out_...
function write_to_record (line 267) | def write_to_record(id_batch, label_batch, predictions, filenum, num_exa...
function get_output_feature (line 277) | def get_output_feature(video_id, labels, features,feature_names):
function main (line 286) | def main(unused_argv):
FILE: youtube-8m-zhangteng/labels_autoencoder.py
class AutoEncoderSoftmaxModel (line 35) | class AutoEncoderSoftmaxModel(models.BaseModel):
method create_model (line 38) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unu...
class AutoEncoderModel (line 84) | class AutoEncoderModel(models.BaseModel):
method create_model (line 87) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unu...
FILE: youtube-8m-zhangteng/labels_embedding.py
class EmbeddingSigmoidModel (line 32) | class EmbeddingSigmoidModel(models.BaseModel):
method create_model (line 35) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unu...
FILE: youtube-8m-zhangteng/labels_rbm.py
class RbmModel (line 29) | class RbmModel(models.BaseModel):
method create_model (line 32) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unu...
method propup (line 55) | def propup(self, visible):
method propdown (line 58) | def propdown(self, hidden):
method cd1 (line 61) | def cd1(self, visibles, learning_rate=0.1):
method reconstruction_error (line 72) | def reconstruction_error(self, model_input):
method sample_prob (line 76) | def sample_prob(self,probs):
method sample_h_given_v (line 78) | def sample_h_given_v(self, v_sample):
method sample_v_given_h (line 80) | def sample_v_given_h(self, h_sample):
method gibbs_hvh (line 82) | def gibbs_hvh(self, h0_sample):
method gibbs_vhv (line 86) | def gibbs_vhv(self, v0_sample):
FILE: youtube-8m-zhangteng/losses.py
class BaseLoss (line 42) | class BaseLoss(object):
method calculate_loss (line 45) | def calculate_loss(self, unused_predictions, unused_labels, **unused_p...
class CrossEntropyLoss (line 61) | class CrossEntropyLoss(BaseLoss):
method calculate_loss (line 64) | def calculate_loss(self, predictions, labels, **unused_params):
method calculate_loss_distill (line 170) | def calculate_loss_distill(self, predictions, labels_distill, labels, ...
method calculate_loss_distill_boost (line 197) | def calculate_loss_distill_boost(self, predictions, labels_distill, la...
method calculate_loss_distill_relabel (line 216) | def calculate_loss_distill_relabel(self, predictions, labels_distill, ...
method calculate_loss_negative (line 234) | def calculate_loss_negative(self, predictions_pos, predictions_neg, la...
method calculate_mseloss (line 248) | def calculate_mseloss(self, predictions, labels, **unused_params):
method calculate_loss_postprocess (line 254) | def calculate_loss_postprocess(self, predictions, labels, **unused_par...
method calculate_loss_max (line 266) | def calculate_loss_max(self, predictions, predictions_experts, labels,...
method calculate_loss_mix (line 280) | def calculate_loss_mix(self, predictions, predictions_class, labels, *...
method calculate_loss_mix2 (line 315) | def calculate_loss_mix2(self, predictions, predictions_class, predicti...
class CrossEntropyLoss_weight (line 336) | class CrossEntropyLoss_weight(BaseLoss):
method calculate_loss (line 340) | def calculate_loss(self, predictions, labels, **unused_params):
class HingeLoss_cos (line 359) | class HingeLoss_cos(BaseLoss):
method calculate_loss (line 366) | def calculate_loss(self, predictions, labels, b1=1.6, b2=-0.4, **unuse...
class SoftmaxLoss (line 412) | class SoftmaxLoss(BaseLoss):
method calculate_loss (line 424) | def calculate_loss(self, predictions, labels, **unused_params):
method calculate_loss_mix (line 448) | def calculate_loss_mix(self, predictions, predictions_class, labels, *...
FILE: youtube-8m-zhangteng/losses_embedding.py
class BaseLoss (line 42) | class BaseLoss(object):
method calculate_loss (line 45) | def calculate_loss(self, unused_predictions, unused_labels, **unused_p...
class CrossEntropyLoss (line 61) | class CrossEntropyLoss(BaseLoss):
method calculate_loss (line 64) | def calculate_loss(self, predictions, labels, **unused_params):
class SoftmaxLoss (line 81) | class SoftmaxLoss(BaseLoss):
method calculate_loss (line 93) | def calculate_loss(self, predictions, labels, **unused_params):
method calculate_loss_mix (line 117) | def calculate_loss_mix(self, predictions, predictions_class, labels, *...
