def __init__(self, vocab_size, n_bits, n_hidden):
super(ReinforcedReceiver, self).__init__()
self.emb_message = core.RelaxedEmbedding(vocab_size, n_hidden)
self.emb_column = nn.Linear(n_bits, n_hidden)
self.fc1 = nn.Linear(2 * n_hidden, 2 * n_hidden)
self.fc2 = nn.Linear(2 * n_hidden, n_bits)
def __init__(self, vocab_size, n_classes, deeper):
super(Receiver, self).__init__()
self.message_inp = core.RelaxedEmbedding(vocab_size, 400)
if not deeper:
self.fc = nn.Linear(400, n_classes)
else:
self.fc1 = nn.Linear(400, 400)
self.fc2 = nn.Linear(400, n_classes)
self.deeper = deeper
def __init__(self, vocab_size, n_classes, n_hidden=0):
super(Receiver, self).__init__()
self.message_inp = core.RelaxedEmbedding(vocab_size, 400)
hidden = []
for _ in range(n_hidden):
hidden.extend([
nn.LeakyReLU(),
nn.Linear(400, 400),
])
self.hidden = nn.Sequential(*hidden)
self.fc = nn.Linear(400, n_classes)
def __init__(self, vocab_size, n_classes):
super(Receiver, self).__init__()
self.message_inp = core.RelaxedEmbedding(vocab_size, 400)
self.fc = nn.Linear(400, n_classes)
def __init__(self, other_agent, vocab_size1, vocab_size2):
super(Proxy, self).__init__()
self.inp = core.RelaxedEmbedding(vocab_size1, vocab_size2)
self.other_agent = other_agent
