def test_insert_along_axis_gradient():
x = tensor.matrix()
y = insert_columns(x, 10, range(0, 10, 2))
f = theano.function([x], tensor.grad(y.sum(), x))
f_val = f(np.random.normal(size=(7, 5)).astype(config.floatX))
assert np.all(f_val == 1)
assert f_val.shape == (7, 5)
y = insert_rows(x, 10, range(0, 10, 2))
f = theano.function([x], tensor.grad(y.sum(), x))
f_val = f(np.random.normal(size=(5, 6)).astype(config.floatX))
assert np.all(f_val == 1)
assert f_val.shape == (5, 6)
x = tensor.tensor3()
y = InsertAlongAxis(3, 1)(x, 10, range(0, 10, 2))
f = theano.function([x], tensor.grad(y.sum(), x))
f_val = f(np.random.normal(size=(2, 5, 2)).astype(config.floatX))
assert np.all(f_val == 1)
assert f_val.shape == (2, 5, 2)
def test_insert_along_axis():
x = tensor.matrix()
y = insert_columns(x, 10, range(0, 10, 2))
f = theano.function([x], y)
x_ = np.random.normal(size=(7, 5)).astype(config.floatX)
f_val = f(x_)
assert f_val.shape == (7, 10)
assert np.all(f_val[:, range(0, 10, 2)] == x_)
assert f_val.dtype == x_.dtype
y = insert_rows(x, 10, range(0, 10, 2))
f = theano.function([x], y)
x_ = np.random.normal(size=(5, 6)).astype(config.floatX)
f_val = f(x_)
assert f_val.shape == (10, 6)
assert np.all(f_val[range(0, 10, 2)] == x_)
assert f_val.dtype == x_.dtype
x = tensor.tensor3()
y = InsertAlongAxis(3, 1)(x, 10, range(0, 10, 2))
f = theano.function([x], y)
x_ = np.random.normal(size=(2, 5, 2)).astype(config.floatX)
f_val = f(x_)
assert f_val.shape == (2, 10, 2)
assert np.all(f_val[:, range(0, 10, 2), :] == x_)
assert f_val.dtype == x_.dtype
x = tensor.tensor3()
y = InsertAlongAxis(3, 1, fill=2)(x, 10, range(0, 10, 2))
f = theano.function([x], y)
x_ = np.random.normal(size=(2, 5, 2)).astype(config.floatX)
f_val = f(x_)
assert f_val.shape == (2, 10, 2)
assert np.all(f_val[:, range(0, 10, 2), :] == x_)
assert np.all(f_val[:, range(1, 10, 2), :] == 2)
assert f_val.dtype == x_.dtype
def __call__(self, batch):
if batch.ndim != 2:
raise ValueError("Only two-dimensional tensors are supported")
return insert_columns(batch, self._total, self._columns)
def __call__(self, batch):
if batch.ndim != 2:
raise ValueError("Only two-dimensional tensors are supported")
return insert_columns(batch, self._total, self._columns)
