parameter_search_grid_full = product([
expand({
"embedding_dimension": 20
}),
expand({
"n_feature_maps_w1": range(5, 12+1, 1),
"n_feature_maps_w2": range(20, 30+1, 1),
"n_feature_maps_d1": range(20, 30+1, 1),
}),
expand({
"kernel_width_w1": range(5, 15+1),
"kernel_width_w2": range(5, 10+1),
"kernel_width_d1": range(5, 15+1),
}),
expand({
"k_max_w1": 7,
"k_max_w2": 5,
"k_max_d1": 5,
}),
expand({
"dropout": True,
"n_epochs": 100000,
"adagrad_gamma": 0.01,
"validation_frequency": 50,
"save_frequency": 50,
"batch_size": 50,
"walltime": "24:00:00",
"n_classes": 2,
"pmem": "6gb",
"ppn": 2,
})
])
parameter_search_grid_full = product([
expand({"embedding_dimension": 20}),
expand({
"n_feature_maps_w1": range(5, 12 + 1, 1),
"n_feature_maps_w2": range(20, 30 + 1, 1),
"n_feature_maps_d1": range(20, 30 + 1, 1),
}),
expand({
"kernel_width_w1": range(5, 15 + 1),
"kernel_width_w2": range(5, 10 + 1),
"kernel_width_d1": range(5, 15 + 1),
}),
expand({
"k_max_w1": 7,
"k_max_w2": 5,
"k_max_d1": 5,
}),
expand({
"regularizer": [1e-4, 5e-3, 1e-3],
"dropout": True,
"n_epochs": 100000,
"adagrad_gamma": 0.01,
"validation_frequency": 50,
"save_frequency": 50,
"batch_size": 50,
"walltime": "24:00:00",
"n_classes": 2,
"pmem": "6gb",
"ppn": 2,
})
])
parameter_search_grid = product([
expand({
"embedding_dimension": [20, 40]
}),
expand({
"n_feature_maps_w1": [10, 20],
"n_feature_maps_w2": [10, 20],
"n_feature_maps_d1": [10, 20],
}),
expand({
"kernel_width_w1": [7],
"kernel_width_w2": [5],
"kernel_width_d1": [3, 5],
}),
expand({
"k_max_w1": [8],
"k_max_w2": [6],
"k_max_d1": [3, 5],
}),
expand({
"dropout": [True, False],
"n_epochs": 10000,
"adagrad_gamma": 0.01,
"validation_frequency": 50,
"save_frequency": 50,
"batch_size": 50,
"walltime": "10:00:00",
"n_classes": 2,
"pmem": "4gb",
"ppn": 4,
})
])
parameter_search_grid = product([
expand({"embedding_dimension": [20, 40]}),
expand({
"n_feature_maps_w1": [10, 20],
"n_feature_maps_w2": [10, 20],
"n_feature_maps_d1": [10, 20],
}),
expand({
"kernel_width_w1": [7],
"kernel_width_w2": [5],
"kernel_width_d1": [3, 5],
}),
expand({
"k_max_w1": [8],
"k_max_w2": [6],
"k_max_d1": [3, 5],
}),
expand({
"dropout": [True, False],
"n_epochs": 10000,
"adagrad_gamma": 0.01,
"validation_frequency": 50,
"save_frequency": 50,
"batch_size": 50,
"walltime": "10:00:00",
"n_classes": 2,
"pmem": "4gb",
"ppn": 4,
})
])
parameter_search_grid_full = product([
expand({
"embedding_dimension": 30
}),
expand({
"n_feature_maps_w1": range(5, 25+1, 3),
"n_feature_maps_w2": range(5, 25+1, 3),
"n_feature_maps_d1": range(5, 25+1, 3),
}),
expand({
"kernel_width_w1": range(3, 7),
"kernel_width_w2": range(3, 7),
"kernel_width_d1": range(3, 7),
}),
expand({
"k_max_w1": range(2, 8, 2),
"k_max_w2": range(2, 6, 2),
"k_max_d1": range(2, 4),
}),
expand({
"dropout": [True, False],
"n_epochs": 10000,
"adagrad_gamma": 0.01,
"validation_frequency": 50,
"save_frequency": 50,
"batch_size": 50,
"walltime": "10:00:00",
"n_classes": 2,
"pmem": "4gb",
"ppn": 4,
})
])
parameter_search_grid_full = product([
expand({"embedding_dimension": 30}),
expand({
"n_feature_maps_w1": range(5, 25 + 1, 3),
"n_feature_maps_w2": range(5, 25 + 1, 3),
"n_feature_maps_d1": range(5, 25 + 1, 3),
}),
expand({
"kernel_width_w1": range(3, 7),
"kernel_width_w2": range(3, 7),
"kernel_width_d1": range(3, 7),
}),
expand({
"k_max_w1": range(2, 8, 2),
"k_max_w2": range(2, 6, 2),
"k_max_d1": range(2, 4),
}),
expand({
"dropout": [True, False],
"n_epochs": 10000,
"adagrad_gamma": 0.01,
"validation_frequency": 50,
"save_frequency": 50,
"batch_size": 50,
"walltime": "10:00:00",
"n_classes": 2,
"pmem": "4gb",
"ppn": 4,
})
])
parameter_search_grid = product([
expand({
"embedding_dimension": 20
}),
expand({
"n_feature_maps_w1": 10,
"n_feature_maps_w2": 20,
"n_feature_maps_d1": 20,
}),
expand({
"kernel_width_w1": 7,
"kernel_width_w2": 5,
"kernel_width_d1": 3,
}),
expand({
"k_max_w1": 8,
"k_max_w2": 6,
"k_max_d1": 3,
}),
expand({
"regularizer": [1e-4, 5e-4, 1e-3],
"dropout_input": [True, False],
"fully_connected_layer": [True, False],
"n_epochs": 10000,
"adagrad_gamma": [0.005, 0.01, 0.02],
"validation_frequency": 50,
"save_frequency": 50,
"batch_size": 50,
"walltime": "24:00:00",
"n_classes": 2,
"pmem": "6gb",
"ppn": 1,
})
])
