# filters = [64, 64], kernels = [3, 3], # drop_remainder_batch = True, overwrite_results = True) #train_convlstm(num_vars = 4, seq_length = 6, epochs = 40, batch_size = 5, # filters = [32, 32, 32], kernels = [3, 3, 3], # drop_remainder_batch = True, overwrite_results = True) #train_convlstm(num_vars = 4, seq_length = 6, epochs = 40, batch_size = 5, # filters = [64, 32], kernels = [5, 5], # drop_remainder_batch = True, overwrite_results = True) # converged models. train_convlstm(num_vars=4, seq_length=24, epochs=1, batch_size=10, filters=[128], kernels=[3], drop_remainder_batch=True, overwrite_results=True) #train_convlstm(num_vars = 4, seq_length = 24, epochs = 40, batch_size = 10, # filters = [128, 32], kernels = [3, 3], # drop_remainder_batch = True, overwrite_results = True) #train_convlstm(num_vars = 4, seq_length = 24, epochs = 40, batch_size = 10, # filters = [16, 16], kernels = [3, 3], # drop_remainder_batch = True, overwrite_results = True) #train_convlstm(num_vars = 4, seq_length = 24, epochs = 40, batch_size = 10, # filters = [8, 8, 8], kernels = [3, 3, 3], # drop_remainder_batch = True, overwrite_results = True)
from base_config import train_convlstm # NON converging models #train_convlstm(num_vars = 4, seq_length = 6, epochs = 40, batch_size = 5, # filters = [256, 256], kernels = [3, 3], # drop_remainder_batch = True, overwrite_results = True) #train_convlstm(num_vars = 4, seq_length = 6, epochs = 40, batch_size = 5, # filters = [64, 64], kernels = [3, 3], # drop_remainder_batch = True, overwrite_results = True) train_convlstm(num_vars=4, seq_length=6, epochs=1, batch_size=5, filters=[32, 32, 32], kernels=[3, 3, 3], drop_remainder_batch=True, overwrite_results=True) #train_convlstm(num_vars = 4, seq_length = 6, epochs = 40, batch_size = 5, # filters = [64, 32], kernels = [5, 5], # drop_remainder_batch = True, overwrite_results = True) # converged models. #train_convlstm(num_vars = 4, seq_length = 24, epochs = 40, batch_size = 10, # filters = [128], kernels = [3], # drop_remainder_batch = True, overwrite_results = True) #train_convlstm(num_vars = 4, seq_length = 24, epochs = 40, batch_size = 10, # filters = [128, 32], kernels = [3, 3],
from base_config import train_convlstm # NON converging models train_convlstm(num_vars=4, seq_length=6, epochs=40, batch_size=5, filters=[256, 256], kernels=[3, 3], drop_remainder_batch=True, overwrite_results=True) train_convlstm(num_vars=4, seq_length=6, epochs=40, batch_size=5, filters=[64, 64], kernels=[3, 3], drop_remainder_batch=True, overwrite_results=True) train_convlstm(num_vars=4, seq_length=6, epochs=40, batch_size=5, filters=[32, 32, 32], kernels=[3, 3, 3], drop_remainder_batch=True, overwrite_results=True) train_convlstm(num_vars=4,