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configs.py
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configs.py
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def config_DGM2_O():
conf = {
# Model Arguments
'd': 4,
'input_dim': 88,
'z_dim':3,
's_dim': 10,
'h_dim': 10,
'e_dim': 10,
'emission_z_dim':10,
'emission_x_dim':10,
'trans_dim':20,
'rnn_dim':60,
'cluster_num':20,
'temp':1.0, # softmax temperature (lower --> more discrete)
'dropout':0.0, # dropout applied to layers (0 = no dropout)
'x_std': 0.1,
'latent_x_std': 0.05,
'sampling_times': 10,
'use_gumbel': False,
'temp_min':0.1,
'temp_init':0.5,
'temp_anneal':0.01,
# Training Arguments
'batch_size':20,
'epochs':500, # maximum number of epochs
'min_epochs':2, # minimum number of epochs to train for
'use_gate': False,
'lr':1e-2, # autoencoder learning rate
'beta1':0.9, # beta1 for adam
'beta2':0.99,
'clip_norm':20.0, # gradient clipping, max norm
'weight_decay':2.0,
'anneal_epochs':1000,
'min_anneal':0.1,
}
return conf
def config_DGM2_L():
conf = {
# Model Arguments
'd': 4,
'input_dim': 88,
'z_dim':3,
's_dim': 10,
'h_dim': 10,
'e_dim': 10,
'emission_z_dim':5,
'emission_x_dim':5,
'trans_dim':20,
'rnn_dim':60,
'cluster_num':20,
'temp':1.0, # softmax temperature (lower --> more discrete)
'dropout':0.05, # dropout applied to layers (0 = no dropout)
'x_std': 0.1,
'latent_x_std': 0.05,
'sampling_times': 10,
'use_gumbel': False,
'temp_min':0.1,
'temp_init':0.5,
'temp_anneal':0.01,
# Training Arguments
'batch_size':20,
'epochs':500, # maximum number of epochs
'min_epochs':2, # minimum number of epochs to train for
'use_gate': False,
'lr':1e-2, # autoencoder learning rate
'beta1':0.9, # beta1 for adam
'beta2':0.99,
'clip_norm':20.0, # gradient clipping, max norm
'weight_decay':2.0,
'anneal_epochs':1000,
'min_anneal':0.1,
}
return conf
'''mimic3 data'''
# def config_DHMM_cluster():
# conf = {
#
# # Model Arguments
# 'd': 4,
# 'input_dim': 88,
# 'z_dim':10,
# 's_dim': 10,
# 'h_dim': 10,
# 'e_dim': 30,
# 'emission_z_dim':10,
# 'emission_x_dim':10,
# 'trans_dim':20,
# 'rnn_dim':60,
# 'cluster_num':50,
# 'temp':1.0, # softmax temperature (lower --> more discrete)
# 'dropout':0.2, # dropout applied to layers (0 = no dropout)
# 'x_std': 0.1,
# 'sampling_times': 10,
# 'use_gumbel': False,
# # Training Arguments
# 'batch_size':20,
# 'epochs':500, # maximum number of epochs
# 'min_epochs':2, # minimum number of epochs to train for
#
# 'lr':1e-2, # autoencoder learning rate
# 'beta1':0.9, # beta1 for adam
# 'beta2':0.99,
# 'clip_norm':20.0, # gradient clipping, max norm
# 'weight_decay':2.0,
# 'anneal_epochs':1000,
# 'min_anneal':0.1,
# }
# return conf
# def config_DHMM_cluster():
# conf = {
#
# # Model Arguments
# 'input_dim': 88,
# 'z_dim':100,
# 's_dim': 100,
# 'h_dim': 100,
# 'e_dim': 100,
# 'emission_z_dim':50,
# 'emission_x_dim':100,
# 'trans_dim':20,
# 'rnn_dim':60,
# 'cluster_num':1000,
# 'temp':1.0, # softmax temperature (lower --> more discrete)
# 'dropout':0, # dropout applied to layers (0 = no dropout)
# 'x_std': 0.01,
# 'd': 2,
# # Training Arguments
# 'batch_size':1000,
# 'epochs':5000, # maximum number of epochs
# 'min_epochs':2, # minimum number of epochs to train for
#
# 'lr':5e-3, # autoencoder learning rate
# 'beta1':0.9, # beta1 for adam
# 'beta2':0.99,
