def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) self.rnns = model_param['n_hidden'] self.film_dim = model_param['film_dim'] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.n_hs = model_param['n_hidden'] self.static_bias = compute_static_bias_initialization(model_param['activation_ratio']) return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.n_hs = model_param['n_hidden'] + [int(self.orch_dim)] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.n_hidden = model_param['n_hidden'] self.n_visible = self.orch_dim self.n_condition = self.orch_dim * (self.temporal_order-1)+ self.piano_dim self.Gibbs_steps = model_param["Gibbs_steps"] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.n_hs = model_param['n_hidden'] self.n_hs_piano = model_param['n_hidden_piano'] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.layers = model_param['n_hidden'] # Number of different ordering when sampling self.num_ordering = model_param['num_ordering'] # Is it a keras model ? self.keras = True return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Stack conv self.filters = model_param["num_filter_piano"] self.kernels = model_param["kernel_size_piano"] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.n_hs = model_param['n_hidden'] self.num_filter = model_param['num_filter'] self.filter_size = model_param['filter_size'] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.layers = [self.piano_dim] + list(model_param['n_hidden']) # Is it a keras model ? self.keras = False return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.MLP_piano_emb = model_param['MLP_piano_emb'] self.GRU_orch_emb = model_param['GRU_orch_emb'] self.last_MLP = model_param['last_MLP'] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.layers = model_param['n_hidden'] # Is it a keras model ? self.keras = True return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Architecture self.layers = model_param['n_hidden'] self.recurrent_layers = model_param['n_hidden'] # Is it a keras model ? self.keras = True # Will be computed later self.context_embedding_size = None return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.layers = model_param['n_hidden'] # Number of different ordering when sampling self.num_ordering = model_param['num_ordering'] # Is it a keras model ? self.keras = True # Will be computed later self.context_embedding_size = None return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Architecture self.mlp_piano_present = model_param['mlp_piano_present'] self.recurrent_layers = model_param['recurrent_layers'] self.mlp_orch_present = model_param['mlp_orch_present'] self.mlp_last_pred = model_param['mlp_last_pred'] # Is it a keras model ? self.keras = True # Will be computed later self.context_embedding_size = None return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) self.num_filter_piano = model_param["num_filter_piano"] self.kernel_size_piano = model_param["kernel_size_piano"] self.num_filter_orch = model_param["num_filter_orch"] self.kernel_size_orch = model_param["kernel_size_orch"] self.embeddings_size = model_param["embeddings_size"] # The last recurrent layer output a vector of dimension embedding size self.gru_orch = list(model_param["gru_orch"]) self.gru_orch.append(self.embeddings_size) self.mlp_pred = model_param["mlp_pred"] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) self.num_filter_piano = model_param["num_filter_piano"] self.kernel_size_piano = model_param["kernel_size_piano"] self.mlp_piano = model_param["mlp_piano"] self.mlp_pred = model_param["mlp_pred"] self.gru_orch = model_param["gru_orch"] # Is it a keras model ? self.keras = True return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.n_h = model_param['n_hidden'] self.n_v = self.orch_dim self.n_c = self.orch_dim * (self.temporal_order-1) self.n_l = self.piano_dim self.n_f = model_param["n_factor"] self.n_fv = model_param["n_factor"] self.n_fh = model_param["n_factor"] self.Gibbs_steps = model_param["Gibbs_steps"] return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Architecture self.mlp_piano_present = model_param['mlp_piano_present'] self.recurrent_layers = model_param['recurrent_layers'] self.mlp_orch_present = model_param['mlp_orch_present'] self.mlp_last_pred = model_param['mlp_last_pred'] # Is it a keras model ? self.keras = True # Will be computed later self.context_embedding_size = None # Static bias self.static_bias = compute_static_bias_initialization( model_param['activation_ratio']) return
def get_hp_space(): super_space = Model_lop.get_hp_space() space = {'n_hidden': list_log_hopt(500, 2000, 10, 1, 2, "n_hidden")} space.update(super_space) return space
def get_hp_space(): super_space = Model_lop.get_hp_space() space = {} 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 = 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': list_log_hopt(500, 2000, 10, 1, 2, "n_hidden"), 'num_ordering': quniform_int('num_ordering', 5, 5, 1) } 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), } space.update(super_space) return space
def get_hp_space(): super_space = Model_lop.get_hp_space() space = { 'filter_0': quniform_int('filter_0', 20, 50, 1), 'kernel_0': quniform_int('kernel_0', 8, 16, 1), 'filter_1': quniform_int('filter_1', 20, 50, 1), 'kernel_1': quniform_int('kernel_1', 8, 16, 1), } space.update(super_space) return space
def get_hp_space(): super_space = Model_lop.get_hp_space() space = { 'num_filter_piano': quniform_int('num_filter_piano', 20, 50, 1), 'kernel_size_piano': quniform_int('kernel_size_piano', 8, 16, 1), 'mlp_piano': list_log_hopt(500, 2000, 10, 1, 3, "mlp_piano"), 'mlp_pred': list_log_hopt(500, 2000, 10, 1, 3, "mlp_pred"), 'gru_orch': list_log_hopt(500, 2000, 10, 1, 3, "gru_orch"), } space.update(super_space) return space
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 get_hp_space(): space = Model_lop.get_hp_space() return space
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) return
def __init__(self, model_param, dimensions): Model_lop.__init__(self, model_param, dimensions) # Hidden layers architecture self.n_hidden = model_param['n_hidden'] self.Gibbs_steps = model_param["Gibbs_steps"] return