diff --git "a/fold_0/logs.models.fold_0.ENCSR856XGY/logfile.modelling.fold_0.ENCSR856XGY.stdout_v1.txt" "b/fold_0/logs.models.fold_0.ENCSR856XGY/logfile.modelling.fold_0.ENCSR856XGY.stdout_v1.txt"
new file mode 100644--- /dev/null
+++ "b/fold_0/logs.models.fold_0.ENCSR856XGY/logfile.modelling.fold_0.ENCSR856XGY.stdout_v1.txt"
@@ -0,0 +1,440 @@
+2022-03-23_22-50-39: python /home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/helpers/hyperparameters/find_chrombpnet_hyperparams.py \
+       --genome=/scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa \
+       --bigwig=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//preprocessing/bigWigs/ENCSR856XGY.bigWig \
+       --peaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//preprocessing/downloads/peaks.bed.gz \
+       --nonpeaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//negatives_data/negatives_with_summit.bed \
+       --outlier_threshold=0.99 \
+       --chr_fold_path=/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_0.json \
+       --negative_sampling_ratio=0.1 \
+       --inputlen=2114 \
+       --outputlen=1000 \
+       --max_jitter=500 \
+       --filters=512 \
+       --n_dilation_layers=8 \
+       --bias_model_path=/scratch/groups/akundaje/anusri/chromatin_atlas/reference/ENCSR880CUB_bias_0.9.h5 \
+       --output_dir=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias/ 
+evaluating hyperparameters on the following chromosomes ['chr2', 'chr4', 'chr5', 'chr7', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr21', 'chr22', 'chrX', 'chrY', 'chr8', 'chr20']
+Number of peaks input:  158362
+Number of peaks filtered because the input/output is on the edge:  0
+Number of peaks being used:  158362
+Number of peaks input:  41638
+Number of peaks filtered because the input/output is on the edge:  0
+Number of peaks being used:  41638
+Number of non peaks input:  158362
+Number of non peaks filtered because the input/output is on the edge:  0
+Number of non peaks being used:  158362
+Number of non peaks input:  41637
+Number of non peaks filtered because the input/output is on the edge:  0
+Number of non peaks being used:  41637
+Number of peaks after removing outliers:  156620
+Number of nonpeaks after removing outliers:  139960
+got the model
+Model: "model"
+__________________________________________________________________________________________________
+Layer (type)                    Output Shape         Param #     Connected to                     
+==================================================================================================
+sequence (InputLayer)           [(None, 2114, 4)]    0                                            
+__________________________________________________________________________________________________
+bpnet_1st_conv (Conv1D)         (None, 2094, 128)    10880       sequence[0][0]                   
+__________________________________________________________________________________________________
+bpnet_1conv (Conv1D)            (None, 2090, 128)    49280       bpnet_1st_conv[0][0]             
+__________________________________________________________________________________________________
+bpnet_1crop (Cropping1D)        (None, 2090, 128)    0           bpnet_1st_conv[0][0]             
+__________________________________________________________________________________________________
+add (Add)                       (None, 2090, 128)    0           bpnet_1conv[0][0]                
+                                                                 bpnet_1crop[0][0]                
+__________________________________________________________________________________________________
+bpnet_2conv (Conv1D)            (None, 2082, 128)    49280       add[0][0]                        
+__________________________________________________________________________________________________
+bpnet_2crop (Cropping1D)        (None, 2082, 128)    0           add[0][0]                        
+__________________________________________________________________________________________________
+add_1 (Add)                     (None, 2082, 128)    0           bpnet_2conv[0][0]                
+                                                                 bpnet_2crop[0][0]                
+__________________________________________________________________________________________________
+bpnet_3conv (Conv1D)            (None, 2066, 128)    49280       add_1[0][0]                      
+__________________________________________________________________________________________________
+bpnet_3crop (Cropping1D)        (None, 2066, 128)    0           add_1[0][0]                      
+__________________________________________________________________________________________________
