def __init__(self, src_filedir, tar_filedir, hidden_size=100):
self.src_i2w, self.src_w2i, self.X_train = self.process_dataset(src_filedir)
self.tar_i2w, self.tar_w2i, self.Y_train = self.process_dataset(tar_filedir, True)
self.src_vocab_size = len(self.src_w2i)
self.tar_vocab_size = len(self.tar_w2i)
self.hidden_size = hidden_size
# encoder
self.l1 = rm.Embedding(hidden_size, self.src_vocab_size)
self.l2 = rm.Lstm(hidden_size)
# decoder
self.l3 = rm.Embedding(hidden_size, self.tar_vocab_size)
self.l4 = rm.Lstm(hidden_size)
self.l5 = rm.Dense(self.tar_vocab_size)
def __init__(self):
self.d1=rm.Dense(32)
self.d2=rm.Dense(32)
self.d3=rm.Dense(32)
self.d4=rm.Dense(1)
self.emb = rm.Embedding(32,6)
self.ad1 = rm.Dense(32)
self.r=rm.Relu()
def test_embedding(node, use_gpu):
node = Variable(node)
assert_cuda_active(use_gpu)
layer = rm.Embedding(output_size=2, input_size=2)
def func(node):
return sum(layer(node))
compare(func, layer.params["w"], node)