def __init__(self,
input_dim,
hidden_dim,
output_dim,
n_layers,
backwards,
drop_prob=0.2,
GPUs=1):
super(GRUNet, self).__init__()
assert backwards % 4 == 0
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.backwards = backwards
self.GPUs = GPUs
self.drop_prob = drop_prob
self.drop = nn.Dropout(p=drop_prob)
self.conv1 = nn.Conv2d(1, 10, (3, 3), stride=1, padding=1)
self.c1deepnet = Deepnet(input_dim * backwards * 10,
[[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim]],
input_dim * backwards)
self.deepnet = Deepnet(input_dim * backwards,
[[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim]],
input_dim * backwards)
self.out_lin = nn.Linear(input_dim * backwards, output_dim)
def __init__(self,
input_dim,
hidden_dim,
output_dim,
batch_size,
n_layers,
backwards,
drop_prob=0.10,
GPUs=1):
super(GRUNet, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.batch_size = batch_size
self.n_layers = n_layers
self.backwards = backwards
self.GPUs = GPUs
self.drop_prob = drop_prob
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.attn_layers = 6
self.GPUs = GPUs
self.drop = nn.Dropout(p=drop_prob)
self.lin_in = nn.Linear(input_dim, hidden_dim)
self.attn_list = nn.ModuleList()
self.layernorm = nn.ModuleList()
for i in range(self.attn_layers):
self.attn_list.append(
torch.nn.MultiheadAttention(hidden_dim, int(hidden_dim / 4)))
self.layernorm.append(nn.LayerNorm(hidden_dim))
self.gru1 = GRU(int(input_dim / 1),
hidden_dim,
n_layers,
drop_prob,
batch_first=True)
self.r1_dnet = Deepnet(self.hidden_dim,
[[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim]],
self.hidden_dim,
nested=0,
droprate=drop_prob)
self.fc1 = nn.Linear(backwards * hidden_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, output_dim)
self.relu = nn.LeakyReLU()
def __init__(self, input_dim, hidden_dim, output_dim, batch_size, n_layers, backwards, drop_prob=0.10, GPUs=1):
super(GRUNet, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.batch_size = batch_size
self.n_layers = n_layers
self.backwards = backwards
self.GPUs = GPUs
self.drop_prob = drop_prob
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.GPUs = GPUs
self.drop = nn.Dropout(p=drop_prob)
'''
self.conv1 = nn.Conv1d(backwards,backwards*3,kernel_size=1,stride=1,padding=0)
#b*3, i
self.conv2 = nn.Conv2d(1,10,kernel_size=(7,3),stride=1,padding=(3,1))
#10,b*3,i
self.pool1 = nn.MaxPool2d((7,3),stride=(1,3),padding=(3,0))
#10,b*3,i/3
self.redu1 = nn.Conv2d(10,1,kernel_size=1,stride=1,padding=0)
#1,b*3,i/3
'''
self.mha1 = torch.nn.MultiheadAttention(input_dim, input_dim)
self.mha2 = torch.nn.MultiheadAttention(hidden_dim, hidden_dim//4)
self.mha3 = torch.nn.MultiheadAttention(hidden_dim, hidden_dim//4)
self.gru1 = GRU(int(input_dim/1), hidden_dim, n_layers, drop_prob, batch_first=True)
self.r1_dnet = Deepnet(self.hidden_dim,
[[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim]],
self.hidden_dim, nested=0, droprate=drop_prob)
self.fc1 = nn.Linear(backwards*input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, output_dim)
self.relu = nn.LeakyReLU()
def __init__(self,
input_dim,
hidden_dim,
output_dim,
n_layers,
backwards,
drop_prob=0.2,
GPUs=1):
super(GRUNet, self).__init__()
assert backwards % 8 == 0
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.backwards = backwards
self.GPUs = GPUs
self.drop_prob = drop_prob
self.drop = nn.Dropout(p=drop_prob)
self.inp_width = int(self.backwards * self.input_dim)
self.conv1a = torch.nn.Conv2d(1,
20, (5, 1),
stride=(1, 1),
padding=(2, 0)) # expand in c axis
self.conv1b = torch.nn.Conv2d(20,
40, (5, 3),
stride=(1, 1),
padding=(2, 1)) # expand in c axis
self.conv1c = torch.nn.Conv2d(40,
80, (5, 3),
stride=(1, 1),
padding=(2, 1)) # expand in c axis
self.pool1 = torch.nn.MaxPool2d(
(7, 1), stride=(2, 1), padding=(3, 0)) # maxpool to get features
self.redu1 = torch.nn.Conv2d(80,
10, (1, 1),
stride=(1, 1),
padding=(0, 0)) # reduce c
self.conv_r1 = int(10 * self.backwards / 2 * self.input_dim)
self.conv2a = torch.nn.Conv2d(10,
40, (5, 3),
stride=(1, 1),
padding=(2, 1)) # reduce in ohl axis
self.conv2b = torch.nn.Conv2d(40,
80, (5, 3),
stride=(1, 1),
padding=(2, 1)) # reduce in ohl axis
self.conv2c = torch.nn.Conv2d(80,
120, (5, 3),
stride=(1, 3),
padding=(2, 0)) # reduce in ohl axis
self.pool2 = torch.nn.MaxPool2d(
(7, 1), stride=(2, 1),
padding=(3, 0)) # extract features out of c2
self.redu2 = torch.nn.Conv2d(120,
20, (1, 1),
stride=(1, 1),
padding=(0, 0)) # reduce c
self.conv_r2 = int(20 * self.backwards / 4 * self.input_dim / 3)
self.conv3 = torch.nn.Conv2d(20,
80, (7, 3),
stride=(1, 1),
padding=(3, 1)) # reduce in ohl axis
self.pool3 = torch.nn.MaxPool2d(
(3, 3), stride=(2, 1),
padding=(1, 1)) # extract features out of c2
self.redu3a = torch.nn.Conv2d(80,
30, (1, 1),
stride=(1, 1),
padding=(0, 0)) # reduce c
self.redu3b = torch.nn.Conv2d(30,
10, (1, 1),
stride=(1, 1),
padding=(0, 0)) # reduce c
self.conv_r3 = int(10 * self.backwards / 8 * self.input_dim / 3)
self.r1_dnet = Deepnet(self.conv_r1,
[[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim]],
hidden_dim)
self.r2_dnet = Deepnet(self.conv_r2,
[[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim]],
hidden_dim)
self.r3_dnet = Deepnet(self.conv_r3,
[[hidden_dim, hidden_dim, hidden_dim],
[hidden_dim, hidden_dim, hidden_dim]],
hidden_dim)
self.out_lin = nn.Linear(hidden_dim * 3, output_dim)