def test_tensor_array_strided_slice_grad_grad():
shape = (3, 2, 2)
from tensorflow.python.ops.gen_array_ops import strided_slice_grad
x = tf.reshape(tf.range(0, numpy.prod(shape), dtype=tf.float32), shape)
x_ = tf.TensorArray(dtype=tf.float32,
size=shape[0],
element_shape=shape[1:])
for t in range(shape[0]):
x_ = x_.write(index=t, value=x[t])
x__ = x_.stack()
y = strided_slice_grad(shape=tf.convert_to_tensor(shape),
begin=tf.constant([0]),
end=tf.constant([0], dtype=tf.int32),
end_mask=1,
strides=tf.constant([1]),
dy=x__)
y.set_shape(x.get_shape())
vx, vx__, vy = session.run([x, x__, y])
print("vx, vx__, vy:", vx, vx__, vy)
assert_almost_equal(vx, vx__)
assert_almost_equal(vx, vy)
dy = tf.reshape(
tf.range(0, numpy.prod(shape), dtype=tf.float32) * 0.1 - 5.135, shape)
dx, = tf.gradients(ys=[y], xs=[x], grad_ys=[dy])
vdx, vdy = session.run([dx, dy])
print("vdx, vdy:", vdx, vdy)
assert_almost_equal(vdx, vdy)
def reverse_strided_slice(shape, out):
#if start == 0 and step == 1:
# return out
from tensorflow.python.ops.gen_array_ops import strided_slice_grad
out = strided_slice_grad(shape=tf.convert_to_tensor(shape),
begin=tf.constant([start_]),
end=tf.constant([0], dtype=tf.int32),
end_mask=1,
strides=tf.constant([step]),
dy=out)
assert isinstance(out, tf.Tensor)
out.set_shape(tf.TensorShape(shape))
return out
def test_strided_slice_grad_grad():
shape = (3, 2, 2)
from tensorflow.python.ops.gen_array_ops import strided_slice_grad
x = tf.reshape(tf.range(0, numpy.prod(shape), dtype=tf.float32), shape)
y = strided_slice_grad(shape=tf.convert_to_tensor(shape),
begin=tf.constant([0]),
end=tf.constant([0], dtype=tf.int32),
end_mask=1,
strides=tf.constant([1]),
dy=x)
y.set_shape(x.get_shape())
vx, vy = session.run([x, y])
print("vx, vy:", vx, vy)
assert_almost_equal(vx, vy)
dy = tf.reshape(
tf.range(0, numpy.prod(shape), dtype=tf.float32) * 0.1 - 5.135, shape)
dx, = tf.gradients(ys=[y], xs=[x], grad_ys=[dy])
vdx, vdy = session.run([dx, dy])
print("vdx, vdy:", vdx, vdy)
assert_almost_equal(vdx, vdy)