Variable sized inputs

[amiraxox]

Hi everyone 👍

I'm quite new to DyNet and I'm having a bit of a problem implementing a neural network with variable sized inputs. I started with the XOR example provided below (Link from DyNet examples ) The problem is that although in the code, the size of input is dynamically assigned "len(inputs)", it does not work if we change the input size of the learning set because the weight matrix W is already fixed which causes errors while calculating the output . Any suggestions please? I would be very grateful. Thank you

import dynet as dy
# create training instances, as before
def create_xor_instances(num_rounds=2000):
    questions = []
    answers = []
    for round in range(num_rounds):
        for x1 in 0,1:
            for x2 in 0,1:
                answer = 0 if x1==x2 else 1
                questions.append((x1,x2))
                answers.append(answer)
    return questions, answers

questions, answers = create_xor_instances()

# create a network for the xor problem given input and output
def create_xor_network(W, V, b, inputs, expected_answer):
    dy.renew_cg() # new computation graph
    x = dy.vecInput(len(inputs))
    x.set(inputs)
    y = dy.scalarInput(expected_answer)
    output = dy.logistic(V*(dy.tanh((W*x)+b)))
    loss =  dy.binary_log_loss(output, y)
    return loss

m2 = dy.ParameterCollection()
W = m2.add_parameters((8,2))
V = m2.add_parameters((1,8))
b = m2.add_parameters((8))
trainer = dy.SimpleSGDTrainer(m2)

seen_instances = 0
total_loss = 0
for question, answer in zip(questions, answers):
    loss = create_xor_network(W, V, b, question, answer)
    seen_instances += 1
    total_loss += loss.value()
    loss.backward()
    trainer.update()
    if (seen_instances > 1 and seen_instances % 100 == 0):
        print("average loss is:",total_loss / seen_instances)