+add_2 (Add)                     (None, 2066, 128)    0           bpnet_3conv[0][0]                
+                                                                 bpnet_3crop[0][0]                
+__________________________________________________________________________________________________
+bpnet_4conv (Conv1D)            (None, 2034, 128)    49280       add_2[0][0]                      
+__________________________________________________________________________________________________
+bpnet_4crop (Cropping1D)        (None, 2034, 128)    0           add_2[0][0]                      
+__________________________________________________________________________________________________
+add_3 (Add)                     (None, 2034, 128)    0           bpnet_4conv[0][0]                
+                                                                 bpnet_4crop[0][0]                
+__________________________________________________________________________________________________
+prof_out_precrop (Conv1D)       (None, 1960, 1)      9601        add_3[0][0]                      
+__________________________________________________________________________________________________
+logits_profile_predictions_pref (None, 1000, 1)      0           prof_out_precrop[0][0]           
+__________________________________________________________________________________________________
+gap (GlobalAveragePooling1D)    (None, 128)          0           add_3[0][0]                      
+__________________________________________________________________________________________________
+logits_profile_predictions (Fla (None, 1000)         0           logits_profile_predictions_prefla
+__________________________________________________________________________________________________
+logcount_predictions (Dense)    (None, 1)            129         gap[0][0]                        
+==================================================================================================
+Total params: 217,730
+Trainable params: 217,730
+Non-trainable params: 0
+__________________________________________________________________________________________________
+Predicting within adjust counts
+
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+2022-03-23_22-59-58: python /home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/train.py \
+       --genome=/scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa \
+       --bigwig=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//preprocessing/bigWigs/ENCSR856XGY.bigWig \
+       --peaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.peaks.bed \
+       --nonpeaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.nonpeaks.bed \
+       --params=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//chrombpnet_model_params.tsv \
+       --output_prefix=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//chrombpnet \
+       --chr_fold_path=/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_0.json \
+       --batch_size=64 \
+       --architecture_from_file=/home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/models/chrombpnet_with_bias_model.py \
+       --trackables logcount_predictions_loss loss logits_profile_predictions_loss val_logcount_predictions_loss val_loss val_logits_profile_predictions_loss
+{'counts_loss_weight': '18.5', 'filters': '512', 'n_dil_layers': '8', 'bias_model_path': '/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias/bias_model_scaled.h5', 'inputlen': '2114', 'outputlen': '1000', 'max_jitter': '500', 'chr_fold_path': '/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_0.json', 'negative_sampling_ratio': '0.1'}
+got the model
+loading peaks...
+got split:train for bed regions:(141697, 10)
+loading nonpeaks...
+got split:train for bed regions:(126132, 10)
+loading peaks...
+got split:valid for bed regions:(14923, 10)
+loading nonpeaks...
+got split:valid for bed regions:(13828, 10)
+Epoch 1/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 973.9578 - logits_profile_predictions_loss: 690.8571 - logcount_predictions_loss: 15.3027
2436/2436 [==============================] - 396s 160ms/step - loss: 973.8565 - logits_profile_predictions_loss: 690.8484 - logcount_predictions_loss: 15.2977 - val_loss: 652.5536 - val_logits_profile_predictions_loss: 643.9862 - val_logcount_predictions_loss: 0.4631
+
+Epoch 00001: val_loss improved from inf to 652.55365, saving model to /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias/chrombpnet.h5
+Epoch 2/50
+
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2435/2436 [============================>.] - ETA: 0s - loss: 645.5808 - logits_profile_predictions_loss: 637.4268 - logcount_predictions_loss: 0.4408