FILE: youtube-8m-zhangteng/mean_average_precision_calculator.py
class MeanAveragePrecisionCalculator (line 44) | class MeanAveragePrecisionCalculator(object):
method __init__ (line 48) | def __init__(self, num_class):
method accumulate (line 71) | def accumulate(self, predictions, actuals, num_positives=None):
method clear (line 95) | def clear(self):
method is_empty (line 99) | def is_empty(self):
method peek_map_at_n (line 103) | def peek_map_at_n(self):
FILE: youtube-8m-zhangteng/model_utils.py
function SampleRandomSequence (line 23) | def SampleRandomSequence(model_input, num_frames, num_samples):
function SampleRandomFrames (line 51) | def SampleRandomFrames(model_input, num_frames, num_samples):
function FramePooling (line 72) | def FramePooling(frames, method, **unused_params):
FILE: youtube-8m-zhangteng/models.py
class BaseModel (line 17) | class BaseModel(object):
method create_model (line 20) | def create_model(self, unused_model_input, **unused_params):
FILE: youtube-8m-zhangteng/readers.py
function resize_axis (line 21) | def resize_axis(tensor, axis, new_size, fill_value=0):
class BaseReader (line 58) | class BaseReader(object):
method prepare_reader (line 61) | def prepare_reader(self, unused_filename_queue):
class YT8MAggregatedFeatureReader (line 66) | class YT8MAggregatedFeatureReader(BaseReader):
method __init__ (line 74) | def __init__(self,
method prepare_reader (line 94) | def prepare_reader(self, filename_queue, batch_size=1024):
class YT8MFrameFeatureReader (line 127) | class YT8MFrameFeatureReader(BaseReader):
method __init__ (line 136) | def __init__(self,
method get_video_matrix (line 159) | def get_video_matrix(self,
method prepare_reader (line 189) | def prepare_reader(self,
class YT8MAggregatedDistillationFeatureReader (line 261) | class YT8MAggregatedDistillationFeatureReader(BaseReader):
method __init__ (line 269) | def __init__(self,
method prepare_reader (line 289) | def prepare_reader(self, filename_queue, batch_size=1024):
class YT8MDistillationFeatureReader (line 323) | class YT8MDistillationFeatureReader(BaseReader):
method __init__ (line 331) | def __init__(self,
method prepare_reader (line 351) | def prepare_reader(self, filename_queue, batch_size=1024):
class YT8MFrameDistillationFeatureReader (line 378) | class YT8MFrameDistillationFeatureReader(BaseReader):
method __init__ (line 387) | def __init__(self,
method get_video_matrix (line 410) | def get_video_matrix(self,
method prepare_reader (line 440) | def prepare_reader(self,
FILE: youtube-8m-zhangteng/rnn_residual.py
function _infer_state_dtype (line 23) | def _infer_state_dtype(explicit_dtype, state):
function _on_device (line 52) | def _on_device(fn, device):
function _rnn_step (line 61) | def _rnn_step(
function dynamic_rnn (line 189) | def dynamic_rnn(cell, inputs, inerval, sequence_length=None, initial_sta...
function _dynamic_rnn_loop (line 370) | def _dynamic_rnn_loop(cell,
FILE: youtube-8m-zhangteng/train-with-rebuild.py
function validate_class_name (line 114) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 142) | def get_input_data_tensors(reader,
function find_class_by_name (line 187) | def find_class_by_name(name, modules):
function build_graph (line 193) | def build_graph(reader,
class Trainer (line 392) | class Trainer(object):
method __init__ (line 395) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 414) | def run(self, start_new_model=False):
method start_server_if_distributed (line 496) | def start_server_if_distributed(self):
method remove_training_directory (line 513) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 526) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 546) | def recover_model(self, meta_filename):
method get_latest_checkpoint (line 551) | def get_latest_checkpoint(self, start_new_model, train_dir):
method build_model (line 565) | def build_model(self):
class ParameterServer (line 611) | class ParameterServer(object):
method __init__ (line 614) | def __init__(self, cluster, task):
method run (line 626) | def run(self):
function start_server (line 635) | def start_server(cluster, task):
function task_as_string (line 658) | def task_as_string(task):
function main (line 661) | def main(unused_argv):
FILE: youtube-8m-zhangteng/train.py
function validate_class_name (line 127) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 155) | def get_input_data_tensors(reader,
function find_class_by_name (line 200) | def find_class_by_name(name, modules):
function build_graph (line 206) | def build_graph(reader,
class Trainer (line 445) | class Trainer(object):
method __init__ (line 448) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 468) | def run(self, start_new_model=False):
method start_server_if_distributed (line 555) | def start_server_if_distributed(self):
method remove_training_directory (line 572) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 585) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 605) | def recover_model(self, meta_filename):
method build_model (line 610) | def build_model(self):
class ParameterServer (line 661) | class ParameterServer(object):
method __init__ (line 664) | def __init__(self, cluster, task):
method run (line 676) | def run(self):
function start_server (line 685) | def start_server(cluster, task):
function task_as_string (line 708) | def task_as_string(task):
function main (line 711) | def main(unused_argv):
FILE: youtube-8m-zhangteng/train_autoencoder.py
function validate_class_name (line 105) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 133) | def get_input_data_tensors(reader,
function find_class_by_name (line 178) | def find_class_by_name(name, modules):
function get_forward_parameters (line 183) | def get_forward_parameters(vocab_size=4716):
function build_graph (line 203) | def build_graph(reader,
class Trainer (line 341) | class Trainer(object):
method __init__ (line 344) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 363) | def run(self, start_new_model=False):
method start_server_if_distributed (line 455) | def start_server_if_distributed(self):
method remove_training_directory (line 472) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 485) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 505) | def recover_model(self, meta_filename):
method build_model (line 510) | def build_model(self):
class ParameterServer (line 553) | class ParameterServer(object):
method __init__ (line 556) | def __init__(self, cluster, task):
method run (line 568) | def run(self):
function start_server (line 577) | def start_server(cluster, task):
function task_as_string (line 600) | def task_as_string(task):
function main (line 603) | def main(unused_argv):
FILE: youtube-8m-zhangteng/train_embedding.py
function validate_class_name (line 106) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 134) | def get_input_data_tensors(reader,
function find_class_by_name (line 179) | def find_class_by_name(name, modules):
function build_graph (line 185) | def build_graph(reader,
class Trainer (line 319) | class Trainer(object):
method __init__ (line 322) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 342) | def run(self, start_new_model=False):
method start_server_if_distributed (line 430) | def start_server_if_distributed(self):
method remove_training_directory (line 447) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 460) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 480) | def recover_model(self, meta_filename):
method build_model (line 485) | def build_model(self):
class ParameterServer (line 528) | class ParameterServer(object):
method __init__ (line 531) | def __init__(self, cluster, task):
method run (line 543) | def run(self):
function start_server (line 552) | def start_server(cluster, task):
function task_as_string (line 575) | def task_as_string(task):
function main (line 578) | def main(unused_argv):
FILE: youtube-8m-zhangteng/train_ensemble.py
function validate_class_name (line 136) | def validate_class_name(flag_value, category, modules, expected_supercla...
