<jupyter_start><jupyter_text>RNN-MD Many particle (16) PB experiment  Load the module and load configuration from many_pb_16.yaml file<jupyter_code>from model.RNN_MD import RNN_MD
rnn_md = RNN_MD(experiment='many_pb_16')<jupyter_output>1 Physical GPUs, 1 Logical GPUs
2.1.0
/device:GPU:0
Num GPUs Available:  1
1 Physical GPUs, 1 Logical GPUs
2.1.0
/device:GPU:0
Num GPUs Available:  1<jupyter_text>Load dataset<jupyter_code>rnn_md.load_many_particle_data()<jupyter_output><empty_output><jupyter_text>Train and save the model<jupyter_code>rnn_md.train()
rnn_md.save_model()<jupyter_output><empty_output><jupyter_text>Load the saved model<jupyter_code>rnn_md.load_model()<jupyter_output>Model: "lstm__model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  multiple                  10368     
_________________________________________________________________
batch_normalization (BatchNo multiple                  128       
_________________________________________________________________
activation (Activation)      multiple                  0         
_________________________________________________________________
dropout (Dropout)            multiple                  0         
_________________________________________________________________
lstm_1 (LSTM)                multiple                  8320      
_________________________________________________________________
batch_normalization_1 (Batch multiple                  128       
_______________________________________________________[...]<jupyter_text>Running a new simulation with RNN-MD and ploting it<jupyter_code>rnn_md.simulate_new_many_particle(testing_index=0)<jupyter_output>Actual data shape: (1996, 16, 3)
Predicted data shape: (1996, 16, 3)<jupyter_text>Actual movie visualization<jupyter_code>%matplotlib notebook
%matplotlib notebook

import sys
sys.path.append('../scripts/')
from viz_src.Particle_viz import Particle_viz


viz_sim = Particle_viz(movie_file_name = "temp_data/actual4000x.lammpstrj")
viz_sim.get_figure()
viz_sim.start()<jupyter_output><empty_output><jupyter_text>Predicted movie visualization<jupyter_code>%matplotlib notebook
%matplotlib notebook

import sys
sys.path.append('../scripts/')
from viz_src.Particle_viz import Particle_viz


viz_sim = Particle_viz(movie_file_name = "temp_data/predicted4000x.lammpstrj")
viz_sim.get_figure()
viz_sim.start()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09501557632398754, 'fraction_numerical': 0.024143302180685357, 'mean_word_length': 5.073286052009457, 'pattern_counts': {'":': 0, '<': 26, '<?xml version=': 0, '>': 26, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '86653', 'license': 'Apache-2.0', 'n_tokens_mistral': 785, 'n_tokens_neox': 727, 'n_words': 136, 'path': 'src/MANY-PB-16-Example.ipynb', 'repo_name': 'kadupitiya/RNN-MD', 'size': '2568'}