Python NativeOp.crossentropy_softmax_and_gradient_z_sparse__slow Examples

Example #1

File: test_NativeOp_sparse.py Project: tazdriver/returnn

def test_crossentropy_softmax_and_gradient_z_sparse():
    n_time = 3
    n_batch = 2
    n_dim = 5
    s0 = np.array([[0, 0], [0, 1], [1, 1], [1, 2], [1, 2], [2, 2], [2, 2]],
                  dtype=f32)
    s1 = np.array([[1, 2], [2, 3], [1, 1], [2, 0], [4, 1], [3, 3], [4, 4]],
                  dtype=f32)
    w = np.array(
        [[.3, 1], [.7, .4], [.1, .6], [.3, .2], [.6, .3], [.4, .5], [.6, 9]],
        dtype=f32)
    m = np.array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 0]],
                 dtype=f32)
    print("y_target:\n%r" %
          NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval())
    np.random.seed(123)
    z = np.random.randn(n_time, n_batch, n_dim).astype(f32)
    print("z:\n%r" % z)
    print("y (softmax(z)):\n%r" % TheanoUtil.softmax(z).eval())
    z_mask = np.array([[1, 1], [1, 1], [1, 1]], dtype=f32)
    args = (z, z_mask, s0, s1, w, m)
    ce1, gradz1 = NativeOp.crossentropy_softmax_and_gradient_z_sparse(*args)
    ce2, gradz2 = NativeOp.crossentropy_softmax_and_gradient_z_sparse__slow(
        *args)
    ce1 = ce1.eval()
    ce2 = ce2.eval()
    gradz1 = gradz1.eval()
    gradz2 = gradz2.eval()
    print("ce1:\n%r" % ce1)
    print("ce2:\n%r" % ce2)
    print("gradz1:\n%r" % gradz1)
    print("gradz2:\n%r" % gradz2)
    assert_almost_equal(ce1, ce2)
    assert_almost_equal(gradz1, gradz2)

Example #2

File: test_NativeOp_sparse.py Project: rwth-i6/returnn

def test_crossentropy_softmax_and_gradient_z_sparse():
  n_time = 3
  n_batch = 2
  n_dim = 5
  s0 = np.array([[0, 0], [0, 1],  [1, 1],  [1, 2],  [1, 2],  [2, 2],  [2, 2]], dtype=f32)
  s1 = np.array([[1, 2], [2, 3],  [1, 1],  [2, 0],  [4, 1],  [3, 3],  [4, 4]], dtype=f32)
  w =  np.array([[.3,1], [.7,.4], [.1,.6], [.3,.2], [.6,.3], [.4,.5], [.6,9]], dtype=f32)
  m =  np.array([[1, 1], [1, 1],  [1, 1],  [1, 1],  [1, 1],  [1, 1],  [1, 0]], dtype=f32)
  print("y_target:\n%r" % NativeOp.sparse_to_dense(s0, s1, w, m, n_time, n_dim).eval())
  np.random.seed(123)
  z = np.random.randn(n_time, n_batch, n_dim).astype(f32)
  print("z:\n%r" % z)
  print("y (softmax(z)):\n%r" % TheanoUtil.softmax(z).eval())
  z_mask = np.array([[1,1], [1,1], [1,1]], dtype=f32)
  args = (z, z_mask, s0, s1, w, m)
  ce1, gradz1 = NativeOp.crossentropy_softmax_and_gradient_z_sparse(*args)
  ce2, gradz2 = NativeOp.crossentropy_softmax_and_gradient_z_sparse__slow(*args)
  ce1 = ce1.eval()
  ce2 = ce2.eval()
  gradz1 = gradz1.eval()
  gradz2 = gradz2.eval()
  print("ce1:\n%r" % ce1)
  print("ce2:\n%r" % ce2)
  print("gradz1:\n%r" % gradz1)
  print("gradz2:\n%r" % gradz2)
  assert_almost_equal(ce1, ce2)
  assert_almost_equal(gradz1, gradz2)