def testFtrlWithL1_L2(self): for dtype in [dtypes.half, dtypes.float32]: with self.cached_session() as sess: 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 = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.001, l2_regularization_strength=2.0) update = opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() 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 10 steps FTRL for _ in range(10): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType( np.array([-0.24059935, -0.46829352]), v0_val) self.assertAllCloseAccordingToType( np.array([-0.02406147, -0.04830509]), v1_val)
def testFtrlWithL1_L2_L2ShrinkageSparse(self): """Tests the new FTRL op with support for l2 shrinkage on sparse grads.""" for dtype in [dtypes.half, dtypes.float32]: with self.cached_session() as sess: var0 = variables.Variable([[1.0], [2.0]], dtype=dtype) var1 = variables.Variable([[4.0], [3.0]], dtype=dtype) grads0 = ops.IndexedSlices( constant_op.constant([0.1], shape=[1, 1], dtype=dtype), constant_op.constant([0]), constant_op.constant([2, 1])) grads1 = ops.IndexedSlices( constant_op.constant([0.02], shape=[1, 1], dtype=dtype), constant_op.constant([1]), constant_op.constant([2, 1])) opt = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.001, l2_regularization_strength=2.0, l2_shrinkage_regularization_strength=0.1) update = opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() 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 10 steps FTRL for _ in range(10): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType([[-0.22578995], [2.]], v0_val) self.assertAllCloseAccordingToType([[4.], [-0.13229476]], v1_val)
def testFtrlwithoutRegularization2(self): for dtype in [dtypes.half, dtypes.float32]: with self.cached_session() as sess: 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 = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0) update = opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() 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 3 steps FTRL for _ in range(3): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType( np.array([-2.55607247, -3.98729396]), v0_val) self.assertAllCloseAccordingToType( np.array([-0.28232238, -0.56096673]), v1_val)
def testFtrlWithL2_Beta(self): # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [dtypes.half, dtypes.float32]: with ops.Graph().as_default(), self.cached_session(): 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 = ftrl.Ftrl( 3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.1, beta=0.1) update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) 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 10 steps FTRL for _ in range(10): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType( np.array([-2.735487, -4.704625]), v0_val) self.assertAllCloseAccordingToType( np.array([-0.294335, -0.586556]), v1_val)
def testFtrlWithL1(self): # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [dtypes.half, dtypes.float32]: with ops.Graph().as_default(), self.cached_session(use_gpu=True): 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 = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.001, l2_regularization_strength=0.0) update = opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() 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 10 steps FTRL for _ in range(10): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType( np.array([-7.66718769, -10.91273689]), v0_val) self.assertAllCloseAccordingToType( np.array([-0.93460727, -1.86147261]), v1_val)
def testFtrlWithBeta(self): # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [dtypes.half, dtypes.float32]: with ops.Graph().as_default(), self.cached_session(use_gpu=True): 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 = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, beta=0.1) update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) 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 10 steps FTRL for _ in range(10): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType( np.array([-6.096838, -9.162214]), v0_val) self.assertAllCloseAccordingToType( np.array([-0.717741, -1.425132]), v1_val)