function get_input_data_tensors (line 165) | def get_input_data_tensors(reader,
function find_class_by_name (line 204) | def find_class_by_name(name, modules):
function build_graph (line 210) | def build_graph(reader1,
class Trainer (line 432) | class Trainer(object):
method __init__ (line 435) | def __init__(self, cluster, task, train_dir, log_device_placement=True):
method run (line 455) | def run(self, start_new_model=False):
method start_server_if_distributed (line 542) | def start_server_if_distributed(self):
method remove_training_directory (line 559) | def remove_training_directory(self, train_dir):
method get_meta_filename (line 572) | def get_meta_filename(self, start_new_model, train_dir):
method recover_model (line 592) | def recover_model(self, meta_filename):
method build_model (line 597) | def build_model(self):
class ParameterServer (line 641) | class ParameterServer(object):
method __init__ (line 644) | def __init__(self, cluster, task):
method run (line 656) | def run(self):
function start_server (line 665) | def start_server(cluster, task):
function task_as_string (line 688) | def task_as_string(task):
function main (line 691) | def main(unused_argv):
FILE: youtube-8m-zhangteng/utils.py
function Dequantize (line 23) | def Dequantize(feat_vector, max_quantized_value=2, min_quantized_value=-2):
function MakeSummary (line 41) | def MakeSummary(name, value):
function AddGlobalStepSummary (line 50) | def AddGlobalStepSummary(summary_writer,
function AddEpochSummary (line 94) | def AddEpochSummary(summary_writer,
function GetListOfFeatureNamesAndSizes (line 140) | def GetListOfFeatureNamesAndSizes(feature_names, feature_sizes):
FILE: youtube-8m-zhangteng/video_level_models.py
class LogisticModel (line 61) | class LogisticModel(models.BaseModel):
method create_model (line 64) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unu...
class MoeModel (line 80) | class MoeModel(models.BaseModel):
method create_model (line 83) | def create_model(self,
class MoeDistillModel (line 157) | class MoeDistillModel(models.BaseModel):
method create_model (line 160) | def create_model(self,
class MoeDistillEmbeddingModel (line 233) | class MoeDistillEmbeddingModel(models.BaseModel):
method create_model (line 236) | def create_model(self,
class MoeDistillChainModel (line 286) | class MoeDistillChainModel(models.BaseModel):
method create_model (line 289) | def create_model(self,
class MoeDistillChainNormModel (line 359) | class MoeDistillChainNormModel(models.BaseModel):
method create_model (line 362) | def create_model(self,
class MoeDistillChainNorm2Model (line 434) | class MoeDistillChainNorm2Model(models.BaseModel):
method create_model (line 437) | def create_model(self,
class MoeDistillSplitModel (line 509) | class MoeDistillSplitModel(models.BaseModel):
method create_model (line 512) | def create_model(self,
class MoeDistillSplit2Model (line 584) | class MoeDistillSplit2Model(models.BaseModel):
method create_model (line 587) | def create_model(self,
class MoeDistillSplit3Model (line 661) | class MoeDistillSplit3Model(models.BaseModel):
method create_model (line 664) | def create_model(self,
class MoeDistillSplit4Model (line 774) | class MoeDistillSplit4Model(models.BaseModel):
method create_model (line 777) | def create_model(self,
class MoeSoftmaxModel (line 877) | class MoeSoftmaxModel(models.BaseModel):
method sub_model (line 879) | def sub_model(self,
method create_model (line 962) | def create_model(self,
class MoeNegativeModel (line 1020) | class MoeNegativeModel(models.BaseModel):
method create_model (line 1023) | def create_model(self,
class MoeMaxModel (line 1107) | class MoeMaxModel(models.BaseModel):
method create_model (line 1110) | def create_model(self,
class MoeMaxMixModel (line 1208) | class MoeMaxMixModel(models.BaseModel):
method create_model (line 1211) | def create_model(self,
class MoeKnowledgeModel (line 1331) | class MoeKnowledgeModel(models.BaseModel):
method create_model (line 1334) | def create_model(self,
class MoeMixModel (line 1441) | class MoeMixModel(models.BaseModel):
method create_model (line 1444) | def create_model(self,
class MoeMixExtendModel (line 1534) | class MoeMixExtendModel(models.BaseModel):
method create_model (line 1537) | def create_model(self,
class MoeMix2Model (line 1632) | class MoeMix2Model(models.BaseModel):
method create_model (line 1635) | def create_model(self,
class MoeMix3Model (line 1762) | class MoeMix3Model(models.BaseModel):
method create_model (line 1765) | def create_model(self,
class MoeMix4Model (line 1872) | class MoeMix4Model(models.BaseModel):
method create_model (line 1875) | def create_model(self,
class MoeNoiseModel (line 2046) | class MoeNoiseModel(models.BaseModel):
method create_model (line 2049) | def create_model(self,
class MoeMix5Model (line 2200) | class MoeMix5Model(models.BaseModel):
method create_model (line 2203) | def create_model(self,
class MoeExtendModel (line 2272) | class MoeExtendModel(models.BaseModel):
method create_model (line 2275) | def create_model(self,
class MoeExtendDistillChainModel (line 2332) | class MoeExtendDistillChainModel(models.BaseModel):
method create_model (line 2335) | def create_model(self,
class MoeExtendCombineModel (line 2402) | class MoeExtendCombineModel(models.BaseModel):
method create_model (line 2405) | def create_model(self,
class MoeExtendSoftmaxModel (line 2504) | class MoeExtendSoftmaxModel(models.BaseModel):
method create_model (line 2507) | def create_model(self,
class MoeSepModel (line 2573) | class MoeSepModel(models.BaseModel):
method create_model (line 2576) | def create_model(self,
class SimModel (line 2639) | class SimModel(models.BaseModel):
method create_model (line 2642) | def create_model(self,
class AutoEncoderModel (line 2707) | class AutoEncoderModel(models.BaseModel):
method create_model (line 2710) | def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unu...
