def gen_inputs(batch_size, ilen, idim, olen, odim):
labels, ilens = sequence_utils.gen_random_sequence(
batch_size, ilen, idim)
xs = []
for l in ilens:
xs.append(np.random.rand(l, idim).astype(dtype=np.float32))
ys, _ = sequence_utils.gen_random_sequence(
batch_size, olen, odim)
return xs, ilens, ys
def main():
np.random.seed(314)
batch_size = 3
sequence_length = 4
num_vocabs = 10
num_hidden = 5
model_fn = MyLSTM(num_hidden, batch_size, sequence_length)
labels, lengths = sequence_utils.gen_random_sequence(
batch_size, sequence_length, num_vocabs)
xs = []
for l in lengths:
xs.append(np.random.rand(l, num_hidden).astype(dtype=np.float32))
h = np.zeros((batch_size, num_hidden), dtype=np.float32)
c = np.zeros((batch_size, num_hidden), dtype=np.float32)
mask = (np.expand_dims(np.arange(sequence_length), 0) < np.expand_dims(
lengths, 1)).astype(np.float32)
args = [xs, h, c, mask]
testtools.generate_testcase(model_fn, args)
import ch2o
if __name__ == '__main__':
import numpy as np
np.random.seed(314)
eprojs = 3
dunits = 4
att_dim = 5
batch_size = 3
sequence_length = 4
num_vocabs = 10
model_fn = lambda: AttDot(eprojs, dunits, att_dim)
labels, ilens = sequence_utils.gen_random_sequence(batch_size,
sequence_length,
num_vocabs)
xs = []
for l in ilens:
xs.append(np.random.rand(l, eprojs).astype(np.float32))
# Check if our modification is valid.
expected = model_fn().original(xs, None, None)
actual = model_fn().forward(xs, None, None)
for e, a in zip(expected, actual):
assert np.allclose(e.array, a.array)
ch2o.generate_testcase(model_fn, [xs, None, None])
z = np.random.rand(batch_size, dunits).astype(np.float32)
ch2o.generate_testcase(lambda: AttDotBackprop(eprojs, dunits, att_dim),