def doTestFtrlwithoutRegularization(self, use_resource=False): for dtype in [dtypes.half, dtypes.float32]: with self.cached_session() as sess: 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 = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0) update = opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() 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 3 steps FTRL for _ in range(3): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType( np.array([-2.60260963, -4.29698515]), v0_val) self.assertAllCloseAccordingToType( np.array([-0.28432083, -0.56694895]), v1_val)
def testFtrlWithL2ShrinkageDoesNotChangeLrSchedule(self): """Verifies that l2 shrinkage in FTRL does not change lr schedule.""" # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [dtypes.half, dtypes.float32]: with ops.Graph().as_default(), self.cached_session() as sess: var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([1.0, 2.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) grads1 = constant_op.constant([0.1, 0.2], dtype=dtype) opt0 = ftrl.Ftrl( 3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.001, l2_regularization_strength=2.0, l2_shrinkage_regularization_strength=0.1) opt1 = ftrl.Ftrl( 3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.001, l2_regularization_strength=2.0) update0 = opt0.apply_gradients([(grads0, var0)]) update1 = opt1.apply_gradients([(grads1, var1)]) self.evaluate(variables.global_variables_initializer()) v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType([1.0, 2.0], v0_val) self.assertAllCloseAccordingToType([1.0, 2.0], v1_val) # Run 10 steps FTRL for _ in range(10): update0.run() update1.run() v0_val, v1_val = self.evaluate([var0, var1]) # var0 is experiencing L2 shrinkage so it should be smaller than var1 # in magnitude. self.assertTrue((v0_val**2 < v1_val**2).all()) accum0 = sess.run(opt0.get_slot(var0, "accumulator")) accum1 = sess.run(opt1.get_slot(var1, "accumulator")) # L2 shrinkage should not change how we update grad accumulator. self.assertAllCloseAccordingToType(accum0, accum1)
def test_numpy_input_fn_with_optimizer_instance(self): """Tests complete flow with optimizer_v2 instance.""" label_dimension = 2 batch_size = 10 train_input_fn, eval_input_fn, predict_input_fn = self._create_input_fn( label_dimension, batch_size) self._test_complete_flow( train_input_fn=train_input_fn, eval_input_fn=eval_input_fn, predict_input_fn=predict_input_fn, input_dimension=label_dimension, label_dimension=label_dimension, batch_size=batch_size, optimizer=ftrl_v2.Ftrl(0.01)) # Test with optimizer_v2 instance
def testMinimizeSparseResourceVariable(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with self.cached_session(): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) 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 = ftrl.Ftrl(1.0).minimize(loss, var_list=[var0]) variables.global_variables_initializer().run() # Fetch params to validate initial values self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0)) # Run 1 step of sgd sgd_op.run() # Validate updated params self.assertAllCloseAccordingToType([[0, 1]], self.evaluate(var0), atol=0.01)
def testEquivGradientDescentwithoutRegularization(self): for dtype in [dtypes.half, dtypes.float32]: with self.cached_session(): val0, val1 = self.applyOptimizer( ftrl.Ftrl( 3.0, # Fixed learning rate learning_rate_power=-0.0, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0), dtype) with self.cached_session(): val2, val3 = self.applyOptimizer( gradient_descent.GradientDescentOptimizer(3.0), dtype) self.assertAllCloseAccordingToType(val0, val2) self.assertAllCloseAccordingToType(val1, val3)
def testEquivGradientDescentwithoutRegularization(self): # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [dtypes.half, dtypes.float32]: with ops.Graph().as_default(), self.cached_session(use_gpu=True): val0, val1 = self.applyOptimizer( ftrl.Ftrl( 3.0, # Fixed learning rate learning_rate_power=-0.0, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0), dtype) with ops.Graph().as_default(), self.cached_session(use_gpu=True): val2, val3 = self.applyOptimizer( gradient_descent.GradientDescentOptimizer(3.0), dtype) self.assertAllCloseAccordingToType(val0, val2) self.assertAllCloseAccordingToType(val1, val3)
