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Updating the Pilot 2 README files.
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‎Pilot2/P2B1/INSTALL.md‎

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# Add matplotlib with image support
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spack install py-matplotlib +image
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```
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#### Activating Spack
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```
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# Activate all of these tools in the spack python environment
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spack activate ipython
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spack activate py-ipython
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spack activate py-keras
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spack activate py-matplotlib
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# Load the ipython environment into your path
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module avail
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module load py-ipython-5.1.0-gcc-4.9.3
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# Lauch ipython and then run the example
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ipython
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[1]: run p2b1_baseline_keras1.py
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```

‎Pilot2/P2B1/README.md‎

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* Number of layers: 5-8
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### Running the baseline implementation
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#### Using virtualenv
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```
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cd P2B1
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workon keras
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python p2b1.py
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```
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#### Using Spack
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cd Pilot2/2B1
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python p2b1_baseline_keras1.py
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```
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# Activate all of these tools in the spack python environment
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spack activate ipython
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spack activate py-ipython
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spack activate py-keras
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spack activate py-matplotlib
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# Load the ipython environment into your path
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module avail
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module load py-ipython-5.1.0-gcc-4.9.3-3y6j6uo
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# Lauch ipython and then run the example
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ipython
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[1]: run p2b1.py
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```
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The training and test data files will be downloaded the first time this is run and will be cached for future runs.
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### Scaling Options
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* ```--case=FULL``` Design autoencoder for data frame with coordinates for all beads
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* ```--case=CENTER``` Design autoencoder for data frame with coordinates of the center-of-mass
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* ```--case=CENTERZ``` Design autoencoder for data frame with z-coordiate of the center-of-mass
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### Expected Results
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### Expected Output
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```
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$ python p2b1.py
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$ python p2b1_baseline_keras1.py
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Using Theano backend.
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Reading Data Files... 3k_Disordered->3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir
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Data Format: [Num Sample (2861), Num Molecules (4864), Num Atoms (12), Position + Molecule Tag (One-hot encoded) (6)]
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Data Format: [Num Sample (2861), Num Molecules (3040), Num Atoms (12), Position + Molecule Tag (One-hot encoded) (6)]
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Define the model and compile
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using mlp network
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Autoencoder Regression problem
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--------------------------------------------------------------------------------------------------------------------
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| type | feature size | #Filters | FilterSize | #params | #MACs |
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--------------------------------------------------------------------------------------------------------------------