2436/2436 [==============================] - ETA: 0s - loss: 645.5786 - logits_profile_predictions_loss: 637.4247 - logcount_predictions_loss: 0.4408
2436/2436 [==============================] - 387s 159ms/step - loss: 645.5764 - logits_profile_predictions_loss: 637.4226 - logcount_predictions_loss: 0.4408 - val_loss: 638.7791 - val_logits_profile_predictions_loss: 631.0373 - val_logcount_predictions_loss: 0.4185
+
+Epoch 00002: val_loss improved from 652.55365 to 638.77905, saving model to /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias/chrombpnet.h5
+Epoch 3/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 632.0756 - logits_profile_predictions_loss: 624.8035 - logcount_predictions_loss: 0.3931
2436/2436 [==============================] - 387s 159ms/step - loss: 632.0750 - logits_profile_predictions_loss: 624.8029 - logcount_predictions_loss: 0.3931 - val_loss: 631.0800 - val_logits_profile_predictions_loss: 625.1251 - val_logcount_predictions_loss: 0.3219
+
+Epoch 00003: val_loss improved from 638.77905 to 631.07996, saving model to /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias/chrombpnet.h5
+Epoch 4/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 625.7873 - logits_profile_predictions_loss: 619.1380 - logcount_predictions_loss: 0.3594
2436/2436 [==============================] - 387s 159ms/step - loss: 625.7867 - logits_profile_predictions_loss: 619.1375 - logcount_predictions_loss: 0.3594 - val_loss: 635.1997 - val_logits_profile_predictions_loss: 629.2040 - val_logcount_predictions_loss: 0.3241
+
+Epoch 00004: val_loss did not improve from 631.07996
+Epoch 5/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 621.1038 - logits_profile_predictions_loss: 614.8108 - logcount_predictions_loss: 0.3402
2436/2436 [==============================] - 387s 159ms/step - loss: 621.1036 - logits_profile_predictions_loss: 614.8106 - logcount_predictions_loss: 0.3402 - val_loss: 621.2945 - val_logits_profile_predictions_loss: 616.0719 - val_logcount_predictions_loss: 0.2823
+
+Epoch 00005: val_loss improved from 631.07996 to 621.29449, saving model to /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias/chrombpnet.h5
+Epoch 6/50
+
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2436/2436 [==============================] - 387s 159ms/step - loss: 616.9763 - logits_profile_predictions_loss: 611.0471 - logcount_predictions_loss: 0.3205 - val_loss: 625.4188 - val_logits_profile_predictions_loss: 620.3799 - val_logcount_predictions_loss: 0.2723
+
+Epoch 00006: val_loss did not improve from 621.29449
+Epoch 7/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 612.1955 - logits_profile_predictions_loss: 606.5244 - logcount_predictions_loss: 0.3065
2436/2436 [==============================] - 387s 159ms/step - loss: 612.1959 - logits_profile_predictions_loss: 606.5248 - logcount_predictions_loss: 0.3065 - val_loss: 622.9989 - val_logits_profile_predictions_loss: 618.0262 - val_logcount_predictions_loss: 0.2688
+
+Epoch 00007: val_loss did not improve from 621.29449
+Epoch 8/50
+
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2436/2436 [==============================] - 387s 159ms/step - loss: 610.2231 - logits_profile_predictions_loss: 604.7373 - logcount_predictions_loss: 0.2965 - val_loss: 622.4803 - val_logits_profile_predictions_loss: 617.3801 - val_logcount_predictions_loss: 0.2757
+
+Epoch 00008: val_loss did not improve from 621.29449
+Epoch 9/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 603.0135 - logits_profile_predictions_loss: 598.0943 - logcount_predictions_loss: 0.2659
2436/2436 [==============================] - 387s 159ms/step - loss: 603.0130 - logits_profile_predictions_loss: 598.0938 - logcount_predictions_loss: 0.2659 - val_loss: 614.9941 - val_logits_profile_predictions_loss: 610.2190 - val_logcount_predictions_loss: 0.2581
+
+Epoch 00009: val_loss improved from 621.29449 to 614.99408, saving model to /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias/chrombpnet.h5
+Epoch 10/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 598.9025 - logits_profile_predictions_loss: 594.1638 - logcount_predictions_loss: 0.2561
2436/2436 [==============================] - 387s 159ms/step - loss: 598.9023 - logits_profile_predictions_loss: 594.1636 - logcount_predictions_loss: 0.2561 - val_loss: 615.5069 - val_logits_profile_predictions_loss: 610.6809 - val_logcount_predictions_loss: 0.2609
+
+Epoch 00010: val_loss did not improve from 614.99408
+Epoch 11/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 593.8317 - logits_profile_predictions_loss: 589.1703 - logcount_predictions_loss: 0.2520
2436/2436 [==============================] - 387s 159ms/step - loss: 593.8320 - logits_profile_predictions_loss: 589.1707 - logcount_predictions_loss: 0.2520 - val_loss: 619.8680 - val_logits_profile_predictions_loss: 615.3439 - val_logcount_predictions_loss: 0.2445
