import generative_playground
except:
import sys, os, inspect
my_location = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append('../../..')
sys.path.append('../../../../../transformer_pytorch')
from generative_playground.molecules.train.vae.main_train_vae import train_vae
from generative_playground.molecules.model_settings import get_settings
molecules = True
grammar = True
settings = get_settings(molecules,grammar)
save_file =settings['filename_stub'] + 'dr0.2_rnn__.h5'
model, fitter, main_dataset = train_vae(molecules=molecules,
BATCH_SIZE=150, # 250 max for p2.xlarge
drop_rate=0.2,
save_file=save_file,
sample_z=False,
encoder_type='rnn',
decoder_type='action',
lr=1e-4,
plot_prefix='rnn do0.2 no_sam 1e-4',
preload_weights=False)
while True:
next(fitter)
import sys, os, inspect
my_location = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append('../../..')
sys.path.append('../../../../../transformer_pytorch')
from generative_playground.molecules.train.vae.main_train_vae import train_vae
from generative_playground.molecules.model_settings import get_settings
molecules = True
grammar = True
settings = get_settings(molecules,grammar)
save_file =settings['filename_stub'] + 'baseline__.h5'
model, fitter, _ = train_vae(molecules=molecules,
grammar=grammar,
BATCH_SIZE=50, # max 500 on a p2.xlarge
save_file=save_file,
sample_z=True,
encoder_type='cnn',
decoder_type='step',
lr=5e-4,
plot_prefix='baseline lr 5e-4 KLW 0.01',
reg_weight= 1,
epsilon_std = 0.01,
dashboard='main',
preload_weights=False)
while True:
next(fitter)
import generative_playground
except:
import sys, os, inspect
my_location = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append('../../..')
sys.path.append('../../../../../transformer_pytorch')
from generative_playground.molecules.train.vae.main_train_vae import train_vae
from generative_playground.molecules.model_settings import get_settings
molecules = True
grammar = True
settings = get_settings(molecules, grammar)
save_file = settings['filename_stub'] + 'dr0.4_rnn_resnet__.h5'
model, fitter, main_dataset = train_vae(
molecules=molecules,
BATCH_SIZE=100, # max 200 on p2.xlarge
drop_rate=0.2,
save_file=save_file,
sample_z=False,
encoder_type='rnn',
decoder_type='action_resnet',
lr=1e-4,
plot_prefix='rnn_resnet do=0.4 1e-4')
while True:
next(fitter)
from generative_playground.models.simple_models import DenseHead
import numpy as np
from generative_playground.utils.gpu_utils import to_gpu
molecules = True
grammar = True
settings = get_settings(molecules, grammar)
dash_name = 'test'
visdom = Dashboard(dash_name)
model, fitter, main_dataset = train_vae(molecules=True,
grammar=True,
BATCH_SIZE=150,
drop_rate=0.3,
sample_z=True,
save_file='next_gen.h5',
encoder_type=False,
lr=5e-4,
plot_prefix='RNN enc lr 1e-4',
dashboard=dash_name,
preload_weights=False)
# this is a wrapper for encoding/decodng
grammar_model = ZincGrammarModel(model=model)
validity_model = to_gpu(
DenseHead(model.encoder, body_out_dim=settings['z_size'], drop_rate=0.3))
valid_smile_ds = IncrementingHDF5Dataset('valid_smiles.h5', valid_frac=0.1)
invalid_smile_ds = IncrementingHDF5Dataset('invalid_smiles.h5', valid_frac=0.1)
valid_fitter = train_validity(grammar=grammar,
model=validity_model,
except:
import sys, os, inspect
my_location = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append('../../..')
sys.path.append('../../../../../transformer_pytorch')
from generative_playground.molecules.train.vae.main_train_vae import train_vae
from generative_playground.molecules.model_settings import get_settings
molecules = True
grammar = True
settings = get_settings(molecules, grammar)
save_file = settings['filename_stub'] + 'dr0.2_attn__.h5'
model, fitter, main_dataset = train_vae(
molecules=molecules,
BATCH_SIZE=10, # it's a bit of a GPU RAM hog
drop_rate=0.2,
save_file=save_file,
sample_z=False,
reg_weight=1, # with 0.01 and do 0.1 had real trouble generalizing?
encoder_type='attention',
decoder_type='attention',
lr=1e-4,
plot_prefix='attn do=0.2 no_sam 1e-4',
preload_weights=False)
while True:
next(fitter)