def test_div_no_nan(self):
x = np.array([1, -1, 0, 1, -1], np.float32)
y = np.array([1, 2, 0, 0, 0], np.float32)
result = divide_no_nan(x, y)
np.testing.assert_equal(result, [1, -0.5, 0, 0, 0])
sess = tf.InteractiveSession()
x = tf.convert_to_tensor(x)
y = tf.convert_to_tensor(y)
result = divide_no_nan(x, y).eval()
np.testing.assert_equal(result, [1, -0.5, 0, 0, 0])
def test_multimode_pad(self):
a = np.array([[1,2], [3,4]])
print(a)
p = pad(a, [[1,1], [1,1]], mode=['symmetric', ['wrap', 'constant']], constant_values=[0, [0, 10]])
np.testing.assert_equal(p[0,1:-1], [1,2])
np.testing.assert_equal(p[3,1:-1], [3,4])
np.testing.assert_equal(p[1:-1,0], [2,4])
np.testing.assert_equal(p[1:-1,3], [10, 10])
print(p)
tf.InteractiveSession()
a_tf = tf.constant(a, tf.float32, shape=(2,2))
p_tf = pad(a_tf, [[1,1], [1,1]], mode=['symmetric', ['wrap', 'constant']], constant_values=[0, [0, 10]])
np.testing.assert_equal(p, p_tf.eval())
def test_unstack(self):
tensor = random_uniform([1,2,3,4])
for dim in range(len(tensor.shape)):
components = unstack(tensor, dim, keepdims=True)
np.testing.assert_equal(tensor, concat(components, axis=dim))
components = unstack(tensor, dim, keepdims=False)
np.testing.assert_equal(tensor, stack(components, axis=dim))
# --- TensorFlow ---
sess = tf.InteractiveSession()
for dim in range(len(tensor.shape)):
components = unstack(tf.constant(tensor), dim, keepdims=True)
np.testing.assert_equal(tensor, concat(components, axis=dim).eval())
components = unstack(tf.constant(tensor), dim, keepdims=False)
np.testing.assert_equal(tensor, stack(components, axis=dim).eval())
def test_laplace_padding(self):
tf.InteractiveSession()
for dims in range(1, 4):
shape = [2] + [4] * dims + [3]
a = zeros(shape)
l = laplace(a, padding='replicate')
np.testing.assert_equal(l, 0)
np.testing.assert_equal(l.shape, a.shape)
l = laplace(a, padding='reflect')
np.testing.assert_equal(l, 0)
np.testing.assert_equal(l.shape, a.shape)
l = laplace(a, padding='circular')
np.testing.assert_equal(l, 0)
np.testing.assert_equal(l.shape, a.shape)
l = laplace(a, padding='valid')
np.testing.assert_equal(l, 0)
np.testing.assert_equal(l.shape, [2] + [2] * dims + [3])
def test_fft(self):
tf.InteractiveSession()
for dims in range(1, 4):
shape = [2] + [4] * dims + [3]
x_np = np.random.randn(*shape) + 1j * np.random.randn(*shape)
x_np = x_np.astype(np.complex64)
x_tf = tf.constant(x_np, tf.complex64)
k_np = fft(x_np)
k_tf = fft(x_tf)
self.assertLess(max(abs(k_np - k_tf.eval())), 1e-3)
x_np = ifft(k_np)
x_tf = ifft(k_tf)
self.assertLess(max(abs(x_np - x_tf.eval())), 1e-3)
def test_pad_circular(self):
tf.InteractiveSession()
# --- 1D ---
a = np.array([1, 2, 3, 4, 5])
a_ = pad(a, [[2, 3]], mode='circular')
np.testing.assert_equal(a_, [4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
a = tf.constant(a)
a_ = pad(a, [[2, 3]], mode='circular').eval()
np.testing.assert_equal(a_, [4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
# --- 2D + batch ---
t = [[3, 1, 2, 3, 1], [6, 4, 5, 6, 4], [3, 1, 2, 3, 1]]
a = np.array([[1, 2, 3], [4, 5, 6]]).reshape([1, 2, 3, 1])
a_ = pad(a, [[0, 0], [0, 1], [1, 1], [0, 0]], mode='circular')
np.testing.assert_equal(a_.shape, [1, 3, 5, 1])
np.testing.assert_equal(a_.reshape([3, 5]), t)
a = tf.constant(a)
a_ = pad(a, [[0, 0], [0, 1], [1, 1], [0, 0]], mode='circular').eval()
np.testing.assert_equal(a_.shape, [1, 3, 5, 1])
np.testing.assert_equal(a_.reshape([3, 5]), t)
