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import numpy
from numpy.random import RandomState
from blocks.algorithms import AdaDelta, Momentum, RMSProp, CompositeRule, BasicMomentum
from blocks.bricks import Tanh, Rectifier
from blocks.initialization import IsotropicGaussian, Constant
from model.hpc_gru import Model
dataset = 'data/logcompil-2016-03-07.txt'
io_dim = 256
repr_dim = 64
embedding_matrix = (RandomState(42).binomial(1, 10./repr_dim, ((io_dim, repr_dim)))
-RandomState(123).binomial(1, 10./repr_dim, ((io_dim, repr_dim))))
# An epoch will be composed of 'num_seqs' sequences of len 'seq_len'
# divided in chunks of lengh 'seq_div_size'
num_seqs = 100
seq_len = 2000
seq_div_size = 100
hidden_dims = [128, 384, 1024]
cost_factors = [1., 1., 1.]
hidden_q = [0.5, 0.5, 0.5]
activation_function = Tanh()
out_hidden = [512]
out_hidden_act = [Tanh]
weight_noise = 0
step_rule = AdaDelta()
#step_rule = CompositeRule([RMSProp(learning_rate=0.01),
# BasicMomentum(momentum=0.9)])
#step_rule = Momentum(learning_rate=.1, momentum=0.9)
weights_init = IsotropicGaussian(0.1)
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 = 0.5 #0.7
# do we want to generate samples at times during training?
sample_len = 1000
sample_freq = 100
sample_init = '\nalex\ttu crois?\n'
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