+
+Epoch 00011: val_loss did not improve from 614.99408
+Epoch 12/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 591.0577 - logits_profile_predictions_loss: 586.5918 - logcount_predictions_loss: 0.2414
2436/2436 [==============================] - 387s 159ms/step - loss: 591.0583 - logits_profile_predictions_loss: 586.5923 - logcount_predictions_loss: 0.2414 - val_loss: 618.1663 - val_logits_profile_predictions_loss: 613.5744 - val_logcount_predictions_loss: 0.2482
+
+Epoch 00012: val_loss did not improve from 614.99408
+Epoch 13/50
+
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2436/2436 [==============================] - ETA: 0s - loss: 586.1026 - logits_profile_predictions_loss: 581.8743 - logcount_predictions_loss: 0.2285
2436/2436 [==============================] - 387s 159ms/step - loss: 586.1027 - logits_profile_predictions_loss: 581.8744 - logcount_predictions_loss: 0.2285 - val_loss: 620.3196 - val_logits_profile_predictions_loss: 615.6031 - val_logcount_predictions_loss: 0.2550
+
+Epoch 00013: val_loss did not improve from 614.99408
+Epoch 14/50
+
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2384/2436 [============================>.] - ETA: 7s - loss: 583.0003 - logits_profile_predictions_loss: 578.8693 - logcount_predictions_loss: 0.2233
2385/2436 [============================>.] - ETA: 7s - loss: 583.0004 - logits_profile_predictions_loss: 578.8693 - logcount_predictions_loss: 0.2233
2386/2436 [============================>.] - ETA: 7s - loss: 583.0004 - logits_profile_predictions_loss: 578.8694 - logcount_predictions_loss: 0.2233
2387/2436 [============================>.] - ETA: 7s - loss: 583.0005 - logits_profile_predictions_loss: 578.8694 - logcount_predictions_loss: 0.2233
2388/2436 [============================>.] - ETA: 7s - loss: 583.0005 - logits_profile_predictions_loss: 578.8695 - logcount_predictions_loss: 0.2233
2389/2436 [============================>.] - ETA: 7s - loss: 583.0006 - logits_profile_predictions_loss: 578.8695 - logcount_predictions_loss: 0.2233
2390/2436 [============================>.] - ETA: 6s - loss: 583.0006 - logits_profile_predictions_loss: 578.8696 - logcount_predictions_loss: 0.2233
2391/2436 [============================>.] - ETA: 6s - loss: 583.0007 - logits_profile_predictions_loss: 578.8696 - logcount_predictions_loss: 0.2233
2392/2436 [============================>.] - ETA: 6s - loss: 583.0008 - logits_profile_predictions_loss: 578.8697 - logcount_predictions_loss: 0.2233
2393/2436 [============================>.] - ETA: 6s - loss: 583.0009 - logits_profile_predictions_loss: 578.8698 - logcount_predictions_loss: 0.2233
2394/2436 [============================>.] - ETA: 6s - loss: 583.0010 - logits_profile_predictions_loss: 578.8699 - logcount_predictions_loss: 0.2233
2395/2436 [============================>.] - ETA: 6s - loss: 583.0011 - logits_profile_predictions_loss: 578.8700 - logcount_predictions_loss: 0.2233
2396/2436 [============================>.] - ETA: 6s - loss: 583.0012 - logits_profile_predictions_loss: 578.8701 - logcount_predictions_loss: 0.2233
2397/2436 [============================>.] - ETA: 5s - loss: 583.0014 - logits_profile_predictions_loss: 578.8703 - logcount_predictions_loss: 0.2233
2398/2436 [============================>.] - ETA: 5s - loss: 583.0015 - logits_profile_predictions_loss: 578.8704 - logcount_predictions_loss: 0.2233
2399/2436 [============================>.] - ETA: 5s - loss: 583.0017 - logits_profile_predictions_loss: 578.8706 - logcount_predictions_loss: 0.2233
2400/2436 [============================>.] - ETA: 5s - loss: 583.0018 - logits_profile_predictions_loss: 578.8707 - logcount_predictions_loss: 0.2233
2401/2436 [============================>.] - ETA: 5s - loss: 583.0020 - logits_profile_predictions_loss: 578.8709 - logcount_predictions_loss: 0.2233
2402/2436 [============================>.] - ETA: 5s - loss: 583.0022 - logits_profile_predictions_loss: 578.8710 - logcount_predictions_loss: 0.2233
2403/2436 [============================>.] - ETA: 5s - loss: 583.0023 - logits_profile_predictions_loss: 578.8712 - logcount_predictions_loss: 0.2233
2404/2436 [============================>.] - ETA: 4s - loss: 583.0024 - logits_profile_predictions_loss: 578.8713 - logcount_predictions_loss: 0.2233
2405/2436 [============================>.] - ETA: 4s - loss: 583.0026 - logits_profile_predictions_loss: 578.8715 - logcount_predictions_loss: 0.2233
2406/2436 [============================>.] - ETA: 4s - loss: 583.0027 - logits_profile_predictions_loss: 578.8716 - logcount_predictions_loss: 0.2233
2407/2436 [============================>.] - ETA: 4s - loss: 583.0029 - logits_profile_predictions_loss: 578.8717 - logcount_predictions_loss: 0.2233