FILE: youtube-8m-zhangteng/writers.py
class BaseWriter (line 21) | class BaseWriter(object):
method prepare_writer (line 24) | def prepare_writer(self, unused_filename_queue):
class YT8MAggregatedFeatureWriter (line 29) | class YT8MAggregatedFeatureWriter(BaseWriter):
method __init__ (line 37) | def __init__(self,
method prepare_writer (line 57) | def prepare_writer(self, filename_queue, batch_size=1024):
Condensed preview — 581 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (3,825K chars).
[
{
"path": ".gitignore",
"chars": 86,
"preview": "model/\nprediction/\n*.pdf\n*/__pycache__/\n*.out\n*.csv\n*.tfrecord\n*.pyc\n.*.swp\neda/data/\n"
},
{
"path": ".gitmodules",
"chars": 186,
"preview": "[submodule \"3rd_party/annoy\"]\n\tpath = 3rd_party/annoy\n\turl = git@github.com:spotify/annoy.git\n[submodule \"tensorflow\"]\n\t"
},
{
"path": "LICENSE",
"chars": 11354,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 2633,
"preview": "# The Monkeytyping Solution to the Youtube-8M Video Understanding Challenge\r\n\r\nThis is the solution repository of the 2n"
},
{
"path": "eda/explore.ipynb",
"chars": 1359974,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"out"
},
{
"path": "eda/vertical.tsv",
"chars": 33324,
"preview": "0\t0\n1\t1\n2\t0\n3\t2\n4\t1\n5\t2\n6\t2\n7\t2\n8\t3\n9\t2\n10\t4\n11\t3\n12\t5\n13\t2\n14\t2\n15\t2\n16\t2\n17\t3\n18\t6\n19\t0\n20\t2\n21\t2\n22\t7\n23\t8\n24\t2\n25\t9\n"
},
{
"path": "youtube-8m-ensemble/.vimrc",
"chars": 60,
"preview": "set tabstop=2\nset shiftwidth=2\nset expandtab\nset autoindent\n"
},
{
"path": "youtube-8m-ensemble/CONTRIBUTING.md",
"chars": 1494,
"preview": "# How to contribute\n\nWe are accepting patches and contributions to this project. To set expectations,\nthis project is pr"
},
{
"path": "youtube-8m-ensemble/LICENSE",
"chars": 11358,
"preview": "\n Apache License\n Version 2.0, January 2004\n "
},
{
"path": "youtube-8m-ensemble/README.md",
"chars": 18733,
"preview": "# YouTube-8M Tensorflow Starter Code\n\nThis repo contains starter code for training and evaluating machine learning\nmodel"
},
{
"path": "youtube-8m-ensemble/__init__.py",
"chars": 597,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/.vimrc",
"chars": 60,
"preview": "set tabstop=2\nset shiftwidth=2\nset expandtab\nset autoindent\n"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/__init__.py",
"chars": 505,
"preview": "from logistic_model import *\nfrom moe_model import *\nfrom attention_moe_model import *\nfrom attention_moe_matrix_model i"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/attention_linear_model.py",
"chars": 1474,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/attention_linmatrix_model.py",
"chars": 2084,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/attention_matrix_model.py",
"chars": 1846,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/attention_moe_matrix_model.py",
"chars": 2679,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/attention_moe_model.py",
"chars": 1665,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/attention_rectified_linear_model.py",
"chars": 1623,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/deep_combine_chain_model.py",
"chars": 3669,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/input_moe_model.py",
"chars": 1046,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/linear_regression_model.py",
"chars": 1062,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/logistic_model.py",
"chars": 908,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/matrix_regression_model.py",
"chars": 1322,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/mean_model.py",
"chars": 724,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/moe_model.py",
"chars": 2150,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/all_ensemble_models/nonunit_matrix_regression_model.py",
"chars": 1298,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport utils\nfrom tensorflow import flags\nimport tensorflow.contrib.sl"
},
{
"path": "youtube-8m-ensemble/average_precision_calculator.py",
"chars": 9766,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/check_distillation.py",
"chars": 8014,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/check_video_id.py",
"chars": 7820,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/check_video_id_match.py",
"chars": 8823,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/cloudml-gpu-distributed.yaml",
"chars": 188,
"preview": "trainingInput:\n runtimeVersion: \"1.0\"\n scaleTier: CUSTOM\n masterType: standard_gpu\n workerCount: 2\n workerType: sta"
},
{
"path": "youtube-8m-ensemble/cloudml-gpu.yaml",
"chars": 160,
"preview": "trainingInput:\n scaleTier: CUSTOM\n # standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs\n masterTyp"
},
{
"path": "youtube-8m-ensemble/data_augmentation.py",
"chars": 347,
"preview": "from tensorflow import flags\n\nflags.DEFINE_string(\"data_augmenter\", \"DefaultAugmenter\", \n \"how to pre"
},
{
"path": "youtube-8m-ensemble/ensemble_command.example",
"chars": 452,
"preview": "# bash [script] [model_name] [config]\n# use a different model name each time, make it understandable\n# create a new conf"
},
{
"path": "youtube-8m-ensemble/ensemble_level_models.py",
"chars": 1882,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/.vimrc",
"chars": 60,
"preview": "set tabstop=2\nset shiftwidth=2\nset expandtab\nset autoindent\n"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/after_submission_no1.conf",
"chars": 736,
"preview": "video_relabel_combine_chain\nvideo_very_deep_combine_chain\nlstmmemory_cell1024_layer2_moe8\nlstmmemory_cell2048_layer2_moe"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/after_submission_no2.conf",
"chars": 1285,
"preview": "video_relabel_combine_chain\nvideo_very_deep_combine_chain\nlstmmemory_cell1024_layer2_moe8\nlstmmemory_cell2048_layer2_moe"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/after_submission_no3.conf",
"chars": 1663,
"preview": "video_relabel_combine_chain\nvideo_very_deep_combine_chain\nlstmmemory_cell1024_layer2_moe8\nlstmmemory_cell2048_layer2_moe"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/after_submission_no4.conf",
"chars": 2601,