def testMinimizeSparseResourceVariable(self): # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with ops.Graph().as_default(), self.cached_session(): var0 = variables.Variable([[1.0, 2.0]], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) def loss(): pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop return pred * pred sgd_op = ftrl.Ftrl(1.0).minimize(loss, var_list=[var0]) self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0)) # Run 1 step of sgd sgd_op.run() # Validate updated params self.assertAllCloseAccordingToType([[0, 1]], self.evaluate(var0), atol=0.01)
def testEquivAdagradwithoutRegularization(self): for dtype in [dtypes.half, dtypes.float32]: with self.cached_session(use_gpu=True): val0, val1 = self.applyOptimizer( ftrl.Ftrl( 3.0, # Adagrad learning rate learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0), dtype) with self.cached_session(use_gpu=True): val2, val3 = self.applyOptimizer( adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1), dtype) self.assertAllCloseAccordingToType(val0, val2) self.assertAllCloseAccordingToType(val1, val3)
def testFtrlWithL1_L2_L2Shrinkage(self): """Test the new FTRL op with support for l2 shrinkage. The addition of this parameter which places a constant pressure on weights towards the origin causes the gradient descent trajectory to differ. The weights will tend to have smaller magnitudes with this parameter set. """ # TODO(tanzheny, omalleyt): Fix test in eager mode. for dtype in [dtypes.half, dtypes.float32]: with ops.Graph().as_default(), self.cached_session(use_gpu=True): 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 = ftrl.Ftrl(3.0, initial_accumulator_value=0.1, l1_regularization_strength=0.001, l2_regularization_strength=2.0, l2_shrinkage_regularization_strength=0.1) update = opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() 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 10 steps FTRL for _ in range(10): update.run() v0_val, v1_val = self.evaluate([var0, var1]) self.assertAllCloseAccordingToType( np.array([-0.22578995, -0.44345796]), v0_val) self.assertAllCloseAccordingToType( np.array([-0.14378493, -0.13229476]), v1_val)
# TODO(shiningsun): consider adding the other v1 optimizers optimizers_v1 = [gradient_descent_optimizer_v1_fn, adagrad_optimizer_v1_fn] adadelta_optimizer_keras_v2_fn = combinations.NamedObject( "AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001)) adagrad_optimizer_keras_v2_fn = combinations.NamedObject( "AdagradKerasV2", lambda: adagrad_keras_v2.Adagrad(0.001)) adam_optimizer_keras_v2_fn = combinations.NamedObject( "AdamKerasV2", lambda: adam_keras_v2.Adam(0.001, epsilon=1.0)) adamax_optimizer_keras_v2_fn = combinations.NamedObject( "AdamaxKerasV2", lambda: adamax_keras_v2.Adamax(0.001, epsilon=1.0)) nadam_optimizer_keras_v2_fn = combinations.NamedObject( "NadamKerasV2", lambda: nadam_keras_v2.Nadam(0.001, epsilon=1.0)) ftrl_optimizer_keras_v2_fn = combinations.NamedObject( "FtrlKerasV2", lambda: ftrl_keras_v2.Ftrl(0.001)) gradient_descent_optimizer_keras_v2_fn = combinations.NamedObject( "GradientDescentKerasV2", lambda: gradient_descent_keras_v2.SGD(0.2)) rmsprop_optimizer_keras_v2_fn = 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 graph_and_eager_modes = ["graph", "eager"]
HP_IN_GRAPH = { 'Adam': ['decay', 'learning_rate'], 'Ftrl': [ 'decay', 'l1_regularization_strength', 'l2_regularization_strength', 'learning_rate', 'learning_rate_power' ], 'RMSProp': ['decay', 'learning_rate', 'momentum', 'rho'], 'Adagrad': ['decay', 'learning_rate'], 'SGD': ['decay', 'learning_rate', 'momentum'], } # optimizer v2 instance. OPT_V2_INSTANCE = { 'Adagrad': adagrad.Adagrad(), 'Adam': adam.Adam(), 'Ftrl': ftrl.Ftrl(), 'RMSProp': rmsprop.RMSprop(), 'SGD': gradient_descent.SGD(), } def _add_new_variable(initial_value, var_name_v2, var_name_v1, var_map, var_names_map): """Creates a new variable and add it to the variable maps.""" var = tf.Variable(initial_value, name=var_name_v2) var_map[var_name_v2] = var var_names_map[var_name_v2] = var_name_v1 def _add_opt_variable(opt_name_v2, var_name_v1, idx, suffix_v2, reader, var_map, var_names_map):