2408/2436 [============================>.] - ETA: 4s - loss: 583.0030 - logits_profile_predictions_loss: 578.8719 - logcount_predictions_loss: 0.2233
2409/2436 [============================>.] - ETA: 4s - loss: 583.0032 - logits_profile_predictions_loss: 578.8720 - logcount_predictions_loss: 0.2233
2410/2436 [============================>.] - ETA: 3s - loss: 583.0033 - logits_profile_predictions_loss: 578.8722 - logcount_predictions_loss: 0.2233
2411/2436 [============================>.] - ETA: 3s - loss: 583.0035 - logits_profile_predictions_loss: 578.8723 - logcount_predictions_loss: 0.2233
2412/2436 [============================>.] - ETA: 3s - loss: 583.0036 - logits_profile_predictions_loss: 578.8725 - logcount_predictions_loss: 0.2233
2413/2436 [============================>.] - ETA: 3s - loss: 583.0038 - logits_profile_predictions_loss: 578.8726 - logcount_predictions_loss: 0.2233
2414/2436 [============================>.] - ETA: 3s - loss: 583.0040 - logits_profile_predictions_loss: 578.8728 - logcount_predictions_loss: 0.2233
2415/2436 [============================>.] - ETA: 3s - loss: 583.0041 - logits_profile_predictions_loss: 578.8729 - logcount_predictions_loss: 0.2233
2416/2436 [============================>.] - ETA: 3s - loss: 583.0043 - logits_profile_predictions_loss: 578.8731 - logcount_predictions_loss: 0.2233
2417/2436 [============================>.] - ETA: 2s - loss: 583.0044 - logits_profile_predictions_loss: 578.8733 - logcount_predictions_loss: 0.2233
2418/2436 [============================>.] - ETA: 2s - loss: 583.0046 - logits_profile_predictions_loss: 578.8734 - logcount_predictions_loss: 0.2233
2419/2436 [============================>.] - ETA: 2s - loss: 583.0047 - logits_profile_predictions_loss: 578.8735 - logcount_predictions_loss: 0.2233
2420/2436 [============================>.] - ETA: 2s - loss: 583.0048 - logits_profile_predictions_loss: 578.8737 - logcount_predictions_loss: 0.2233
2421/2436 [============================>.] - ETA: 2s - loss: 583.0050 - logits_profile_predictions_loss: 578.8738 - logcount_predictions_loss: 0.2233
2422/2436 [============================>.] - ETA: 2s - loss: 583.0051 - logits_profile_predictions_loss: 578.8740 - logcount_predictions_loss: 0.2233
2423/2436 [============================>.] - ETA: 1s - loss: 583.0053 - logits_profile_predictions_loss: 578.8741 - logcount_predictions_loss: 0.2233
2424/2436 [============================>.] - ETA: 1s - loss: 583.0055 - logits_profile_predictions_loss: 578.8743 - logcount_predictions_loss: 0.2233
2425/2436 [============================>.] - ETA: 1s - loss: 583.0057 - logits_profile_predictions_loss: 578.8745 - logcount_predictions_loss: 0.2233
2426/2436 [============================>.] - ETA: 1s - loss: 583.0059 - logits_profile_predictions_loss: 578.8747 - logcount_predictions_loss: 0.2233
2427/2436 [============================>.] - ETA: 1s - loss: 583.0061 - logits_profile_predictions_loss: 578.8749 - logcount_predictions_loss: 0.2233
2428/2436 [============================>.] - ETA: 1s - loss: 583.0063 - logits_profile_predictions_loss: 578.8751 - logcount_predictions_loss: 0.2233
2429/2436 [============================>.] - ETA: 1s - loss: 583.0065 - logits_profile_predictions_loss: 578.8753 - logcount_predictions_loss: 0.2233
2430/2436 [============================>.] - ETA: 0s - loss: 583.0067 - logits_profile_predictions_loss: 578.8755 - logcount_predictions_loss: 0.2233
2431/2436 [============================>.] - ETA: 0s - loss: 583.0069 - logits_profile_predictions_loss: 578.8757 - logcount_predictions_loss: 0.2233
2432/2436 [============================>.] - ETA: 0s - loss: 583.0071 - logits_profile_predictions_loss: 578.8759 - logcount_predictions_loss: 0.2233
2433/2436 [============================>.] - ETA: 0s - loss: 583.0073 - logits_profile_predictions_loss: 578.8761 - logcount_predictions_loss: 0.2233
2434/2436 [============================>.] - ETA: 0s - loss: 583.0075 - logits_profile_predictions_loss: 578.8763 - logcount_predictions_loss: 0.2233
2435/2436 [============================>.] - ETA: 0s - loss: 583.0077 - logits_profile_predictions_loss: 578.8765 - logcount_predictions_loss: 0.2233
2436/2436 [==============================] - ETA: 0s - loss: 583.0080 - logits_profile_predictions_loss: 578.8768 - logcount_predictions_loss: 0.2233
2436/2436 [==============================] - 387s 159ms/step - loss: 583.0082 - logits_profile_predictions_loss: 578.8770 - logcount_predictions_loss: 0.2233 - val_loss: 619.0779 - val_logits_profile_predictions_loss: 614.5745 - val_logcount_predictions_loss: 0.2434
+
+Epoch 00014: val_loss did not improve from 614.99408
+Restoring model weights from the end of the best epoch.
