def next(self, state, weights, gradients):
optimizer.check_weights_gradients_match(weights, gradients)
if self._momentum is None:
updated_state = state
updated_weights = tf.nest.map_structure(
lambda w, g: w - self._lr * g, weights, gradients)
else:
_check_momentum_matches_weights(state, weights)
updated_state = tf.nest.map_structure(
lambda m, g: self._momentum * m + g, state, gradients)
updated_weights = tf.nest.map_structure(
lambda w, m: w - self._lr * m, weights, updated_state)
return updated_state, updated_weights
def next(self, state, weights, gradients):
gradients = optimizer.handle_indexed_slices_gradients(gradients)
optimizer.check_weights_gradients_match(weights, gradients)
lr = state[optimizer.LEARNING_RATE_KEY]
beta_1 = state[_BETA_1_KEY]
beta_2 = state[_BETA_2_KEY]
epsilon = state[_EPSILON_KEY]
step = state[_STEP_KEY] + 1
accumulator = state[_ACCUMULATOR_KEY]
preconditioner = state[_PRECONDITIONER_KEY]
optimizer.check_weights_state_match(weights, accumulator,
'accumulator')
optimizer.check_weights_state_match(weights, preconditioner,
'preconditioner')
updated_accumulator = tf.nest.map_structure(
lambda a, g: a + (g - a) * (1 - beta_1), accumulator, gradients)
def preconditioner_update(s, g):
g2 = tf.math.square(g)
sign = tf.sign(g2 - s)
return s + (1 - beta_2) * sign * g2
updated_preconditioner = tf.nest.map_structure(preconditioner_update,
preconditioner,
gradients)
normalized_lr = lr * tf.math.sqrt(
(1 - tf.math.pow(beta_2, tf.cast(step, tf.float32)))) / (
1 - tf.math.pow(beta_1, tf.cast(step, tf.float32)))
updated_weights = tf.nest.map_structure(
lambda w, g, a, s: w - normalized_lr * a /
(tf.math.sqrt(s) + epsilon), weights, gradients,
updated_accumulator, updated_preconditioner)
updated_state = collections.OrderedDict([
(optimizer.LEARNING_RATE_KEY, lr),
(_BETA_1_KEY, beta_1),
(_BETA_2_KEY, beta_2),
(_EPSILON_KEY, epsilon),
(_STEP_KEY, step),
(_ACCUMULATOR_KEY, updated_accumulator),
(_PRECONDITIONER_KEY, updated_preconditioner),
])
return updated_state, updated_weights
def next(self, state, weights, gradients):
gradients = optimizer.handle_indexed_slices_gradients(gradients)
optimizer.check_weights_gradients_match(weights, gradients)
lr = state[optimizer.LEARNING_RATE_KEY]
epsilon = state[_EPSILON_KEY]
preconditioner = state[_PRECONDITIONER_KEY]
optimizer.check_weights_state_match(weights, preconditioner,
'preconditioner')
updated_preconditioner = tf.nest.map_structure(
lambda a, g: a + tf.math.square(g), preconditioner, gradients)
updated_weights = tf.nest.map_structure(
lambda w, g, a: w - lr * g / tf.math.sqrt(a + epsilon), weights,
gradients, updated_preconditioner)
updated_state = collections.OrderedDict([
(optimizer.LEARNING_RATE_KEY, lr),
(_EPSILON_KEY, epsilon),
(_PRECONDITIONER_KEY, updated_preconditioner),
])
return updated_state, updated_weights
def next(self, state, weights, gradients):
gradients = optimizer.handle_indexed_slices_gradients(gradients)
optimizer.check_weights_gradients_match(weights, gradients)
lr = state[optimizer.LEARNING_RATE_KEY]
if _MOMENTUM_KEY not in state:
updated_weights = tf.nest.map_structure(lambda w, g: w - lr * g, weights,
gradients)
updated_state = collections.OrderedDict([(optimizer.LEARNING_RATE_KEY, lr)
])
else:
momentum = state[_MOMENTUM_KEY]
accumulator = state[_ACCUMULATOR_KEY]
optimizer.check_weights_state_match(weights, accumulator, 'accumulator')
updated_accumulator = tf.nest.map_structure(lambda a, g: momentum * a + g,
accumulator, gradients)
updated_weights = tf.nest.map_structure(lambda w, m: w - lr * m, weights,
updated_accumulator)
updated_state = collections.OrderedDict([
(optimizer.LEARNING_RATE_KEY, lr),
(_MOMENTUM_KEY, momentum),
(_ACCUMULATOR_KEY, updated_accumulator),
])
return updated_state, updated_weights
def test_check_weights_gradients_match(self, weights, gradients):
with self.assertRaises(ValueError):
optimizer.check_weights_gradients_match(weights, gradients)