def get_hp_space(): super_space = Model_lop.get_hp_space() space = { 'n_hidden': hp.choice('n_hidden', [ [ hopt_wrapper.qloguniform_int('n_hidden_1_' + str(i), log(100), log(5000), 10) for i in range(1) ], [ hopt_wrapper.qloguniform_int('n_hidden_2_' + str(i), log(100), log(5000), 10) for i in range(2) ], [ hopt_wrapper.qloguniform_int('n_hidden_3_' + str(i), log(100), log(5000), 10) for i in range(3) ], ]), } space.update(super_space) return space
def get_hp_space(): super_space = Model_lop.get_hp_space() space = { 'n_hidden': hopt_wrapper.qloguniform_int('n_hidden', log(100), log(5000), 10), 'n_factor': hopt_wrapper.qloguniform_int('n_factor', log(200), log(1000), 10), } space.update(super_space) return space
def get_hp_space(): super_space = Model_lop.get_hp_space() space = { 'num_filter_piano': list_hopt_fixedSized([(20, 30, 1), (10, 20, 1)], 'num_filter_piano'), 'kernel_size_piano': list_hopt_fixedSized([(12, 24, 1), (12, 24, 1)], "kernel_size_piano"), 'num_filter_orch': list_hopt_fixedSized([(30, 50, 1), (10, 20, 1)], 'num_filter_orch'), 'kernel_size_orch': list_hopt_fixedSized([(12, 24, 1), (12, 24, 1)], "kernel_size_orch"), 'embeddings_size': qloguniform_int("embeddings_size", log(500), log(2000), 10), 'mlp_pred': list_log_hopt(500, 2000, 10, 1, 3, "mlp_pred"), 'gru_orch': list_log_hopt(500, 2000, 10, 0, 2, "gru_orch"), } space.update(super_space) return space
def get_hp_space(): super_space = MLFPP.get_hp_space() space = { 'hs_piano': list_log_hopt(500, 2000, 10, 0, 2, 'hs_piano'), 'hs_orch': list_log_hopt(500, 2000, 10, 0, 2, 'hs_orch'), 'embeddings_size': qloguniform_int('hs_orch', log(500), log(1000), 10), } space.update(super_space) return space
def get_hp_space(): super_space = MLFPP.get_hp_space() space = { 'recurrent_orch': hp.choice('n_hidden', [ [ hopt_wrapper.qloguniform_int('n_hidden_1_' + str(i), log(500), log(3000), 10) for i in range(1) ], [ hopt_wrapper.qloguniform_int('n_hidden_2_' + str(i), log(500), log(3000), 10) for i in range(2) ], [ hopt_wrapper.qloguniform_int('n_hidden_3_' + str(i), log(500), log(3000), 10) for i in range(3) ], ]), 'recurrent_piano': hp.choice('n_hidden', [ [ hopt_wrapper.qloguniform_int('n_hidden_1_' + str(i), log(500), log(3000), 10) for i in range(1) ], [ hopt_wrapper.qloguniform_int('n_hidden_2_' + str(i), log(500), log(3000), 10) for i in range(2) ], ]), 'mlp_piano': hp.choice('n_hidden', [ [ hopt_wrapper.qloguniform_int('n_hidden_1_' + str(i), log(500), log(3000), 10) for i in range(1) ], [ hopt_wrapper.qloguniform_int('n_hidden_2_' + str(i), log(500), log(3000), 10) for i in range(2) ], ]) } space.update(super_space) return space
def get_hp_space(): space_training = { 'temporal_order': hopt_wrapper.qloguniform_int('temporal_order', log(3), log(20), 1) } space_regularization = { 'dropout_probability': hp.choice('dropout', [0.0, hp.normal('dropout_probability', 0.5, 0.1)]), 'weight_decay_coeff': hp.choice('weight_decay_coeff', [0.0, hp.uniform('a', 1e-4, 1e-4)]), } space_training.update(space_regularization) return space_training
def get_hp_space(): space_training = { # 'temporal_order': hopt_wrapper.qloguniform_int('temporal_order', log(2), log(7), 1), 'temporal_order': hopt_wrapper.qloguniform_int('temporal_order', log(5), log(5), 1), 'tn_weight': 1 / 10, 'sparsity_coeff': 0, } space_regularization = { 'dropout_probability': hp.choice('dropout', [0.0]), 'weight_decay_coeff': hp.choice('weight_decay_coeff', [0.0]) } space_training.update(space_regularization) return space_training
def get_hp_space(): super_space = Model_lop.get_hp_space() space = { 'n_hidden_embedding': hp.choice('n_hidden_embedding', [ [hopt_wrapper.qloguniform_int('n_hidden_embedding_'+str(i), log(1500), log(3000), 10) for i in range(1)], [hopt_wrapper.qloguniform_int('n_hidden_embedding_'+str(i), log(1500), log(3000), 10) for i in range(2)], [hopt_wrapper.qloguniform_int('n_hidden_embedding_'+str(i), log(1500), log(3000), 10) for i in range(3)], ]), 'n_hidden_NADE': hp.choice('n_hidden_NADE', [ [hopt_wrapper.qloguniform_int('n_hidden_NADE_'+str(i), log(1500), log(3000), 10) for i in range(1)], [hopt_wrapper.qloguniform_int('n_hidden_NADE_'+str(i), log(1500), log(3000), 10) for i in range(2)], [hopt_wrapper.qloguniform_int('n_hidden_NADE_'+str(i), log(1500), log(3000), 10) for i in range(3)], ]), 'num_ordering': quniform_int('num_ordering', 5, 10, 1) } space.update(super_space) return space
def list_log_hopt(min_unit, max_unit, step, min_num_layer, max_num_layer, name): return hp.choice(name, [ [qloguniform_int(name+'_'+str(i), log(min_unit), log(max_unit), step) for i in range(num_layer)] \ for num_layer in range(min_num_layer, max_num_layer) ])