+Epoch 00014: early stopping
+save model
+save model without bias
+2022-03-24_00-38-49: python /home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/predict.py \
+        --genome=/scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa \
+        --bigwig=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//preprocessing/bigWigs/ENCSR856XGY.bigWig \
+        --peaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.peaks.bed \
+        --nonpeaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.nonpeaks.bed \
+        --chr_fold_path=/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_0.json \
+        --inputlen=2114 \
+        --outputlen=1000 \
+        --output_prefix=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//chrombpnet \
+        --batch_size=256 \
+        --model_h5=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//chrombpnet.h5
+got the model
+loading peaks...
+got split:test for bed regions:(41638, 10)
+loading nonpeaks...
+got split:test for bed regions:(41637, 10)
+0/326
+100/326
+200/326
+300/326
+2022-03-24_00-43-40: python /home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/predict.py \
+        --genome=/scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa \
+        --bigwig=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//preprocessing/bigWigs/ENCSR856XGY.bigWig \
+        --peaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.peaks.bed \
+        --nonpeaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.nonpeaks.bed \
+        --chr_fold_path=/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_0.json \
+        --inputlen=2114 \
+        --outputlen=1000 \
+        --output_prefix=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//chrombpnet_wo_bias \
+        --batch_size=256 \
+        --model_h5=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//chrombpnet_wo_bias.h5 
+got the model
+loading peaks...
+got split:test for bed regions:(41638, 10)
+loading nonpeaks...
+got split:test for bed regions:(41637, 10)
+0/326
+100/326
+200/326
+300/326
+2022-03-24_00-47-36: python /home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/predict.py \
+        --genome=/scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa \
+        --bigwig=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//preprocessing/bigWigs/ENCSR856XGY.bigWig \
+        --peaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.peaks.bed \
+        --nonpeaks=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.nonpeaks.bed \
+        --chr_fold_path=/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_0.json \
+        --inputlen=2114 \
+        --outputlen=1000 \
+        --output_prefix=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//bias \
+        --batch_size=256 \
+        --model_h5=/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//bias_model_scaled.h5
+got the model
+loading peaks...
+got split:test for bed regions:(41638, 10)
+loading nonpeaks...
+got split:test for bed regions:(41637, 10)
+0/326
+100/326
+200/326
+300/326
+2022-03-24_00-50-25: mkdir /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//footprints
+2022-03-24_00-50-25: python /home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/evaluation/marginal_footprints/marginal_footprinting.py \
+        -g /scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa \
+        -r /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.nonpeaks.bed \
+        -chr chr1 \
+        -m /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//chrombpnet_wo_bias.h5 \
+        -bs 512 \
+        -o /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//footprints/corrected \
+        -pwm_f src/evaluation/marginal_footprints/motif_to_pwm.tsv \
+        -mo dnase_1,dnase_2
+    MOTIF_NAME            MOTIF_PWM_FWD
+0        tn5_1         GCACAGTACAGAGCTG
+1        tn5_2  GTGCACAGTTCTAGAGTGTGCAG
+2        tn5_3       CCTCTACACTGTGCAGAA
+3        tn5_4    GCACAGTTCTAGACTGTGCAG
+4        tn5_5    CTGCACAGTGTAGAGTTGTGC
+5      dnase_1             TTTACAAGTCCA
+6      dnase_2             TGTACTTACGAA
+7         NRF1               GCGCATGCGC
+8          AP1         CGATATGACTCATCCC
+9         CTCF    TTGGCCACTAGGGGGCGCTAT
+10         ETS     CCGAAAGCGGAAGTGAGAC 
+11         SP1        AAGGGGGCGGGGCCTAA
+12        RUNX          CCCTAACCACAGCCC
+13        NFKB      GCAAGGGAAATTCCCCAGG
+14    GATA+TAL  GGCTGGGGGGGGCAGATAAGGCC
+15         TAL                  GGCTGGG
+16        NFYB          CCAGCCAATCAGAGC
+17       GABPA         GAAACCGGAAGTGGCC
+18  BACH1+MAFK       AACTGCTGAGTCATCCCG
+19        NRF1      CCCCGCGCATGCGCAGTGC
+20       HNF4G     CCGTTGGACTTTGGACCCTG
+got the model
+Model: "model_1"
+__________________________________________________________________________________________________
+Layer (type)                    Output Shape         Param #     Connected to                     
+==================================================================================================
+sequence (InputLayer)           [(None, 2114, 4)]    0                                            
+__________________________________________________________________________________________________
+wo_bias_bpnet_1st_conv (Conv1D) (None, 2094, 512)    43520       sequence[0][0]                   
+__________________________________________________________________________________________________
+wo_bias_bpnet_1conv (Conv1D)    (None, 2090, 512)    786944      wo_bias_bpnet_1st_conv[0][0]     
+__________________________________________________________________________________________________
+wo_bias_bpnet_1crop (Cropping1D (None, 2090, 512)    0           wo_bias_bpnet_1st_conv[0][0]     
+__________________________________________________________________________________________________
+add (Add)                       (None, 2090, 512)    0           wo_bias_bpnet_1conv[0][0]        
+                                                                 wo_bias_bpnet_1crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_2conv (Conv1D)    (None, 2082, 512)    786944      add[0][0]                        