"preview": "video_relabel_combine_chain\nvideo_very_deep_combine_chain\nlstmmemory_cell1024_layer2_moe8\nlstmmemory_cell2048_layer2_moe"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/auto-preensemble-deep_combine_chain_model.sh",
"chars": 867,
"preview": "model=$1\nconf=$2\npart=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"se"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/auto-preensemble-matrix_model.sh",
"chars": 824,
"preview": "model=$1\nconf=$2\npart=$3\npostfix=$4\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/check-video_id.sh",
"chars": 483,
"preview": "conf=$1\npart=$2\n\nvalidate_path=/Youtube-8M/model_predictions/${part}\nvalidate_data_patterns=\"\"\nfor d in $(cat $conf); do"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/check-video_id_match.sh",
"chars": 578,
"preview": "conf=$1\npart=$2\n\nvalidate_path=/Youtube-8M/model_predictions_local/${part}\nvalidate_data_patterns=\"\"\nfor d in $(cat $con"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/combine-tfrecords-frame-v2.sh",
"chars": 578,
"preview": "#!/bin/bash\nfile_num_mod=$1\n\ndata_path=/Youtube-8M/model_predictions_x32/train\n\ninput_data_pattern=\"/Youtube-8M/data/fra"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/combine-tfrecords-frame.sh",
"chars": 517,
"preview": "#!/bin/bash\n\ndata_path=/Youtube-8M/model_predictions/train\n\ninput_data_pattern=\"/Youtube-8M/data/frame/train/train*.tfre"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/combine-tfrecords-video-v2.sh",
"chars": 527,
"preview": "#!/bin/bash\n\ndata_path=/Youtube-8M/model_predictions_local/train\n\ninput_data_pattern=\"/Youtube-8M/data/video/train/*.tfr"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/combine-tfrecords-video.sh",
"chars": 518,
"preview": "#!/bin/bash\n\ndata_path=/Youtube-8M/model_predictions/train\n\ninput_data_pattern=\"/Youtube-8M/data/video/train/train*.tfre"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no1.conf",
"chars": 116,
"preview": "lstmmem1024_layer2_moe4_deep_combine_chain_add_length\nlstmmemory_cell2048_layer2_moe4\nvideo_very_deep_combine_chain\n"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no10.conf",
"chars": 1155,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnnlstmmemory1024_moe8\ndeep_cnn_deep_combine_chain\nframehop"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no11.conf",
"chars": 1262,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no12.conf",
"chars": 1344,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no13.conf",
"chars": 2278,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no14.conf",
"chars": 1605,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no15.conf",
"chars": 1671,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no16.conf",
"chars": 1656,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no17.conf",
"chars": 1013,
"preview": "cnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ndistillation_cnn_dcc_boosting/sub_model_1\ndi"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no18.conf",
"chars": 1854,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no19.conf",
"chars": 1991,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no2.conf",
"chars": 283,
"preview": "cnn_deep_combine_chain\ndeep_cnn_deep_combine_chain\nlstm_attention8_max\nlstmmem1024_layer2_moe4_deep_combine_chain_add_le"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no20.conf",
"chars": 1709,
"preview": "cnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ndistillation_cnn_dcc_boosting/sub_model_1\ndi"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no21.conf",
"chars": 2601,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnn_de"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no3.conf",
"chars": 412,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnnlstmmemory1024_moe8\ndeep_cnn_deep_combine_chain\nframehop"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no4.conf",
"chars": 452,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnnlstmmemory1024_moe8\ndeep_cnn_deep_combine_chain\nframehop"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no5.conf",
"chars": 368,
"preview": "cnn_deep_combine_chain\ncnnlstmmemory1024_moe8\ndeep_cnn_deep_combine_chain\nframehop_lstm\nlstm_attention8_max\nlstm_cnn_dee"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no6.conf",
"chars": 513,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnnlstmmemory1024_moe8\ndeep_cnn_deep_combine_chain\nframehop"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no7.conf",
"chars": 855,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnnlstmmemory1024_moe8\ndeep_cnn_deep_combine_chain\nframehop"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no8.conf",
"chars": 591,
"preview": "cnn_deep_combine_chain\ncnn_deep_combine_chain_bagging/ensemble_matrix_model\ncnnlstmmemory1024_moe8\nframehop_lstm\nlstm_at"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/ensemble_no9.conf",
"chars": 931,
"preview": "attentionlstm_moe4\nbiunilstm1024_moe4\ncnn_deep_combine_chain\ncnnlstmmemory1024_moe8\ndeep_cnn_deep_combine_chain\nframehop"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-attention_linear_model.sh",
"chars": 1297,
"preview": "model=$1\nconf=$2\nstart=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"s"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-attention_linmatrix_model.sh",
"chars": 1388,
"preview": "model=$1\nconf=$2\nstart=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"s"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-attention_matrix_model.sh",
"chars": 1344,
"preview": "model=$1\nconf=$2\nstart=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"s"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-attention_moe_matrix_model.sh",
"chars": 1392,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-attention_moe_model.sh",
"chars": 1339,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-attention_rectified_linear_model.sh",
"chars": 1306,
"preview": "model=$1\nconf=$2\nstart=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"s"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-deep_combine_chain_model.sh",
