Size Mismatch when passing a state batch to network

Hi all. Since I’m a beginner in ML, this question or the design overall may sound silly, sorry about that. I’m open to any suggestions.

I have a simple network with three linear layers one of which is output layer.

    self.fc1 = nn.Linear(in_features=2, out_features=12)
	self.fc2 = nn.Linear(in_features=12, out_features=16)
	self.out = nn.Linear(in_features=16, out_features=4)

My states are consisting of two values, coordinate x and why. That’s why input layer has two features.

In I’m sampling and extracting memories in ReplayMemory class and pass them to get_current function:

        experiences = memory.sample(batch_size)
		states, actions, rewards, next_states = qvalues.extract_tensors(experiences)

		current_q_values = qvalues.QValues.get_current(policy_net, states, actions)

Since a single state is consisting of two values, length of the states tensor is batchsize x 2 while length of the actions is batchsize. (Maybe that’s the problem?)

When I pass “states” to my network in get_current function to obtain predicted q-values for the state, I get this error:

size mismatch, m1: [1x16], m2: [2x12]

It looks like it is trying to grab the states tensor as if it is a single state tensor. I don’t want that. In the tutorials that I follow, they pass the states tensor which is a stack of multiple states, and there is no problem. What am I doing wrong? :slight_smile:

This is how I store an experience:

memory.push(dqn.Experience(state, action, next_state, reward))

This is my extract tensors function:

def extract_tensors(experiences):
    # Convert batch of Experiences to Experience of batches
    batch = dqn.Experience(*zip(*experiences))

    state_batch =[0] for d in experiences))
    action_batch =[1] for d in experiences))
    reward_batch =[2] for d in experiences))
    nextState_batch =[3] for d in experiences))


    return (state_batch,action_batch,reward_batch,nextState_batch)

Tutorial that I follow is this project’s tutorial.

Look between 148th and 169th lines. And especially 169th line where it passes the states batch to the network.

SOLVED. It turned out that I didn’t know how to properly create 2d tensor. 2D Tensor must be like this:

states = torch.tensor([[1, 1], [2,2]], dtype=torch.float)