def testStringKeyGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant("key")
k2 = constant_op.constant("key2")
v = constant_op.constant(2.0)
v2 = constant_op.constant(22.0)
tape.watch(v2)
m = map_ops.tensor_map_insert(m, k2, v2)
m = map_ops.tensor_map_insert(m, k, v)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 2)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
self.assertAllClose(l, v)
m = map_ops.tensor_map_insert(m, k, v2)
l2 = map_ops.tensor_map_lookup(m, k, v2.dtype)
self.assertAllClose(l2, v2)
g = tape.gradient(l2 * 5, v2)
self.assertAllEqual(g, 5)
m, e = map_ops.tensor_map_erase(m, k, v2.dtype)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 1)
self.assertAllClose(e, v2)
g2 = tape.gradient(e * 6, v2)
self.assertAllEqual(g2, 6)
del tape
def testMultipleInsertLookupGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
k3 = constant_op.constant(3.0)
v = constant_op.constant(11.0)
v2 = constant_op.constant(12.0)
v3 = constant_op.constant(13.0)
tape.watch(v)
tape.watch(v2)
tape.watch(v3)
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
m = map_ops.tensor_map_insert(m, k3, v3)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
l2 = map_ops.tensor_map_lookup(m, k2, v2.dtype)
l3 = map_ops.tensor_map_lookup(m, k3, v3.dtype)
g = tape.gradient(l * 5, v)
g2 = tape.gradient(l2 * 6, v2)
g3 = tape.gradient(l3 * 7, v3)
self.assertAllEqual(g, 5)
self.assertAllEqual(g2, 6)
self.assertAllEqual(g3, 7)
del tape
def testTensorMapEraseFromEmptyMapFails(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
with self.assertRaisesRegex(errors.InvalidArgumentError,
"Trying to erase non-existent item."):
m = map_ops.tensor_map_erase(m, k, dtypes.float32)
self.evaluate(m)
def testStringKeyGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant("key")
k2 = constant_op.constant("key2")
v = constant_op.constant(2.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 2)
# Test lookup and gradient.
l = map_ops.tensor_map_lookup(m, k, v.dtype)
self.assertAllClose(l, v)
self.assertAllClose(tape.gradient(l * 5, v), 5)
# Test replace and gradient.
m = map_ops.tensor_map_insert(m, k, v2)
l2 = map_ops.tensor_map_lookup(m, k, v2.dtype)
self.assertAllClose(l2, v2)
g = tape.gradient(l2 * 6, v2)
self.assertAllEqual(g, 6)
# Test erase, has key, and gradient.
m = map_ops.tensor_map_erase(m, k, v2.dtype)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 1)
h = map_ops.tensor_map_has_key(m, k)
self.assertAllEqual(h, False)
l = map_ops.tensor_map_lookup(m, k2, v2.dtype)
g2 = tape.gradient(l * 6, v2)
self.assertAllEqual(g2, 6)
del tape
def testTensorMapInsert(self): m = map_ops.empty_tensor_map() k = constant_op.constant(1.0) v = constant_op.constant(2.0) m = map_ops.tensor_map_insert(m, k, v) s = map_ops.tensor_map_size(m) self.assertAllEqual(s, 1)
def testEraseFirstGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(11.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.tensor_map_insert(m, k, v)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
m = map_ops.tensor_map_insert(m, k2, v2)
m, e = map_ops.tensor_map_erase(m, k, v.dtype)
l2 = map_ops.tensor_map_lookup(m, k2, v2.dtype)
self.assertAllClose(l2, v2)
self.assertAllClose(e, v)
g = tape.gradient(l * 5, v)
self.assertAllEqual(g, 5)
g2 = tape.gradient(l2 * 6, v2)
self.assertAllEqual(g2, 6)
g3 = tape.gradient(e * 7, v)
self.assertAllEqual(g3, 7)
m, e2 = map_ops.tensor_map_erase(m, k2, v2.dtype)
g4 = tape.gradient(e2 * 8, v2)
self.assertAllEqual(g4, 8)
del tape
def testTensorMapLookup(self): m = map_ops.empty_tensor_map() k = constant_op.constant(1.0) v = constant_op.constant(2.0) m = map_ops.tensor_map_insert(m, k, v) l = map_ops.tensor_map_lookup(m, k, dtypes.float32) self.assertAllClose(l, v)
def testTensorMapLookupFromEmptyMapFails(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
with self.assertRaisesRegex(errors.InvalidArgumentError,
"Trying to lookup non-existent key."):
l = map_ops.tensor_map_lookup(m, k, dtypes.float32)
self.evaluate(l)
def testStackKeysEmptyMapFails(self):
m = map_ops.empty_tensor_map()
with self.assertRaisesRegex(
errors.InvalidArgumentError,
"TensorMapStackKeys cannot be called "
"on empty map."):
keys = map_ops.tensor_map_stack_keys(m, dtypes.float32)
self.evaluate(keys)
def testStackKeysIncorrectDtypeFails(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant("key_with_wrong_dtype")
v = constant_op.constant(2.0)
m = map_ops.tensor_map_insert(m, k, v)
simple = "Key does not match requested dtype."
