from blocks.algorithms import AdaDelta
from blocks.bricks import Tanh
from blocks.initialization import IsotropicGaussian, Constant
from model.condlstm import Model
dataset = 'data/logcompil.txt'
io_dim = 256
# An epoch will be composed of 'num_seqs' sequences of len 'seq_len'
# divided in chunks of lengh 'seq_div_size'
num_seqs = 50
seq_len = 5000
seq_div_size = 200
layers = [
# Slowlier
{'dim': 128,
'reset_after': ' \t\n,.:;/!?()[]{}<>\\\'"*+-^_|#~&`@$%',
},
{'dim': 128,
'run_on': ' \t\n,.:;/!?()[]{}<>\\\'"*+-^_|#~&`@$%',
'reset_after': ' \t\n',
},
{'dim': 128,
'run_on': ' \t\n',
'reset_after': '\t\n',
},
{'dim': 256,
'run_on': ' \t\n',
'reset_after': '\n',
},
{'dim': 512,
'run_on': '\t\n',
},
# Slowest
{'dim': 512,
'run_on': '\n',
},
# Fastlier
{'dim': 512,
'run_on': '\t\n',
},
{'dim': 256,
'run_on': ' \t\n',
'reset_before':'\n',
},
{'dim': 128,
'run_on': ' \t\n',
'reset_before': '\t\n',
},
{'dim': 128,
'run_on': ' \t\n,.:;/!?()[]{}<>\\\'"*+-^_|#~&`@$%',
'reset_before': ' \t\n',
},
{'dim': 128,
'reset_before': ' \t\n,.:;/!?()[]{}<>\\\'"*+-^_|#~&`@$%',
},
]
activation_function = Tanh()
w_noise_std = 0
i_dropout = 0
l1_reg = 0
l1_reg_weight = 0
step_rule = AdaDelta()
weights_init = IsotropicGaussian(0.01)
biases_init = Constant(0.)
# parameter saving freq (number of batches)
monitor_freq = 100
save_freq = 100
# used for sample generation and IRC mode
#sample_temperature = 0.7 #0.5
sample_temperature = 0.9 #0.5
# do we want to generate samples at times during training?
sample_len = 500
sample_freq = 100
sample_init = '\nalex\ttu crois?\n'
