def testConstructNAdamWithLR(self): opt = nadam.Nadam(lr=1.0) opt_2 = nadam.Nadam(learning_rate=0.1, lr=1.0) opt_3 = nadam.Nadam(learning_rate=0.1) self.assertIsInstance(opt.lr, tf.Variable) self.assertIsInstance(opt_2.lr, tf.Variable) self.assertIsInstance(opt_3.lr, tf.Variable) self.evaluate(tf.compat.v1.global_variables_initializer()) self.assertAllClose(self.evaluate(opt.lr), (1.0)) self.assertAllClose(self.evaluate(opt_2.lr), (1.0)) self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
def testSparse(self): # TODO(tanzheny, omalleyt): Fix test in eager mode. sparse_epsilon = 1e-7 for dtype in [tf.half, tf.float32, tf.float64]: with tf.Graph().as_default(), 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, 1.0, 2.0], dtype=dtype.as_numpy_dtype) grads0_np = np.array([0.1, 0, 0.1], dtype=dtype.as_numpy_dtype) var1_np = np.array([3.0, 3.0, 4.0], dtype=dtype.as_numpy_dtype) grads1_np = np.array([0.01, 0, 0.01], dtype=dtype.as_numpy_dtype) var0 = tf.Variable(var0_np) var1 = tf.Variable(var1_np) grads0_np_indices = np.array([0, 2], dtype=np.int32) grads0 = tf.IndexedSlices( tf.constant(grads0_np[grads0_np_indices]), tf.constant(grads0_np_indices), tf.constant([3])) grads1_np_indices = np.array([0, 2], dtype=np.int32) grads1 = tf.IndexedSlices( tf.constant(grads1_np[grads1_np_indices]), tf.constant(grads1_np_indices), tf.constant([3])) opt = nadam.Nadam(epsilon=sparse_epsilon) update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) self.evaluate(tf.compat.v1.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 1.0, 2.0], var0) self.assertAllClose([3.0, 3.0, 4.0], var1) 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) self.assertAllCloseAccordingToType(0.999**(t + 1), beta2_power) update.run() mcache = update_m_cache(mcache, t) var0_np, m0, v0 = nadam_update_numpy( var0_np, grads0_np, t, m0, v0, mcache, epsilon=sparse_epsilon) var1_np, m1, v1 = nadam_update_numpy( var1_np, grads1_np, t, m1, v1, mcache, epsilon=sparse_epsilon) # Validate updated params self.assertAllCloseAccordingToType(var0_np, var0) self.assertAllCloseAccordingToType(var1_np, var1)
def testBasic(self): # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [tf.half, tf.float32, tf.float64]: with tf.Graph().as_default(), 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 = tf.Variable(var0_np) var1 = tf.Variable(var1_np) grads0 = tf.constant(grads0_np) grads1 = tf.constant(grads1_np) opt = nadam.Nadam() update = opt.apply_gradients( zip([grads0, grads1], [var0, var1])) self.evaluate(tf.compat.v1.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], var0) self.assertAllClose([3.0, 4.0], var1) # 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) self.assertAllCloseAccordingToType(var1_np, var1)
gradient_descent_optimizer_v1_fn, adagrad_optimizer_v1_fn, ftrl_optimizer_v1_fn, rmsprop_optimizer_v1_fn ] adadelta_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001)) adagrad_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "AdagradKerasV2", lambda: adagrad_keras_v2.Adagrad(0.001)) adam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "AdamKerasV2", lambda: adam_keras_v2.Adam(0.001, epsilon=1.0)) adam_experimental_fn = tf.__internal__.test.combinations.NamedObject( "AdamExperimental", lambda: adam_experimental.Adam(0.001)) adamax_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "AdamaxKerasV2", lambda: adamax_keras_v2.Adamax(0.001, epsilon=1.0)) nadam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "NadamKerasV2", lambda: nadam_keras_v2.Nadam(0.001, epsilon=1.0)) ftrl_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "FtrlKerasV2", lambda: ftrl_keras_v2.Ftrl(0.001)) gradient_descent_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "GradientDescentKerasV2", lambda: gradient_descent_keras_v2.SGD(0.001)) rmsprop_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject( "RmsPropKerasV2", lambda: rmsprop_keras_v2.RMSprop(0.001)) # TODO(shiningsun): consider adding the other v2 optimizers optimizers_v2 = [ gradient_descent_optimizer_keras_v2_fn, adagrad_optimizer_keras_v2_fn ] optimizers_v1_and_v2 = optimizers_v1 + optimizers_v2
def testConstructNAdamWithScheduleDecay(self): opt = nadam.Nadam(schedule_decay=0.2) self.assertIsInstance(opt.decay, tf.Variable) self.evaluate(tf.compat.v1.global_variables_initializer()) self.assertAllClose(self.evaluate(opt.decay), (0.2))
def testNadamCompatibility(self): opt_v1 = optimizer_v1.Nadam(lr=0.001) opt_v2 = nadam.Nadam(learning_rate=0.001) self._testOptimizersCompatibility(opt_v1, opt_v2)