with self.assertRaisesRegex(errors.InvalidArgumentError, simple):
keys = map_ops.tensor_map_stack_keys(m, dtypes.float32)
self.evaluate(keys)
def testTensorMapEraseMissingKeyFails(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(2.0)
m = map_ops.tensor_map_insert(m, k2, v)
with self.assertRaisesRegex(errors.InvalidArgumentError,
"Trying to erase non-existent item."):
m, e = map_ops.tensor_map_erase(m, k, dtypes.float32)
self.evaluate(e)
def testTensorMapLookupMissingKeyFails(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(11.0)
m = map_ops.tensor_map_insert(m, k, v)
with self.assertRaisesRegex(errors.InvalidArgumentError,
"Trying to lookup non-existent key."):
l = map_ops.tensor_map_lookup(m, k2, dtypes.float32)
self.evaluate(l)
def testInsertLookupGrad(self):
with backprop.GradientTape() as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
v = constant_op.constant(11.0)
tape.watch(v)
m = map_ops.tensor_map_insert(m, k, v)
l = map_ops.tensor_map_lookup(m, k, dtypes.float32)
l *= 5
g = tape.gradient(l, v)
self.assertAllEqual(g, 5)
def testTensorMapHasKey(self): m = map_ops.empty_tensor_map() k = constant_op.constant(1.0) k2 = constant_op.constant(2.0) v = constant_op.constant(2.0) m = map_ops.tensor_map_insert(m, k, v) # Check has key. b = map_ops.tensor_map_has_key(m, k) b2 = map_ops.tensor_map_has_key(m, k2) self.assertAllEqual(b, True) self.assertAllEqual(b2, False)
def testTensorMapErase(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
v = constant_op.constant(2.0)
m = map_ops.tensor_map_insert(m, k, v)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 1)
m, e = map_ops.tensor_map_erase(m, k, dtypes.float32)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 0)
self.assertAllClose(e, v)
def testStackKeysIncorrectShapeFails(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant([1.0, 11.0])
v = constant_op.constant(2.0)
v2 = constant_op.constant(22.0)
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
with self.assertRaisesRegex(errors.InvalidArgumentError,
"Keys must all have the same shape."):
keys = map_ops.tensor_map_stack_keys(m, dtypes.float32)
self.evaluate(keys)
def testInsertLookupComposeGrad(self):
with backprop.GradientTape() as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(11.0)
tape.watch(v)
m = map_ops.tensor_map_insert(m, k, v)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
m = map_ops.tensor_map_insert(m, k2, l)
l2 = map_ops.tensor_map_lookup(m, k2, l.dtype)
g = tape.gradient(l2 * 5, v)
self.assertAllEqual(g, 5)
def testVectorValue(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant([1.0, 2.0])
v = constant_op.constant([11.0, 22.0])
m = map_ops.tensor_map_insert(m, k, v)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 1)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
self.assertAllEqual(l, v)
m, e = map_ops.tensor_map_erase(m, k, v.dtype)
s = map_ops.tensor_map_size(m)
self.assertAllEqual(s, 0)
self.assertAllClose(e, v)
def testStringValue(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant("key")
v = constant_op.constant("value")
k2 = constant_op.constant(1.0)
v2 = constant_op.constant(2.0)
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
self.assertAllEqual(l, v)
l2 = map_ops.tensor_map_lookup(m, k2, v2.dtype)
self.assertAllClose(l2, v2)
m, e = map_ops.tensor_map_erase(m, k, v.dtype)
self.assertAllEqual(e, v)
def testVectorValue(self): m = map_ops.empty_tensor_map() k = constant_op.constant([1.0, 2.0]) v = constant_op.constant([11.0, 22.0]) # Test insert and lookup. m = map_ops.tensor_map_insert(m, k, v) s = map_ops.tensor_map_size(m) self.assertAllEqual(s, 1) l = map_ops.tensor_map_lookup(m, k, v.dtype) self.assertAllEqual(l, v) # Test erase and has key. m = map_ops.tensor_map_erase(m, k, v.dtype) s = map_ops.tensor_map_size(m) self.assertAllEqual(s, 0) self.assertAllEqual(map_ops.tensor_map_has_key(m, k), False)
def testSameKeyInsertLookupGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
v = constant_op.constant(2.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k, v2)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
g = tape.gradient(l * 5, v)
g2 = tape.gradient(l * 5, v2)
self.assertAllClose(g, array_ops.zeros_like(v))
self.assertAllClose(g2, 5)
def testEraseInsertComposedGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(11.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.tensor_map_insert(m, k, v)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
m = map_ops.tensor_map_erase(m, k, v.dtype)
m = map_ops.tensor_map_insert(m, k2, l)
l2 = map_ops.tensor_map_lookup(m, k2, l.dtype)
g = tape.gradient(l2 * 5, v)
self.assertAllEqual(g, 5)
del tape
def testIfHasKeyLookup(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(2.0)
m = map_ops.tensor_map_insert(m, k, v)
default_value = array_ops.zeros_like(v)
l = control_flow_ops.cond(
map_ops.tensor_map_has_key(m, k),
lambda: map_ops.tensor_map_lookup(m, k, dtypes.float32),
lambda: default_value)
l2 = control_flow_ops.cond(
map_ops.tensor_map_has_key(m, k2),
lambda: map_ops.tensor_map_lookup(m, k, dtypes.float32),
lambda: default_value)
self.assertAllClose(l, v)
self.assertAllClose(l2, default_value)
def testLookupMultiplyGrad(self):
with backprop.GradientTape(persistent=True) as tape:
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(11.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.empty_tensor_map()
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
l1 = map_ops.tensor_map_lookup(m, k, v.dtype)
l2 = map_ops.tensor_map_lookup(m, k2, v2.dtype)
g = tape.gradient(l1 * l2, [v, v2])
self.assertAllClose(g, [v2, v])
g2 = tape.gradient(l1 * l1, v)
self.assertAllClose(g2, 2 * v)
del tape
def testStringKeyValue(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant("key")
v = constant_op.constant("value")
k2 = constant_op.constant(1.0)
v2 = constant_op.constant(2.0)
