def __init__(self,
input_size=11,
model_type='LSTM',
activation='ReLU',
bidirectional=False,
hidden_size=64,
num_layers=1,
linear_layers=None,
dropout_rate=0.5,
use_cuda=True,
cuda_num='cuda:0'):
super(Custom_RNN, self).__init__()
self.use_cuda = use_cuda
self.cuda_num = cuda_num
self.num_layers = num_layers
self.hidden_size = hidden_size
self.linear_layers = linear_layers
self.linear_layers.insert(0, hidden_size)
self.rnn_layer01 = model_config.set_recurrent_layer(
name=model_type,
input_size=input_size,
bidirectional=bidirectional,
hidden_size=hidden_size,
num_layers=num_layers)
self.linear_stack = nn.Sequential()
self.linear_stack = model_config.build_linear_layer(
layer=self.linear_stack,
linear_layers=self.linear_layers,
activation=activation,
dropout_rate=dropout_rate)
def __init__(self,
input_size=11,
recurrent_model='LSTM',
activation='PReLU',
bidirectional=False,
recurrent_hidden_size=64,
recurrent_num_layers=1,
linear=3,
cuda=True):
super(VanillaRecurrentNetwork, self).__init__()
self.activation = activation
self.hidden_size = recurrent_hidden_size
self.num_layers = recurrent_num_layers
self.cuda = cuda
self.recurrent01 = model_config.set_recurrent_layer(
name=recurrent_model,
input_size=input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True,
bidirectional=bidirectional)
if linear == 3:
self.linear_relu_stack = nn.Sequential(
nn.Linear(self.hidden_size, 64), nn.Dropout(0.3),
model_config.set_activation(self.activation),
nn.Linear(64, 64), nn.Dropout(0.3),
model_config.set_activation(self.activation), nn.Linear(64, 1))
elif linear == 4:
self.linear_relu_stack = nn.Sequential(
nn.Linear(self.hidden_size, 32), nn.Dropout(0.3),
model_config.set_activation(self.activation),
nn.Linear(32, 64), nn.Dropout(0.3),
model_config.set_activation(self.activation),
nn.Linear(64, 32), nn.Dropout(0.3),
model_config.set_activation(self.activation), nn.Linear(32, 1))
elif linear == 5:
self.linear_relu_stack = nn.Sequential(
nn.Linear(self.hidden_size, 32), nn.Dropout(0.3),
model_config.set_activation(self.activation),
nn.Linear(32, 64), nn.Dropout(0.3),
model_config.set_activation(self.activation),
nn.Linear(64, 64), nn.Dropout(0.3),
model_config.set_activation(self.activation),
nn.Linear(64, 32), nn.Dropout(0.3),
model_config.set_activation(self.activation), nn.Linear(32, 1))
def __init__(self,
input_size=11,
model_type='LSTM',
activation='ReLU',
bidirectional=False,
hidden_size=64,
num_layers=1,
linear_layers=None,
dropout_rate=0.5,
use_cuda=True,
convolution_layer=3,
use_batch_norm=True,
cuda_num='cuda:0'):
super(Custom_CRNN, self).__init__()
# convolution layer를 더 쌓는게 맞을지 고민해봐야 할것 같음
# nn.Conv1d(input_channel, output_channel, kernel_size)\
self.num_layers = num_layers
self.hidden_size = hidden_size
self.linear_layers = linear_layers
self.linear_layers.insert(0, hidden_size)
self.use_cuda = use_cuda
self.cuda_num = cuda_num
self.conv1d_stack = nn.Sequential()
self.conv1d_stack = model_config.build_conv1d_layer(
self.conv1d_stack,
convolution_layers=convolution_layer,
input_size=input_size)
self.rnn_layer01 = model_config.set_recurrent_layer(
name=model_type,
input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
bidirectional=bidirectional)
self.linear_stack = nn.Sequential()
self.linear_stack = model_config.build_linear_layer(
layer=self.linear_stack,
linear_layers=self.linear_layers,
activation=activation,
dropout_rate=dropout_rate,
use_batch_norm=use_batch_norm)
def __init__(self,
input_size=11,
recurrent_model='LSTM',
activation='PReLU',
bidirectional=False,
recurrent_num_layeres=1,
recurrent_hidden_size=256):
super(VanillaCRNNNetwork, self).__init__()
# convolution
self.num_layers = recurrent_num_layeres
self.hidden_size = recurrent_hidden_size
self.activation = activation
self.conv1d_layer = nn.Conv1d(input_size, input_size, 3)
self.lstm_layer = model_config.set_recurrent_layer(
name=recurrent_model,
input_size=input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
batch_first=True,
bidirectional=bidirectional)
self.linear_layer1 = nn.Linear(256, 64)
self.dropout = nn.Dropout(0.3)
self.activation = model_config.set_activation(self.activation)
self.linear_layer2 = nn.Linear(64, 1)