+__________________________________________________________________________________________________
+wo_bias_bpnet_2crop (Cropping1D (None, 2082, 512)    0           add[0][0]                        
+__________________________________________________________________________________________________
+add_1 (Add)                     (None, 2082, 512)    0           wo_bias_bpnet_2conv[0][0]        
+                                                                 wo_bias_bpnet_2crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_3conv (Conv1D)    (None, 2066, 512)    786944      add_1[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_3crop (Cropping1D (None, 2066, 512)    0           add_1[0][0]                      
+__________________________________________________________________________________________________
+add_2 (Add)                     (None, 2066, 512)    0           wo_bias_bpnet_3conv[0][0]        
+                                                                 wo_bias_bpnet_3crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_4conv (Conv1D)    (None, 2034, 512)    786944      add_2[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_4crop (Cropping1D (None, 2034, 512)    0           add_2[0][0]                      
+__________________________________________________________________________________________________
+add_3 (Add)                     (None, 2034, 512)    0           wo_bias_bpnet_4conv[0][0]        
+                                                                 wo_bias_bpnet_4crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_5conv (Conv1D)    (None, 1970, 512)    786944      add_3[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_5crop (Cropping1D (None, 1970, 512)    0           add_3[0][0]                      
+__________________________________________________________________________________________________
+add_4 (Add)                     (None, 1970, 512)    0           wo_bias_bpnet_5conv[0][0]        
+                                                                 wo_bias_bpnet_5crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_6conv (Conv1D)    (None, 1842, 512)    786944      add_4[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_6crop (Cropping1D (None, 1842, 512)    0           add_4[0][0]                      
+__________________________________________________________________________________________________
+add_5 (Add)                     (None, 1842, 512)    0           wo_bias_bpnet_6conv[0][0]        
+                                                                 wo_bias_bpnet_6crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_7conv (Conv1D)    (None, 1586, 512)    786944      add_5[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_7crop (Cropping1D (None, 1586, 512)    0           add_5[0][0]                      
+__________________________________________________________________________________________________
+add_6 (Add)                     (None, 1586, 512)    0           wo_bias_bpnet_7conv[0][0]        
+                                                                 wo_bias_bpnet_7crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_8conv (Conv1D)    (None, 1074, 512)    786944      add_6[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_8crop (Cropping1D (None, 1074, 512)    0           add_6[0][0]                      
+__________________________________________________________________________________________________
+add_7 (Add)                     (None, 1074, 512)    0           wo_bias_bpnet_8conv[0][0]        
+                                                                 wo_bias_bpnet_8crop[0][0]        
+__________________________________________________________________________________________________
+wo_bias_bpnet_prof_out_precrop  (None, 1000, 1)      38401       add_7[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_logitt_before_fla (None, 1000, 1)      0           wo_bias_bpnet_prof_out_precrop[0]
+__________________________________________________________________________________________________
+gap (GlobalAveragePooling1D)    (None, 512)          0           add_7[0][0]                      
+__________________________________________________________________________________________________
+wo_bias_bpnet_logits_profile_pr (None, 1000)         0           wo_bias_bpnet_logitt_before_flatt
+__________________________________________________________________________________________________
+wo_bias_bpnet_logcount_predicti (None, 1)            513         gap[0][0]                        
+==================================================================================================
+Total params: 6,377,986
+Trainable params: 6,377,986
+Non-trainable params: 0
+__________________________________________________________________________________________________
+inferred model inputlen:  2114
+inferred model outputlen:  1000
+inserting motif:  dnase_1
+TTTACAAGTCCA
+
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+
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+inserting motif:  dnase_2
+TGTACTTACGAA
+
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+
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+Saving marginal footprints
+2022-03-24_00-52-35: mkdir /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//footprints
+2022-03-24_00-52-35: python /home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/evaluation/marginal_footprints/marginal_footprinting.py \
+        -g /scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa \
+        -r /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//filtered.nonpeaks.bed \
+        -chr chr1 \
+        -m /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//bias_model_scaled.h5 \
+        -bs 512 \
+        -o /scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR856XGY//chrombpnet_model_encsr880cub_bias//footprints/bias \
