class GRUNet(nn.Module):
def __init__(self,
input_dim,
hidden_dim,
output_dim,
batch_size,
n_layers,
backwards,
drop_prob=0.5,
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.drop = nn.Dropout(p=drop_prob)
self.inp_width = int(self.backwards * self.input_dim)
self.GRU = GRU(input_size=input_dim,
hidden_size=hidden_dim,
num_layers=n_layers)
'''
self.r1_dnet = Deepnet(self.inp_width,
[[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.inp_width, nested=2, droprate=drop_prob)
'''
self.out_lin = nn.Linear(hidden_dim, output_dim)
def init_hidden(self, device):
h = self.GRU.init_hidden(device)
return h
def forward(self, x, h):
bs = x.shape[0]
#out = x.view(bs,self.inp_width)
#out = self.r1_dnet(self.drop(out))
#out = self.drop(out)
out, h = self.GRU(x, h)
out = self.out_lin(out[:, -1])
return out, h
class GRUNet(nn.Module):
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_hidden(self, batch_size,device):
hidden = self.gru1.init_hidden(batch_size,device)
return hidden
def forward(self, x, h):
bs = x.shape[0]
#out = self.conv1(x)
#out = self.relu(self.conv2(out.view(bs,1,self.backwards*3,self.input_dim)))
#out = self.pool1(out)
#out = self.redu1(out).view(bs,self.backwards*3,int(self.input_dim/3))
#out = self.relu(out)
#'''
out = x.transpose(0,1)
out,attn = self.mha1(out,out,out)
out = out.transpose(0, 1)
out = self.relu(self.fc1(out.reshape(bs, self.backwards * self.input_dim)))
out = self.drop(out)
out = self.r1_dnet(out)
out = self.relu(self.fc2(out))
out = self.fc3(out)
return out, h
class GRUNet(nn.Module):
def __init__(self,
input_dim,
hidden_dim,
output_dim,
batch_size,
n_layers,
backwards,
drop_prob=0.40,
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, 8))
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(
backwards * 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_hidden(self, batch_size, device):
hidden = self.gru1.init_hidden(batch_size, device)
return hidden
def forward(self, x, h):
bs = x.shape[0]
out = self.relu(self.lin_in(x)) #input->hidden dim encoder
out = self.drop(out)
out = out.transpose(0, 1) #mha1 batch transpose
for i in range(len(self.attn_list)):
tmp, attn = self.attn_list[i](out, out, out)
#out = torch.layer_norm(torch.add(tmp, out), tmp.shape)
out = self.layernorm[i](torch.add(tmp, out))
out = out.transpose(0, 1)
out = out.reshape(bs, self.backwards * self.hidden_dim)
#out = self.relu( self.fc1(out))
#out = self.drop(out)
out = self.r1_dnet(out)
out = self.fc3(out)
return out, h