"chars": 1434,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-input_moe_model.sh",
"chars": 1244,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-linear_model.sh",
"chars": 1128,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-matrix_model.sh",
"chars": 1149,
"preview": "model=$1\nconf=$2\npostfix=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo "
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-mean_model.sh",
"chars": 751,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-moe_model.sh",
"chars": 1115,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/eval-nonunit_matrix_model.sh",
"chars": 1135,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/explore-mean_model.log",
"chars": 49980,
"preview": "single model\n------------------------------------------------\ncnn_deep_combine_chain\nGAP = 0.816665221681\n--------------"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/explore-mean_model.sh",
"chars": 5362,
"preview": "train_path=/Youtube-8M/model_predictions/ensemble_train\n\nfor d in $(ls $train_path | sort); do\n train_data_patterns=\"${"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/final_submission.conf",
"chars": 12601,
"preview": "# This is not a real configuration file that you could use with ensemble_scripts\n# This is merely for the sake of unders"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-attention_linear_model.sh",
"chars": 718,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\ntest_path=/Youtube-8M/model_predictions/test\n\ntest_data_patterns=\"\"\nfor d in $(cat $conf)"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-attention_linmatrix_model.sh",
"chars": 755,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\ntest_path=/Youtube-8M/model_predictions/test\n\ntest_data_patterns=\"\"\nfor d in $(cat $conf)"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-attention_matrix_model.sh",
"chars": 751,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\ntest_path=/Youtube-8M/model_predictions/test\n\ntest_data_patterns=\"\"\nfor d in $(cat $conf)"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-attention_moe_matrix_model.sh",
"chars": 786,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\ntest_path=/Youtube-8M/model_predictions/test\n\ntest_data_patterns=\"\"\nfor d in $(cat $conf)"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-attention_moe_model.sh",
"chars": 747,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\ntest_path=/Youtube-8M/model_predictions/test\n\ntest_data_patterns=\"\"\nfor d in $(cat $conf)"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-attention_rectified_linear_model.sh",
"chars": 727,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\ntest_path=/Youtube-8M/model_predictions/test\n\ntest_data_patterns=\"\"\nfor d in $(cat $conf)"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-linear_model.sh",
"chars": 766,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n ec"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-matrix_model.sh",
"chars": 794,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n ec"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-mean_model.sh",
"chars": 730,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/infer-moe_model.sh",
"chars": 741,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/make-bagging-of-ensembles.sh",
"chars": 3839,
"preview": "#!/bin/bash\nDEFAULT_GPU_ID=0\n\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_VISIBL"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/make-virtual-groups.sh",
"chars": 1092,
"preview": "#!/bin/bash\n\ngroup_dir=\"../model/virtual_grouping\"\n\nfor file in $(ls $group_dir); do\n if [[ $file =~ ^virtual_group ]] "
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/preensemble-attention_matrix_model.sh",
"chars": 1024,
"preview": "model=$1\nconf=$2\npart=$3\ncheckpoint=$4\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/preensemble-matrix_model.sh",
"chars": 854,
"preview": "model=$1\nconf=$2\npart=$3\ncheckpoint=$4\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/preensemble-mean_model.sh",
"chars": 826,
"preview": "model=$1\nconf=$2\npart=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"se"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-attention_linear_model.sh",
"chars": 934,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-attention_linmatrix_model.sh",
"chars": 972,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-attention_matrix_model.sh",
"chars": 968,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-attention_moe_matrix_model.sh",
"chars": 1002,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-attention_moe_model.sh",
"chars": 965,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-attention_rectified_linear_model.sh",
"chars": 943,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-deep_combine_chain_model.sh",
"chars": 1157,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-input_moe_model.sh",
"chars": 863,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-linear_model.sh",
"chars": 762,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-matrix_model.sh",
"chars": 785,
"preview": "model=$1\nconf=$2\npostfix=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo "
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-matrix_model_lr.sh",
"chars": 819,
"preview": "model=$1\nconf=$2\nlearn_rate=$3\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n ec"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-mean_model.sh",
"chars": 705,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-moe_model.sh",
"chars": 749,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/ensemble_scripts/train-nonunit_matrix_model.sh",
"chars": 769,
"preview": "model=$1\nconf=$2\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n echo \"set CUDA_V"
},
{
"path": "youtube-8m-ensemble/eval.py",
"chars": 12152,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/eval_util.py",
"chars": 9544,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/feature_transform.py",
"chars": 503,
"preview": "from tensorflow import flags\n\nflags.DEFINE_string(\"feature_transformer\", \"DefaultTransformer\", \n \"how"
},
{
"path": "youtube-8m-ensemble/inference-combine-tfrecords-frame.py",
