Hi everyone,
I want to do something similar to fine tuning - add a layer to a trained network, but I want to add the layer at the beginning and not in the end so the output types are the same as before, How should I do that?
Here is how the original model looks like:
class ActorCritic(torch.nn.Module):
    def __init__(self, num_inputs, action_space):
        super(ActorCritic, self).__init__()
        self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
        self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1)

        self.lstm = nn.LSTMCell(32 * 3 * 3, 256)

        num_outputs = action_space.n
        self.critic_linear = nn.Linear(256, 1)
        self.actor_linear = nn.Linear(256, num_outputs)

        self.apply(weights_init)
        self.actor_linear.weight.data = normalized_columns_initializer(
            self.actor_linear.weight.data, 0.01)
        self.actor_linear.bias.data.fill_(0)
        self.critic_linear.weight.data = normalized_columns_initializer(
            self.critic_linear.weight.data, 1.0)
        self.critic_linear.bias.data.fill_(0)

        self.lstm.bias_ih.data.fill_(0)
        self.lstm.bias_hh.data.fill_(0)
        self.train()

    def forward(self, inputs):
        inputs, (hx, cx) = inputs
        x = F.elu(self.conv1(inputs))
        x = F.elu(self.conv2(x))
        x = F.elu(self.conv3(x))
        x = F.elu(self.conv4(x))
        x = x.view(-1, 32 * 3 * 3)
        hx, cx = self.lstm(x, (hx, cx))
        x = hx
       return self.critic_linear(x), self.actor_linear(x), (hx, cx)

I want to add a layer (convolutional if possible) before conv1 that will receive the same input as conv1 and pass the output to it.