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from blocks.initialization import IsotropicGaussian, Constant
import data
from model.mlp_emb import Model, Stream
use_cuts_for_training = True
dim_embeddings = [
# ('origin_call', data.origin_call_train_size, 100),
# ('origin_stand', data.stands_size, 100),
# ('week_of_year', 52, 100),
# ('day_of_week', 7, 100),
('qhour_of_day', 24 * 4, 10),
('day_type', 3, 1),
]
dim_input = sum(x for (_, _, x) in dim_embeddings)
dim_hidden = [10, 10]
output_mode = "destination"
dim_output = 2
embed_weights_init = IsotropicGaussian(0.01)
mlp_weights_init = IsotropicGaussian(0.01)
mlp_biases_init = IsotropicGaussian(0.001)
learning_rate = 0.001
momentum = 0.9
batch_size = 100
max_splits = 100
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