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| dense | 0x0 | 512 | 01x01 | 2.49 Mill, 9.50 MB ( 46.7%) | 2.49 Mill ( 46.7%) |
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| dense | 0x0 | 256 | 01x01 | 0.13 Mill, 0.50 MB ( 2.5%) | 0.13 Mill ( 2.5%) |
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| dense | 0x0 | 128 | 01x01 | 0.03 Mill, 0.12 MB ( 0.6%) | 0.03 Mill ( 0.6%) |
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| dense | 0x0 | 512 | 01x01 | 1.56 Mill, 5.94 MB ( 45.0%) | 1.56 Mill ( 45.0%) |
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| dense | 0x0 | 256 | 01x01 | 0.13 Mill, 0.50 MB ( 3.8%) | 0.13 Mill ( 3.8%) |
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| dense | 0x0 | 128 | 01x01 | 0.03 Mill, 0.12 MB ( 0.9%) | 0.03 Mill ( 0.9%) |
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| dense | 0x0 | 64 | 01x01 | 0.01 Mill, 0.03 MB ( 0.2%) | 0.01 Mill ( 0.2%) |
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| dense | 0x0 | 32 | 01x01 | 0.00 Mill, 0.01 MB ( 0.0%) | 0.00 Mill ( 0.0%) |
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| dense | 0x0 | 32 | 01x01 | 0.00 Mill, 0.01 MB ( 0.1%) | 0.00 Mill ( 0.1%) |
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| dense | 0x0 | 16 | 01x01 | 0.00 Mill, 0.00 MB ( 0.0%) | 0.00 Mill ( 0.0%) |
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| dense | 0x0 | 32 | 01x01 | 0.00 Mill, 0.00 MB ( 0.0%) | 0.00 Mill ( 0.0%) |
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| dense | 0x0 | 64 | 01x01 | 0.00 Mill, 0.01 MB ( 0.0%) | 0.00 Mill ( 0.0%) |
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| dense | 0x0 | 64 | 01x01 | 0.00 Mill, 0.01 MB ( 0.1%) | 0.00 Mill ( 0.1%) |
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| dense | 0x0 | 128 | 01x01 | 0.01 Mill, 0.03 MB ( 0.2%) | 0.01 Mill ( 0.2%) |
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| dense | 0x0 | 256 | 01x01 | 0.03 Mill, 0.12 MB ( 0.6%) | 0.03 Mill ( 0.6%) |
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| dense | 0x0 | 512 | 01x01 | 0.13 Mill, 0.50 MB ( 2.5%) | 0.13 Mill ( 2.5%) |
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| dense | 0x0 | 4864 | 01x01 | 2.49 Mill, 9.50 MB ( 46.7%) | 2.49 Mill ( 46.7%) |
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| dense | 0x0 | 256 | 01x01 | 0.03 Mill, 0.12 MB ( 0.9%) | 0.03 Mill ( 0.9%) |
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| dense | 0x0 | 512 | 01x01 | 0.13 Mill, 0.50 MB ( 3.8%) | 0.13 Mill ( 3.8%) |
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| dense | 0x0 | 3040 | 01x01 | 1.56 Mill, 5.94 MB ( 45.0%) | 1.56 Mill ( 45.0%) |
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--------------------------------------------------------------------------------------------------------------------
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|++++++++++++++++++++++++++++++++++++++++++++++++| 5.33 Mill, 20.33 MB ( 100.0%)| 5.33 Mill ( 100.0%) | |
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|++++++++++++++++++++++++++++++++++++++++++++++++| 3.46 Mill, 13.21 MB ( 100.0%)| 3.46 Mill ( 100.0%) | |
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--------------------------------------------------------------------------------------------------------------------
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...
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Loss on epoch 0: 12.2727
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33%|------------------------------------| | 1/3 [00:28<00:57, 28.79s/it]
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Loss on epoch 1: 11.5519
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67%|---------------------------------------------------------------| | 2/3 [00:53<00:27, 27.44s/it]
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Loss on epoch 2: 11.3828
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100%|----------------------------------------------------------------------------------------------------------| 3/3 [01:17<00:00, 26.53s/it]
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0%| | 0/3 [00:00<?, ?it/s]
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Loss on epoch 0: 18.1642
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Loss on epoch 1: 17.4444
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Loss on epoch 2: 17.5339
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Cooling Learning Rate by factor of 10...
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Loss on epoch 0: 10.2965
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Loss on epoch 1: 10.3103
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Loss on epoch 2: 10.3181
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Loss on epoch 0: 15.2757
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Loss on epoch 1: 15.3096
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Loss on epoch 2: 15.3504
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Cooling Learning Rate by factor of 10...
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Loss on epoch 0: 9.9807
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Loss on epoch 1: 10.0237
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Loss on epoch 2: 10.0357
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Loss on epoch 0: 14.8456
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Loss on epoch 1: 14.8246
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Loss on epoch 2: 14.8405
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```

