def testAdagradDAWithL1_L2(self):
for dtype in self.float_types:
with self.test_session(), self.test_scope():
global_step = resource_variable_ops.ResourceVariable(
0, dtype=dtypes.int64)
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0],
dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([4.0, 3.0],
dtype=dtype)
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
opt = adagrad_da.AdagradDAOptimizer(
3.0,
global_step,
initial_gradient_squared_accumulator_value=0.1,
l1_regularization_strength=0.001,
l2_regularization_strength=2.0)
update = opt.apply_gradients(zip([grads0, grads1],
[var0, var1]),
global_step=global_step)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
self.assertAllCloseAccordingToType([4.0, 3.0], var1.eval())
# Run a step of AdagradDA
update.run()
self.assertAllCloseAccordingToType(
np.array([-0.046907, -0.093659]), var0.eval())
self.assertAllCloseAccordingToType(
np.array([-0.004275, -0.009023]), var1.eval())
def testAdagradDAWithL1_L2(self):
for dtype in [dtypes.float64, dtypes.float32]:
with self.cached_session() as sess:
global_step = variables.Variable(0, dtype=dtypes.int64)
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
opt = adagrad_da.AdagradDAOptimizer(
3.0,
global_step,
initial_gradient_squared_accumulator_value=0.1,
l1_regularization_strength=0.001,
l2_regularization_strength=2.0)
update = opt.apply_gradients(zip([grads0, grads1],
[var0, var1]),
global_step=global_step)
self.evaluate(variables.global_variables_initializer())
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
# Run a step of AdagradDA
update.run()
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllCloseAccordingToType(
np.array([-0.046907, -0.093659]), v0_val)
self.assertAllCloseAccordingToType(
np.array([-0.004275, -0.009023]), v1_val)
def testAdagradDAwithoutRegularizationBasic2(self):
for dtype in [dtypes.float64, dtypes.float32]:
with ops.Graph().as_default(), self.cached_session():
global_step = variables.Variable(0, dtype=dtypes.int64)
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([4.0, 3.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
opt = adagrad_da.AdagradDAOptimizer(
3.0,
global_step,
initial_gradient_squared_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0)
update = opt.apply_gradients(zip([grads0, grads1],
[var0, var1]),
global_step=global_step)
self.evaluate(variables.global_variables_initializer())
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllCloseAccordingToType([1.0, 2.0], v0_val)
self.assertAllCloseAccordingToType([4.0, 3.0], v1_val)
# Run a step of AdagradDA
update.run()
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllCloseAccordingToType(
np.array([-0.904534, -1.603567]), v0_val)
self.assertAllCloseAccordingToType(
np.array([-0.094821, -0.189358]), v1_val)
def testAdagradDAwithoutRegularizationBasic2(self):
for dtype in self.float_types:
with self.session(), self.test_scope():
global_step = resource_variable_ops.ResourceVariable(
0, dtype=dtypes.int64)
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0],
dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([4.0, 3.0],
dtype=dtype)
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
opt = adagrad_da.AdagradDAOptimizer(
3.0,
global_step,
initial_gradient_squared_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0)
update = opt.apply_gradients(zip([grads0, grads1],
[var0, var1]),
global_step=global_step)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType([1.0, 2.0],
self.evaluate(var0))
self.assertAllCloseAccordingToType([4.0, 3.0],
self.evaluate(var1))
# Run a step of AdagradDA
update.run()
self.assertAllCloseAccordingToType(
np.array([-0.904534, -1.603567]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([-0.094821, -0.189358]), self.evaluate(var1))
def testAdagradDAWithoutRegularizationBasic1(self):
for dtype in self.float_types:
with self.cached_session(), self.test_scope():
global_step = resource_variable_ops.ResourceVariable(
0, dtype=dtypes.int64)
var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
opt = adagrad_da.AdagradDAOptimizer(
3.0,
global_step,
initial_gradient_squared_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0)
update = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]), global_step=global_step)
variables.global_variables_initializer().run()
self.assertAllClose([0.0, 0.0], self.evaluate(var0))
self.assertAllClose([0.0, 0.0], self.evaluate(var1))
# Run a step of AdagradDA
update.run()