"chars": 13108,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/inference-combine-tfrecords-video.py",
"chars": 12768,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/inference-pre-ensemble.py",
"chars": 12606,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/inference.py",
"chars": 9584,
"preview": "#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance wit"
},
{
"path": "youtube-8m-ensemble/losses.py",
"chars": 8295,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/mean_average_precision_calculator.py",
"chars": 4065,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/model_selection_scripts/.vimrc",
"chars": 60,
"preview": "set tabstop=2\nset shiftwidth=2\nset expandtab\nset autoindent\n"
},
{
"path": "youtube-8m-ensemble/model_selection_scripts/extend-step-mean_model.sh",
"chars": 742,
"preview": "#!/bin/bash\n\nDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" && pwd )\"\nmodel_name=\"$1\"\ncandidates_conf=\"$2\"\n\ntrain_path=/Y"
},
{
"path": "youtube-8m-ensemble/model_selection_scripts/get_extend_candidates.py",
"chars": 878,
"preview": "import sys\nfrom tensorflow import flags\n\nFLAGS = flags.FLAGS\n\nif __name__==\"__main__\":\n flags.DEFINE_string(\"top_k_file"
},
{
"path": "youtube-8m-ensemble/model_selection_scripts/get_patterns.py",
"chars": 530,
"preview": "import sys\nfrom tensorflow import flags\n\nFLAGS = flags.FLAGS\n\nif __name__==\"__main__\":\n flags.DEFINE_string(\"train_path"
},
{
"path": "youtube-8m-ensemble/model_selection_scripts/get_top_k.py",
"chars": 855,
"preview": "import sys\nfrom tensorflow import flags\n\nFLAGS = flags.FLAGS\n\nif __name__==\"__main__\":\n flags.DEFINE_string(\"log_file\","
},
{
"path": "youtube-8m-ensemble/model_selection_scripts/greedy-selection-mean_model.sh",
"chars": 1250,
"preview": "#!/bin/bash\n\nDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" && pwd )\"\nmodel_name=$1\n\nextend_step_script=\"${DIR}/extend-st"
},
{
"path": "youtube-8m-ensemble/model_utils.py",
"chars": 3467,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/models.py",
"chars": 824,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/readers.py",
"chars": 5484,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/top_k_scripts/eval-attention_matrix_model.sh",
"chars": 1363,
"preview": "model=$1\nconf=$2\nmoe=$3\natt=$4\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n ec"
},
{
"path": "youtube-8m-ensemble/top_k_scripts/infer-attention_matrix_model.sh",
"chars": 751,
"preview": "model=$1\nconf=$2\ncheckpoint=$3\ntest_path=/Youtube-8M/model_predictions/test\n\ntest_data_patterns=\"\"\nfor d in $(cat $conf)"
},
{
"path": "youtube-8m-ensemble/top_k_scripts/preensemble-attention_matrix_model.sh",
"chars": 1024,
"preview": "model=$1\nconf=$2\npart=$3\ncheckpoint=$4\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU"
},
{
"path": "youtube-8m-ensemble/top_k_scripts/run_top_k.sh",
"chars": 324,
"preview": "#!/bin/bash\n\nfor i in 8 12 16 20; do \n bash top_k_scripts/train-attention_matrix_model.sh model_selection/top_${i}_mode"
},
{
"path": "youtube-8m-ensemble/top_k_scripts/train-attention_matrix_model.sh",
"chars": 992,
"preview": "model=$1\nconf=$2\nmoe=$3\natt=$4\n\nDEFAULT_GPU_ID=0\nif [ -z ${CUDA_VISIBLE_DEVICES+x} ]; then\n GPU_ID=$DEFAULT_GPU_ID\n ec"
},
{
"path": "youtube-8m-ensemble/train.py",
"chars": 25849,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-ensemble/training_utils/del.py",
"chars": 417,
"preview": "import os\n\ncheck = {}\ncheck_list = []\nfor filename in os.listdir(\".\"):\n if filename.endswith(\"meta\"):\n "
},
{
"path": "youtube-8m-ensemble/training_utils/sample_conf.py",
"chars": 819,
"preview": "import random\nfrom datetime import datetime\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nif"
},
{
"path": "youtube-8m-ensemble/training_utils/sample_freq.py",
"chars": 1049,
"preview": "import random\nfrom datetime import datetime\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nif"
},
{
"path": "youtube-8m-ensemble/training_utils/select.py",
"chars": 477,
"preview": "import os\nimport sys\n\nevery = int(sys.argv[1])\n\ncheck = {}\ncheck_list = []\nfor filename in os.listdir(\".\"):\n if f"
},
{
"path": "youtube-8m-ensemble/utils.py",
"chars": 6351,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-wangheda/.vimrc",
"chars": 73,
"preview": "set tabstop=2\nset shiftwidth=2\nset expandtab\nset autoindent\ncolor desert\n"
},
{
"path": "youtube-8m-wangheda/CONTRIBUTING.md",
"chars": 1494,
"preview": "# How to contribute\n\nWe are accepting patches and contributions to this project. To set expectations,\nthis project is pr"
},
{
"path": "youtube-8m-wangheda/LICENSE",
"chars": 11358,
"preview": "\n Apache License\n Version 2.0, January 2004\n "
},
{
"path": "youtube-8m-wangheda/README.md",
"chars": 18733,
"preview": "# YouTube-8M Tensorflow Starter Code\n\nThis repo contains starter code for training and evaluating machine learning\nmodel"
},
{
"path": "youtube-8m-wangheda/__init__.py",
"chars": 597,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "youtube-8m-wangheda/all_data_augmentation/__init__.py",
"chars": 160,
"preview": "\nfrom default_augmenter import *\nfrom half_augmenter import *\nfrom half_video_augmenter import *\nfrom noise_augmenter im"
},
{
"path": "youtube-8m-wangheda/all_data_augmentation/clipping_augmenter.py",
"chars": 1221,
"preview": "\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nclass ClippingAugmenter:\n \"\"\"This only works"
},
{
"path": "youtube-8m-wangheda/all_data_augmentation/default_augmenter.py",
"chars": 302,
"preview": "\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nclass DefaultAugmenter:\n \"\"\"This only works "
},
{
"path": "youtube-8m-wangheda/all_data_augmentation/half_augmenter.py",
"chars": 1787,
"preview": "\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nclass HalfAugmenter:\n \"\"\"This only works wit"
},
{
"path": "youtube-8m-wangheda/all_data_augmentation/half_video_augmenter.py",
"chars": 2236,
"preview": "\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nclass HalfVideoAugmenter:\n \"\"\"This only work"
},
{
"path": "youtube-8m-wangheda/all_data_augmentation/noise_augmenter.py",
