def test_crossentropy_softmax_and_gradient_z_sparse_viterbi():
n_time = 3
n_batch = 2
n_dim = 5
alignment = np.array([[0, 1], [1, 2], [2, 3]], dtype="int32")
mask = np.array([[1, 1], [1, 1], [1, 1]], dtype=f32)
y_t, y_i, y_w, y_mask = NativeOp.onehot_to_sparse(alignment, mask)
np.random.seed(123)
z = np.random.randn(n_time, n_batch, n_dim).astype(f32)
z_mask = np.array([[1,1], [1,1], [1,1]], dtype=f32)
nll1, _pcx1 = T.nnet.crossentropy_softmax_1hot(x=T.as_tensor_variable(z).reshape((n_time * n_batch, n_dim)), y_idx=T.as_tensor_variable(alignment).reshape((n_time * n_batch,)))
nll2, _gradz2 = NativeOp.crossentropy_softmax_and_gradient_z_sparse(z, z_mask, y_t, y_i, y_w, y_mask)
nll1 = nll1.eval()
nll2 = nll2.eval()
print("nll1:\n%r" % nll1)
print("nll2:\n%r" % nll2)
def test_crossentropy_softmax_and_gradient_z_sparse_viterbi():
n_time = 3
n_batch = 2
n_dim = 5
alignment = np.array([[0, 1], [1, 2], [2, 3]], dtype="int32")
mask = np.array([[1, 1], [1, 1], [1, 1]], dtype=f32)
y_t, y_i, y_w, y_mask = NativeOp.onehot_to_sparse(alignment, mask)
np.random.seed(123)
z = np.random.randn(n_time, n_batch, n_dim).astype(f32)
z_mask = np.array([[1, 1], [1, 1], [1, 1]], dtype=f32)
nll1, _pcx1 = T.nnet.crossentropy_softmax_1hot(
x=T.as_tensor_variable(z).reshape((n_time * n_batch, n_dim)),
y_idx=T.as_tensor_variable(alignment).reshape((n_time * n_batch, )))
nll2, _gradz2 = NativeOp.crossentropy_softmax_and_gradient_z_sparse(
z, z_mask, y_t, y_i, y_w, y_mask)
nll1 = nll1.eval()
nll2 = nll2.eval()
print("nll1:\n%r" % nll1)
print("nll2:\n%r" % nll2)