# Test insert and lookup on string key-value pair.
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
self.assertAllEqual(l, v)
# Test lookup on float key-value pair.
l2 = map_ops.tensor_map_lookup(m, k2, v2.dtype)
self.assertAllClose(l2, v2)
# Test erase and has key.
self.assertAllEqual(map_ops.tensor_map_has_key(m, k), True)
m = map_ops.tensor_map_erase(m, k, v.dtype)
self.assertAllEqual(map_ops.tensor_map_has_key(m, k), False)
self.assertAllEqual(map_ops.tensor_map_has_key(m, k2), True)
def testStackKeys(self):
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
k3 = constant_op.constant(3.0)
v = constant_op.constant(21.0)
v2 = constant_op.constant(22.0)
v3 = constant_op.constant(23.0)
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
keys = map_ops.tensor_map_stack_keys(m, k.dtype)
expected = constant_op.constant([1.0, 2.0])
self.assertAllClose(array_ops.shape(keys), array_ops.shape(expected))
self.assertAllClose(sort_ops.sort(keys), expected)
m = map_ops.tensor_map_insert(m, k3, v3)
keys = map_ops.tensor_map_stack_keys(m, k.dtype)
expected = constant_op.constant([1.0, 2.0, 3.0])
self.assertAllClose(array_ops.shape(keys), array_ops.shape(expected))
self.assertAllClose(sort_ops.sort(keys), expected)
def testDiffKeySameValueGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
k2 = constant_op.constant(11.0)
v = constant_op.constant(2.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
l2 = map_ops.tensor_map_lookup(m, k2, v.dtype)
g = tape.gradient(l + l2, v)
self.assertAllEqual(g, 2)
m = map_ops.tensor_map_insert(m, k2, v2)
l2 = map_ops.tensor_map_lookup(m, k2, v2.dtype)
g2 = tape.gradient(l + l2, v2)
self.assertAllEqual(g2, 1)
del tape
def testLookupAddGrad(self):
with backprop.GradientTape(persistent=True) as tape:
k = constant_op.constant(1.0)
k2 = constant_op.constant(2.0)
v = constant_op.constant(11.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.empty_tensor_map()
m = map_ops.tensor_map_insert(m, k, v)
m = map_ops.tensor_map_insert(m, k2, v2)
l1 = map_ops.tensor_map_lookup(m, k, v.dtype)
l2 = map_ops.tensor_map_lookup(m, k2, v2.dtype)
g = tape.gradient(l1 + l2, [l1, l2])
self.assertAllClose(g, [1, 1])
g2 = tape.gradient(l1 + l2, [v, v2])
self.assertAllClose(g2, [1, 1])
g3 = tape.gradient(l1 + l2 * 4, v2)
self.assertAllEqual(g3, 4)
del tape
def testReplaceLookupGrad(self):
with backprop.GradientTape(persistent=True) as tape:
m = map_ops.empty_tensor_map()
k = constant_op.constant(1.0)
v = constant_op.constant(11.0)
v2 = constant_op.constant(22.0)
tape.watch(v)
tape.watch(v2)
m = map_ops.tensor_map_insert(m, k, v)
l = map_ops.tensor_map_lookup(m, k, v.dtype)
self.assertAllClose(l, v)
g = tape.gradient(l * 5, v)
self.assertAllEqual(g, 5)
m = map_ops.tensor_map_insert(m, k, v2)
l2 = map_ops.tensor_map_lookup(m, k, v2.dtype)
self.assertAllClose(l2, v2)
g2 = tape.gradient(l2 * 6, v)
g3 = tape.gradient(l2 * 7, v2)
self.assertAllClose(g2, array_ops.zeros_like(v))
self.assertAllClose(g3, 7)
del tape
def testEmptyTensorMapSize(self): m = map_ops.empty_tensor_map() s = map_ops.tensor_map_size(m) self.assertAllEqual(s, 0)