"chars": 505,
"preview": "\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nclass NoiseAugmenter:\n \"\"\"This only works wi"
},
{
"path": "youtube-8m-wangheda/all_feature_transform/__init__.py",
"chars": 173,
"preview": "\nfrom default_transformer import *\nfrom identical_transformer import *\nfrom engineer_transformer import *\nfrom avg_trans"
},
{
"path": "youtube-8m-wangheda/all_feature_transform/avg_transformer.py",
"chars": 575,
"preview": "import tensorflow as tf\n\nclass AvgTransformer:\n def transform(self, model_input_raw, num_frames, **unused_params):\n "
},
{
"path": "youtube-8m-wangheda/all_feature_transform/default_transformer.py",
"chars": 278,
"preview": "\nimport tensorflow as tf\n\nclass DefaultTransformer:\n def transform(self, model_input_raw, num_frames, **unused_params):"
},
{
"path": "youtube-8m-wangheda/all_feature_transform/engineer_transformer.py",
"chars": 2640,
"preview": "\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nclass EngineerTransformer:"
},
{
"path": "youtube-8m-wangheda/all_feature_transform/identical_transformer.py",
"chars": 162,
"preview": "\nimport tensorflow as tf\n\nclass IdenticalTransformer:\n def transform(self, model_input_raw, num_frames, **unused_params"
},
{
"path": "youtube-8m-wangheda/all_feature_transform/resolution_transformer.py",
"chars": 1140,
"preview": "\nimport tensorflow as tf\nfrom tensorflow import flags\nFLAGS = flags.FLAGS\n\nclass ResolutionTransformer:\n def resolution"
},
{
"path": "youtube-8m-wangheda/all_frame_models/.vimrc",
"chars": 70,
"preview": "syntax on\nset tabstop=2\nset shiftwidth=2\nset expandtab\nset autoindent\n"
},
{
"path": "youtube-8m-wangheda/all_frame_models/__init__.py",
"chars": 2348,
"preview": "from bilstm_model import *\nfrom biunilstm_model import *\nfrom cnn_kmax_model import *\nfrom dbof_model import *\nfrom fram"
},
{
"path": "youtube-8m-wangheda/all_frame_models/bilstm_model.py",
"chars": 2412,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/biunilstm_model.py",
"chars": 2542,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/cnn_deep_combine_chain_model.py",
"chars": 5578,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/cnn_kmax_model.py",
"chars": 3385,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/cnn_lstm_memory_model.py",
"chars": 3248,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/cnn_lstm_memory_multitask_model.py",
"chars": 4575,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/cnn_lstm_memory_normalization_model.py",
"chars": 3907,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/cnn_model.py",
"chars": 3391,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/dbof_model.py",
"chars": 4597,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/deep_cnn_deep_combine_chain_model.py",
"chars": 7360,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/deep_lstm_model.py",
"chars": 2351,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/distillchain_cnn_deep_combine_chain_model.py",
"chars": 6169,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/distillchain_lstm_attention_max_pooling_model.py",
"chars": 4761,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/distillchain_lstm_cnn_deep_combine_chain_model.py",
"chars": 7770,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/distillchain_lstm_memory_deep_combine_chain_model.py",
"chars": 5282,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/distillchain_lstm_parallel_finaloutput_model.py",
"chars": 3235,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/distillchain_multiscale_cnn_lstm_model.py",
"chars": 5442,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/frame_seg_model.py",
"chars": 2111,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/framehop_lstm_memory_deep_combine_chain_model.py",
"chars": 7986,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/framehop_lstm_memory_model.py",
"chars": 5533,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/gru_pooling_model.py",
"chars": 2128,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/gru_with_pooling_model.py",
"chars": 2196,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/layernorm_lstm_memory_model.py",
"chars": 2540,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/logistic_model.py",
"chars": 1704,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_advanced_model.py",
"chars": 1954,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_attention_lstm_model.py",
"chars": 4037,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_attention_max_pooling_model.py",
"chars": 4036,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_attention_model.py",
"chars": 3227,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_auxloss_deep_combine_chain_model.py",
"chars": 5187,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_cnn_deep_combine_chain_model.py",
"chars": 7106,
"preview": "import math\nimport models\nimport tensorflow as tf\nimport numpy as np\nimport utils\nfrom tensorflow import flags\nimport te"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_divided_model.py",
"chars": 2062,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_look_back_model.py",
"chars": 3328,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_memory_chain_model.py",
"chars": 2559,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_memory_deep_chain_model.py",
"chars": 4405,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_memory_input_chain_model.py",
"chars": 2987,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_memory_model.py",
"chars": 2698,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
},
{
"path": "youtube-8m-wangheda/all_frame_models/lstm_memory_multitask_model.py",
"chars": 2520,
"preview": "import sys\nimport models\nimport model_utils\nimport math\nimport numpy as np\nimport video_level_models\nimport tensorflow a"
}
]
// ... and 381 more files (download for full content)
About this extraction
This page contains the full source code of the wangheda/youtube-8m GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 581 files (3.4 MB), approximately 928.9k tokens, and a symbol index with 1145 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.