# 'clip_norm':20.0, # gradient clipping, max norm
# 'weight_decay':2.0,
# 'anneal_epochs':1000,
# 'min_anneal':0.1,
# }
# return conf
# def config_DHMM_cluster_tlstm():
# conf = {
#
# # Model Arguments
# 'd': 4,
# 'input_dim': 88,
# 'z_dim':10,
# 's_dim': 10,
# 'h_dim': 10,
# 'e_dim': 10,
# 'emission_z_dim':50,
# 'emission_x_dim':20,
# 'trans_dim':20,
# 'rnn_dim':60,
# 'cluster_num':50,
# 'temp':1.0, # softmax temperature (lower --> more discrete)
# 'dropout':0.0, # dropout applied to layers (0 = no dropout)
# 'x_std': 0.1,
# 'sampling_times': 10,
# 'use_gumbel': False,
# # Training Arguments
# 'batch_size':20,
# 'epochs':500, # maximum number of epochs
# 'min_epochs':2, # minimum number of epochs to train for
#
# 'lr':1e-2, # autoencoder learning rate
# 'beta1':0.9, # beta1 for adam
# 'beta2':0.99,
# 'clip_norm':20.0, # gradient clipping, max norm
# 'weight_decay':2.0,
# 'anneal_epochs':1000,
# 'min_anneal':0.1,
# }
# return conf
#
#
#
#
# def config_DHMM_cluster2():
# conf = {
#
# # Model Arguments
# 'input_dim': 88,
# 'z_dim':10,
# 's_dim': 10,
# 'h_dim': 10,
# 'e_dim': 30,
# 'emission_z_dim':50,
# 'emission_x_dim':100,
# 'trans_dim':20,
# 'rnn_dim':60,
# 'cluster_num':30,
# 'temp':1.0, # softmax temperature (lower --> more discrete)
# 'dropout':0, # dropout applied to layers (0 = no dropout)
# 'x_std': 0.1,
# 'z_std': 0.1,
# 'd': 2,
# # Training Arguments
# 'batch_size':20,
# 'epochs':5000, # maximum number of epochs
# 'min_epochs':2, # minimum number of epochs to train for
#
# 'lr':1e-2, # autoencoder learning rate
# 'beta1':0.9, # beta1 for adam
# 'beta2':0.99,
# 'clip_norm':20.0, # gradient clipping, max norm
# 'weight_decay':2.0,
# 'anneal_epochs':1000,
# 'min_anneal':0.1,
# }
# return conf
#
#
# def config_DHMM_cluster3():
# conf = {
#
# # Model Arguments
# 'input_dim': 88,
# 'z_dim':10,
# 's_dim': 10,
# 'h_dim': 10,
# 'e_dim': 50,
# 'emission_z_dim':50,
# 'emission_x_dim':100,
# 'trans_dim':20,
# 'rnn_dim':60,
# 'cluster_num':30,
# 'temp':1.0, # softmax temperature (lower --> more discrete)
# 'dropout':0, # dropout applied to layers (0 = no dropout)
# 'x_std': 0.1,
# 'z_std': 0.1,
# 'd': 2,
# # Training Arguments
# 'batch_size':20,
# 'epochs':5000, # maximum number of epochs
# 'min_epochs':2, # minimum number of epochs to train for
#
# 'lr':1e-2, # autoencoder learning rate
# 'beta1':0.9, # beta1 for adam
# 'beta2':0.99,
# 'clip_norm':20.0, # gradient clipping, max norm
# 'weight_decay':2.0,
# 'anneal_epochs':1000,
# 'min_anneal':0.1,
# }
# return conf
#
#
# def config_DHMM_cluster4():
# conf = {
#
# # Model Arguments
# 'input_dim': 88,
# 'z_dim':10,
# 's_dim': 10,
# 'h_dim': 10,
# 'e_dim': 50,
# 'emission_z_dim':50,
# 'emission_x_dim':100,
# 'trans_dim':20,
# 'rnn_dim':60,
# 'cluster_num':30,
# 'temp':1.0, # softmax temperature (lower --> more discrete)
# 'dropout':0, # dropout applied to layers (0 = no dropout)
# 'x_std': 0.1,
# 'z_std': 0.1,
# 'd': 2,
# # Training Arguments
# 'batch_size':20,
# 'epochs':5000, # maximum number of epochs
# 'min_epochs':2, # minimum number of epochs to train for
#
# 'lr':1e-2, # autoencoder learning rate
# 'beta1':0.9, # beta1 for adam
# 'beta2':0.99,
# 'clip_norm':20.0, # gradient clipping, max norm
# 'weight_decay':2.0,
# 'anneal_epochs':1000,
# 'min_anneal':0.1,
# }
# return conf