def testConstructNAdamWithLR(self): opt = nadam.Nadam(lr=1.0) self.assertEqual(opt.lr, 1.0) opt_2 = nadam.Nadam(learning_rate=0.1, lr=1.0) self.assertEqual(opt_2.lr, 1.0) opt_3 = nadam.Nadam(learning_rate=0.1) self.assertEqual(opt_3.lr, 0.1)
def testConstructNAdamWithEpsilonValues(self):
opt = nadam.Nadam(epsilon=None)
config = opt.get_config()
self.assertEqual(config["epsilon"], 1e-7)
opt = nadam.Nadam(epsilon=1e-8)
config = opt.get_config()
self.assertEqual(config["epsilon"], 1e-8)
def testBasic(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1, mcache = 0.0, 0.0, 0.0, 0.0, 1.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
opt = nadam.Nadam()
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 3 steps of Nadam
for t in range(3):
update.run()
mcache = update_m_cache(mcache, t)
var0_np, m0, v0 = nadam_update_numpy(var0_np, grads0_np, t, m0, v0,
mcache)
var1_np, m1, v1 = nadam_update_numpy(var1_np, grads1_np, t, m1, v1,
mcache)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, var0.eval())
self.assertAllCloseAccordingToType(var1_np, var1.eval())
def testBasicWithLearningRateDecay(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
learning_rate = 0.001
decay = 0.5
opt = nadam.Nadam(learning_rate=learning_rate, decay=decay)
update = opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
beta1_power, beta2_power = get_beta_accumulators(opt, dtype)
# Run 3 steps of Nadam
for t in range(3):
self.assertAllCloseAccordingToType(0.9**(t + 1),
beta1_power.eval())
self.assertAllCloseAccordingToType(0.999**(t + 1),
beta2_power.eval())
update.run()
lr = learning_rate / (1 + decay * t)
var0_np, m0, v0 = nadam_update_numpy(var0_np,
grads0_np,
t,
m0,
v0,
alpha=lr)
var1_np, m1, v1 = nadam_update_numpy(var1_np,
grads1_np,
t,
m1,
v1,
alpha=lr)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, var0.eval())
self.assertAllCloseAccordingToType(var1_np, var1.eval())