‎Pilot2/P2B2/README.md‎

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## P2B2: Predictive, recurrent, Autoencoder Compressed Representation for Molecular Dynamics Simulation Data
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## P2B2: Predictive network using Recurrent Neural Network, with Autoencoder Compressed Representation input, for Molecular Dynamics Simulation Data
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**Overview**: Cut down on manual inspection time for molecular simulation data
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### Running the baseline implementation
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Using virtualenv
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```
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cd P2B2
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workon keras
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cd Pilot2/P2B2
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python p2b2_baseline_keras1.py
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```
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The training and test data files will be downloaded the first time this is run and will be cached for future runs.
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### Scaling Options
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* ```--case=FULL``` Design autoencoder for data frame with coordinates for all beads
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* ```--case=CENTER``` Design autoencoder for data frame with coordinates of the center-of-mass
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* ```--case=CENTERZ``` Design autoencoder for data frame with z-coordiate of the center-of-mass
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### Expected Results
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(keras) vanessen1@vandamme: ~/Research/DeepLearning/ECP CANDLE/Benchmarks/Benchmarks.git/Pilot2/P2B2$ python p2b2_baseline_keras1.py
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### Expected Output
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```
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python p2b2_baseline_keras1.py
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Using Theano backend.
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{'num_hidden': [], 'num_recurrent': [16, 16, 16], 'noise_factor': 0, 'learning_rate': 0.01, 'batch_size': 32, 'look_forward': 1, 'epochs': 1, 'weight_decay': 0.0005, 'look_back': 10, 'cool': 'True'}
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{'num_hidden': [], 'num_recurrent': [16, 16, 16, 16], 'noise_factor': 0, 'learning_rate': 0.01, 'batch_size': 32, 'look_forward': 1, 'epochs': 1, 'weight_decay': 0.0005, 'look_back': 10, 'cool': 'True'}
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Reading Data...
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Reading Data Files... 3k_Disordered->3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir
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('X_train type and shape:', dtype('float64'), (89, 10, 3040))
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Non-trainable params: 0
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____________________________________________________________________________________________________
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Loss on epoch 0: 47.9408
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../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_21_outof_29.npy
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../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_22_outof_29.npy
87+
../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_23_outof_29.npy
88+
../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_24_outof_29.npy
89+
../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_25_outof_29.npy
90+
../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_26_outof_29.npy
91+
../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_27_outof_29.npy
92+
../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_28_outof_29.npy
93+
../Data/common/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20_chunk_29_outof_29.npy
94+
Loss on epoch 0: 47.957
95+
100%|----------------------------------------------------------------------------------------------------------| 1/1 [00:42<00:00, 42.32s/it]
6796
Cooling Learning Rate by factor of 10...
6897
0%| | 0/1 [00:00<?, ?it/s]
69-
Loss on epoch 0: 30.2241
70-
100%|---------------------------------------------------------------| | 1/1 [00:35<00:00, 35.29s/it]
98+
Loss on epoch 0: 30.5609
99+
100%|----------------------------------------------------------------------------------------------------------| 1/1 [00:49<00:00, 49.95s/it]
71100
Cooling Learning Rate by factor of 10...
72101
0%| | 0/1 [00:00<?, ?it/s]
73-
Loss on epoch 0: 23.4605
74-
100%|----------------------------------------------------------------------------------------------------------| 1/1 [00:35<00:00, 35.72s/it]
75-
(keras)
102+
Loss on epoch 0: 23.2651
103+
100%|----------------------------------------------------------------------------------------------------------| 1/1 [00:41<00:00, 41.74s/it]
104+
```

‎Pilot2/P2B2/p2b2.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -274,7 +274,7 @@ def train_ac(self):
274274
loss_data=self.model.train_on_batch(x,y,sample_weight=sample_weight)
275275
iter_loss.append(loss_data)
276276
file_loss.append(np.array(iter_loss).mean(axis=0))
277-
print 'Loss on epoch %d:'%e, file_loss[-1]
277+
print '\nLoss on epoch %d:'%e, file_loss[-1]
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epoch_loss.append(np.array(file_loss).mean(axis=0))
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return epoch_loss
280280

‎Pilot2/P2B2/p2b2_small_model.txt‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
11
[Global_Params]
22
num_hidden=[]
3-
num_recurrent=[16,16,16]
3+
num_recurrent=[16,16,16,16]
44
look_back=10
55
look_forward=1
66
batch_size=32

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