def __init__(self, inp_size, hidden_size_1, hidden_size_2, hidden_size_3,
output_size):
super(network, self).__init__()
self.gru_1 = nn.GRU(inp_size, hidden_size_1)
# for the conditioning weights weights
self.cond_gauss = gv.conditional_gaussians
self.cond_linear = nn.Linear(hidden_size_1, self.cond_gauss * 3)
self.conditioner = cm.conditioner()
self.condition_len = len(self.conditioner.string)
self.gru_2 = nn.GRU(hidden_size_1 + self.condition_len, hidden_size_2)
self.linear_1 = nn.Linear(hidden_size_2 + self.condition_len,
hidden_size_3)
self.linear_2 = nn.Linear(
hidden_size_3 + hidden_size_2 + hidden_size_1 + self.condition_len,
output_size)
self.inp_size = inp_size
self.hidden_size_1 = hidden_size_1
self.hidden_size_2 = hidden_size_2
self.hidden_size_3 = hidden_size_3
self.output_size = output_size
# for training
self.drop = nn.Dropout(0.4)
## not being used as its not overfitting to train
self.relu = nn.ReLU()
self.go = graves_output.network()
def __init__(self, inp_size, hidden_size_1, output_size):
super(network, self).__init__()
self.gru_1 = nn.GRU(inp_size, hidden_size_1)
self.linear = nn.Linear(hidden_size_1, output_size)
self.hidden_size_1 = hidden_size_1
self.inp_size = inp_size
# for training
self.drop = nn.Dropout(0.4)
self.relu = nn.ReLU()
self.go = graves_output.network()
def __init__(self, inp_size, hidden_size_1,hidden_size_2,hidden_size_3,output_size):
super(network, self).__init__()
self.gru_1 = nn.GRU(inp_size, hidden_size_1)
self.gru_2 = nn.GRU(hidden_size_1+inp_size, hidden_size_2)
self.linear_1 = nn.Linear(hidden_size_2+inp_size,hidden_size_3)
self.linear_2 = nn.Linear(hidden_size_3+hidden_size_2+hidden_size_1,output_size)
self.inp_size = inp_size
self.hidden_size_1 = hidden_size_1
self.hidden_size_2 = hidden_size_2
self.hidden_size_3 = hidden_size_3
self.output_size = output_size
# for training
self.drop = nn.Dropout(0.4)
## not being used as its not overfitting to train
self.relu = nn.ReLU()
self.go = graves_output.network()