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from blocks.initialization import IsotropicGaussian, Constant
from blocks.algorithms import Momentum
from blocks.bricks import Tanh
import data
from model.memory_network_mlp import Model, Stream
n_begin_end_pts = 5
dim_embeddings = [
('origin_call', data.origin_call_train_size, 10),
('origin_stand', data.stands_size, 10),
('week_of_year', 52, 10),
('day_of_week', 7, 10),
('qhour_of_day', 24 * 4, 10),
('day_type', 3, 10),
]
embed_weights_init = IsotropicGaussian(0.001)
class MLPConfig(object):
__slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init', 'embed_weights_init', 'dim_embeddings')
prefix_encoder = MLPConfig()
prefix_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
prefix_encoder.dim_hidden = [100]
prefix_encoder.weights_init = IsotropicGaussian(0.01)
prefix_encoder.biases_init = Constant(0.001)
prefix_encoder.embed_weights_init = embed_weights_init
prefix_encoder.dim_embeddings = dim_embeddings
candidate_encoder = MLPConfig()
candidate_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
candidate_encoder.dim_hidden = [100]
candidate_encoder.weights_init = IsotropicGaussian(0.01)
candidate_encoder.biases_init = Constant(0.001)
candidate_encoder.embed_weights_init = embed_weights_init
candidate_encoder.dim_embeddings = dim_embeddings
representation_size = 100
representation_activation = Tanh
normalize_representation = False
step_rule = Momentum(learning_rate=0.1, momentum=0.9)
batch_size = 10000
# batch_sort_size = 20
monitor_freq = 1000
max_splits = 200
train_candidate_size = 20000
valid_candidate_size = 20000
test_candidate_size = 20000
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