# Let g to be gradient accumulator, gg to be gradient squared
# accumulator, T be the global step, lr is the learning rate, and k the
# initial gradient squared accumulator value.
# w = \dfrac{sign(-g)*lr*|g - l1*T|_{+}}{l2*T*lr + \sqrt{k+gg})}
# For -0.1*3.0*(0.1 - 0)/(0 + sqrt(0.1 + 0.1*0.1)) = -0.904534
# similarly for others.
self.assertAllCloseAccordingToType(
np.array([-0.904534, -1.603567]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([-0.094821, -0.189358]), self.evaluate(var1))
def test_ops_with_var_and_adagrad_da(self):
var_list = [
deo.get_variable('sp_var', initializer=0.0, dim=2),
]
gstep = training_util.create_global_step()
opt_list = [
adagrad_da.AdagradDAOptimizer(0.1, gstep),
]
self.common_run_context(var_list, opt_list, name='adagrad_da_test')
def doTestAdagradDAwithoutRegularizationBasic1(self, use_resource=False):
for dtype in [dtypes.float64, dtypes.float32]:
with ops.Graph().as_default(), self.cached_session():
global_step = variables.Variable(0, dtype=dtypes.int64)
if use_resource:
var0 = resource_variable_ops.ResourceVariable([0.0, 0.0],
dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([0.0, 0.0],
dtype=dtype)
else:
var0 = variables.Variable([0.0, 0.0], dtype=dtype)
var1 = variables.Variable([0.0, 0.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
opt = adagrad_da.AdagradDAOptimizer(
3.0,
global_step,
initial_gradient_squared_accumulator_value=0.1,
l1_regularization_strength=0.0,
l2_regularization_strength=0.0)
update = opt.apply_gradients(zip([grads0, grads1],
[var0, var1]),
global_step=global_step)
self.evaluate(variables.global_variables_initializer())
v0_val, v1_val = self.evaluate([var0, var1])
self.assertAllClose([0.0, 0.0], v0_val)
self.assertAllClose([0.0, 0.0], v1_val)
# Run a step of AdagradDA
update.run()
v0_val, v1_val = self.evaluate([var0, var1])
# Let g be the gradient accumulator, gg be the gradient squared
# accumulator, T be the global step, lr be the learning rate,
# and k the initial gradient squared accumulator value.
# w = \dfrac{sign(-g)*lr*|g - l1*T|_{+}}{l2*T*lr + \sqrt{k+gg})}
# For -0.1*3.0*(0.1 - 0)/(0 + sqrt(0.1 + 0.1*0.1)) = -0.904534
# similarly for others.
self.assertAllCloseAccordingToType(
np.array([-0.904534, -1.603567]), v0_val)
self.assertAllCloseAccordingToType(
np.array([-0.094821, -0.189358]), v1_val)
def testMinimizeSparseResourceVariable(self):
for dtype in [dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
global_step = resource_variable_ops.ResourceVariable(
0, dtype=dtypes.int64)
x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
loss = pred * pred
sgd_op = adagrad_da.AdagradDAOptimizer(
1.0, global_step).minimize(loss)
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval())
# Run 1 step of sgd
sgd_op.run()
# Validate updated params
self.assertAllCloseAccordingToType(
[[-1, -1]], var0.eval(), rtol=0.01)
def test_adagrad_da_apply_restriction(self):
gstep = training_util.create_global_step()
opt = adagrad_da.AdagradDAOptimizer(0.1, gstep)
self.commonly_apply_restriction_verify(opt)
def test_adagrad_da_restrict_on_policy(self):
gstep = training_util.create_global_step()
opt = adagrad_da.AdagradDAOptimizer(0.1, gstep)
self.common_single_step_restrict_verification(opt)
def test_adagradda_minimize_trainable(self):
base_gs = training_util.create_global_step()
base_opt = adagrad_da.AdagradDAOptimizer(1.0, base_gs)
test_opt = adagrad_da.AdagradDAOptimizer(1.0, base_gs)
self.common_minimize_trainable(base_opt, test_opt, name="adagrad_da")