+        -pwm_f src/evaluation/marginal_footprints/motif_to_pwm.tsv \
+        -mo dnase_1,dnase_2
+    MOTIF_NAME            MOTIF_PWM_FWD
+0        tn5_1         GCACAGTACAGAGCTG
+1        tn5_2  GTGCACAGTTCTAGAGTGTGCAG
+2        tn5_3       CCTCTACACTGTGCAGAA
+3        tn5_4    GCACAGTTCTAGACTGTGCAG
+4        tn5_5    CTGCACAGTGTAGAGTTGTGC
+5      dnase_1             TTTACAAGTCCA
+6      dnase_2             TGTACTTACGAA
+7         NRF1               GCGCATGCGC
+8          AP1         CGATATGACTCATCCC
+9         CTCF    TTGGCCACTAGGGGGCGCTAT
+10         ETS     CCGAAAGCGGAAGTGAGAC 
+11         SP1        AAGGGGGCGGGGCCTAA
+12        RUNX          CCCTAACCACAGCCC
+13        NFKB      GCAAGGGAAATTCCCCAGG
+14    GATA+TAL  GGCTGGGGGGGGCAGATAAGGCC
+15         TAL                  GGCTGGG
+16        NFYB          CCAGCCAATCAGAGC
+17       GABPA         GAAACCGGAAGTGGCC
+18  BACH1+MAFK       AACTGCTGAGTCATCCCG
+19        NRF1      CCCCGCGCATGCGCAGTGC
+20       HNF4G     CCGTTGGACTTTGGACCCTG
+got the model
+Model: "model"
+__________________________________________________________________________________________________
+Layer (type)                    Output Shape         Param #     Connected to                     
+==================================================================================================
+sequence (InputLayer)           [(None, 2114, 4)]    0                                            
+__________________________________________________________________________________________________
+bpnet_1st_conv (Conv1D)         (None, 2094, 128)    10880       sequence[0][0]                   
+__________________________________________________________________________________________________
+bpnet_1conv (Conv1D)            (None, 2090, 128)    49280       bpnet_1st_conv[0][0]             
+__________________________________________________________________________________________________
+bpnet_1crop (Cropping1D)        (None, 2090, 128)    0           bpnet_1st_conv[0][0]             
+__________________________________________________________________________________________________
+add (Add)                       (None, 2090, 128)    0           bpnet_1conv[0][0]                
+                                                                 bpnet_1crop[0][0]                
+__________________________________________________________________________________________________
+bpnet_2conv (Conv1D)            (None, 2082, 128)    49280       add[0][0]                        
+__________________________________________________________________________________________________
+bpnet_2crop (Cropping1D)        (None, 2082, 128)    0           add[0][0]                        
+__________________________________________________________________________________________________
+add_1 (Add)                     (None, 2082, 128)    0           bpnet_2conv[0][0]                
+                                                                 bpnet_2crop[0][0]                
+__________________________________________________________________________________________________
+bpnet_3conv (Conv1D)            (None, 2066, 128)    49280       add_1[0][0]                      
+__________________________________________________________________________________________________
+bpnet_3crop (Cropping1D)        (None, 2066, 128)    0           add_1[0][0]                      
+__________________________________________________________________________________________________
+add_2 (Add)                     (None, 2066, 128)    0           bpnet_3conv[0][0]                
+                                                                 bpnet_3crop[0][0]                
+__________________________________________________________________________________________________
+bpnet_4conv (Conv1D)            (None, 2034, 128)    49280       add_2[0][0]                      
+__________________________________________________________________________________________________
+bpnet_4crop (Cropping1D)        (None, 2034, 128)    0           add_2[0][0]                      
+__________________________________________________________________________________________________
+add_3 (Add)                     (None, 2034, 128)    0           bpnet_4conv[0][0]                
+                                                                 bpnet_4crop[0][0]                
+__________________________________________________________________________________________________
+prof_out_precrop (Conv1D)       (None, 1960, 1)      9601        add_3[0][0]                      
+__________________________________________________________________________________________________
+logits_profile_predictions_pref (None, 1000, 1)      0           prof_out_precrop[0][0]           
+__________________________________________________________________________________________________
+gap (GlobalAveragePooling1D)    (None, 128)          0           add_3[0][0]                      
+__________________________________________________________________________________________________
+logits_profile_predictions (Fla (None, 1000)         0           logits_profile_predictions_prefla
+__________________________________________________________________________________________________
+logcount_predictions (Dense)    (None, 1)            129         gap[0][0]                        
+==================================================================================================
+Total params: 217,730
+Trainable params: 217,730
+Non-trainable params: 0
+__________________________________________________________________________________________________
+inferred model inputlen:  2114
+inferred model outputlen:  1000
+inserting motif:  dnase_1
+TTTACAAGTCCA
+
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+inserting motif:  dnase_2
+TGTACTTACGAA
+
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+Saving marginal footprints