From a6fdddce3f94913a0f8fadfcf8c74005e76c192e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=89tienne=20Simon?= Date: Fri, 24 Jul 2015 16:10:55 -0400 Subject: Remove old memory network config files --- config/memory_network_1.py | 44 ------------------------------------ config/memory_network_2.py | 56 ---------------------------------------------- config/memory_network_3.py | 56 ---------------------------------------------- 3 files changed, 156 deletions(-) delete mode 100644 config/memory_network_1.py delete mode 100644 config/memory_network_2.py delete mode 100644 config/memory_network_3.py diff --git a/config/memory_network_1.py b/config/memory_network_1.py deleted file mode 100644 index 70b0f3e..0000000 --- a/config/memory_network_1.py +++ /dev/null @@ -1,44 +0,0 @@ -from blocks.initialization import IsotropicGaussian, Constant - -import data -from model.memory_network import Model, Stream - - -n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory - -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), -] - - -class MLPConfig(object): - __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init') - -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, 100, 100] -prefix_encoder.weights_init = IsotropicGaussian(0.01) -prefix_encoder.biases_init = Constant(0.001) - -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, 100, 100] -candidate_encoder.weights_init = IsotropicGaussian(0.01) -candidate_encoder.biases_init = Constant(0.001) - -normalize_representation = True - -embed_weights_init = IsotropicGaussian(0.001) - -batch_size = 32 - -max_splits = 1 -num_cuts = 1000 - -train_candidate_size = 1000 -valid_candidate_size = 10000 diff --git a/config/memory_network_2.py b/config/memory_network_2.py deleted file mode 100644 index cdd2bc1..0000000 --- a/config/memory_network_2.py +++ /dev/null @@ -1,56 +0,0 @@ -from blocks import roles -from blocks.bricks import Rectifier, Tanh, Logistic -from blocks.filter import VariableFilter -from blocks.initialization import IsotropicGaussian, Constant - -import data -from model.memory_network import Model, Stream - - -n_begin_end_pts = 10 # how many points we consider at the beginning and end of the known trajectory - -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), -] - - -class MLPConfig(object): - __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init') - -prefix_encoder = MLPConfig() -prefix_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) -prefix_encoder.dim_hidden = [1000, 1000] -prefix_encoder.weights_init = IsotropicGaussian(0.01) -prefix_encoder.biases_init = Constant(0.001) - -candidate_encoder = MLPConfig() -candidate_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) -candidate_encoder.dim_hidden = [1000, 1000] -candidate_encoder.weights_init = IsotropicGaussian(0.01) -candidate_encoder.biases_init = Constant(0.001) - -representation_size = 1000 -representation_activation = Tanh -normalize_representation = True - -embed_weights_init = IsotropicGaussian(0.001) - -dropout = 0.5 -dropout_inputs = VariableFilter(bricks=[Rectifier], name='output') - -noise = 0.01 -noise_inputs = VariableFilter(roles=[roles.PARAMETER]) - -batch_size = 512 - -max_splits = 1 -num_cuts = 1000 - -train_candidate_size = 10000 -valid_candidate_size = 20000 - diff --git a/config/memory_network_3.py b/config/memory_network_3.py deleted file mode 100644 index aa1fecb..0000000 --- a/config/memory_network_3.py +++ /dev/null @@ -1,56 +0,0 @@ -from blocks import roles -from blocks.bricks import Rectifier, Tanh, Logistic -from blocks.filter import VariableFilter -from blocks.initialization import IsotropicGaussian, Constant - -import data -from model.memory_network import Model, Stream - - -n_begin_end_pts = 10 # how many points we consider at the beginning and end of the known trajectory - -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), -] - - -class MLPConfig(object): - __slots__ = ('dim_input', 'dim_hidden', 'dim_output', 'weights_init', 'biases_init') - -prefix_encoder = MLPConfig() -prefix_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) -prefix_encoder.dim_hidden = [200, 200, 200] -prefix_encoder.weights_init = IsotropicGaussian(0.01) -prefix_encoder.biases_init = Constant(0.001) - -candidate_encoder = MLPConfig() -candidate_encoder.dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings) -candidate_encoder.dim_hidden = [200, 200, 200] -candidate_encoder.weights_init = IsotropicGaussian(0.01) -candidate_encoder.biases_init = Constant(0.001) - -representation_size = 500 -representation_activation = Tanh -normalize_representation = True - -embed_weights_init = IsotropicGaussian(0.001) - -dropout = 0.5 -dropout_inputs = VariableFilter(bricks=[Rectifier], name='output') - -noise = 0.01 -noise_inputs = VariableFilter(roles=[roles.PARAMETER]) - -batch_size = 512 - -max_splits = 1 -num_cuts = 1000 - -train_candidate_size = 10000 -valid_candidate_size = 20000 - -- cgit v1.2.3