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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 model.hpc_lstm 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 = [64, 256, 1024]
+cost_factors = [1., 1., 1.]
+hidden_q = [0.5, 0.5, 0.5]
+activation_function = Tanh()
+
+out_hidden = [512]
+out_hidden_act = [Rectifier]
+
+step_rule = AdaDelta()
+#step_rule = CompositeRule([RMSProp(learning_rate=0.01),
+# BasicMomentum(momentum=0.9)])
+#step_rule = Momentum(learning_rate=.1, momentum=0.9)
+
+# 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
+
+# do we want to generate samples at times during training?
+sample_len = 1000
+sample_freq = 100
+sample_init = '\nalex\ttu crois?\n'
+