# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for Momentum.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import momentum as momentum_lib class MomentumOptimizerTest(test.TestCase): def _update_nesterov_momentum_numpy(self, var, accum, g, lr, momentum): var = var + accum * lr * momentum accum = accum * momentum + g var = var - lr * accum var = var - accum * lr * momentum return var, accum def doTestBasic(self, use_resource=False, use_callable_params=False): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): if use_resource: var0 = resource_variable_ops.ResourceVariable( [1.0, 2.0], dtype=dtype, name="var0_%d" % i) var1 = resource_variable_ops.ResourceVariable( [3.0, 4.0], dtype=dtype, name="var1_%d" % i) else: var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) learning_rate = lambda: 2.0 momentum = lambda: 0.9 if not use_callable_params: learning_rate = learning_rate() momentum = momentum() mom_opt = momentum_lib.MomentumOptimizer( learning_rate=learning_rate, momentum=momentum) mom_update = mom_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) if not context.executing_eagerly(): self.evaluate(variables.global_variables_initializer()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], self.evaluate(var0)) self.assertAllClose([3.0, 4.0], self.evaluate(var1)) # Check we have slots self.assertEqual(["momentum"], mom_opt.get_slot_names()) slot0 = mom_opt.get_slot(var0, "momentum") self.assertEquals(slot0.get_shape(), var0.get_shape()) slot1 = mom_opt.get_slot(var1, "momentum") self.assertEquals(slot1.get_shape(), var1.get_shape()) if not context.executing_eagerly(): self.assertFalse(slot0 in variables.trainable_variables()) self.assertFalse(slot1 in variables.trainable_variables()) # Step 1: the momentum accumulators where 0. So we should see a normal # update: v -= grad * learning_rate if not context.executing_eagerly(): self.evaluate(mom_update) # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType(np.array([0.1, 0.1]), self.evaluate(slot0)) self.assertAllCloseAccordingToType(np.array([0.01, 0.01]), self.evaluate(slot1)) # Check that the parameters have been updated. self.assertAllCloseAccordingToType( np.array([1.0 - (0.1 * 2.0), 2.0 - (0.1 * 2.0)]), self.evaluate(var0)) self.assertAllCloseAccordingToType( np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]), self.evaluate(var1)) # Step 2: the momentum accumulators contain the previous update. if context.executing_eagerly(): mom_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) else: self.evaluate(mom_update) # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType( np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]), self.evaluate(slot0)) self.assertAllCloseAccordingToType( np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]), self.evaluate(slot1)) # Check that the parameters have been updated. self.assertAllCloseAccordingToType( np.array([ 1.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0), 2.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0) ]), self.evaluate(var0)) self.assertAllCloseAccordingToType( np.array([ 2.98 - ((0.9 * 0.01 + 0.01) * 2.0), 3.98 - ( (0.9 * 0.01 + 0.01) * 2.0) ]), self.evaluate(var1)) def testBasic(self): with self.cached_session(): self.doTestBasic(use_resource=False) @test_util.run_in_graph_and_eager_modes(reset_test=True) def testResourceBasic(self): self.doTestBasic(use_resource=True) def testBasicCallableParams(self): with context.eager_mode(): self.doTestBasic(use_resource=True, use_callable_params=True) def testVariablesAcrossGraphs(self): optimizer = momentum_lib.MomentumOptimizer(0.01, 0.5) with ops.Graph().as_default(): var0 = resource_variable_ops.ResourceVariable( [1.0, 2.0], dtype=dtypes.float32, name="var0") var1 = resource_variable_ops.ResourceVariable( [3.0, 4.0], dtype=dtypes.float32, name="var1") loss = math_ops.reduce_sum(var0 + var1) optimizer.minimize(loss) optimizer_variables = optimizer.variables() self.assertStartsWith(optimizer_variables[0].name, "var0") self.assertStartsWith(optimizer_variables[1].name, "var1") self.assertEquals(2, len(optimizer_variables)) with ops.Graph().as_default(): var2 = resource_variable_ops.ResourceVariable( [1.0, 2.0], dtype=dtypes.float32, name="var2") var3 = resource_variable_ops.ResourceVariable( [3.0, 4.0], dtype=dtypes.float32, name="var3") loss = math_ops.reduce_sum(var2 + var3) optimizer.minimize(loss) optimizer_variables = optimizer.variables() self.assertStartsWith(optimizer_variables[0].name, "var2") self.assertStartsWith(optimizer_variables[1].name, "var3") self.assertEquals(2, len(optimizer_variables)) def testNesterovMomentum(self): for dtype in [dtypes.float32, dtypes.float64]: with self.cached_session(): var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([3.0, 4.0], dtype=dtype) var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) accum0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) accum1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) cost = 5 * var0 * var0 + 3 * var1 global_step = variables.Variable( array_ops.zeros([], dtypes.int64), name="global_step") mom_op = momentum_lib.MomentumOptimizer( learning_rate=2.0, momentum=0.9, use_nesterov=True) opt_op = mom_op.minimize(cost, global_step, [var0, var1]) variables.global_variables_initializer().run() for t in range(1, 5): opt_op.run() var0_np, accum0_np = self._update_nesterov_momentum_numpy( var0_np, accum0_np, var0_np * 10, 2.0, 0.9) var1_np, accum1_np = self._update_nesterov_momentum_numpy(var1_np, accum1_np, 3, 2.0, 0.9) self.assertAllClose(var0_np, var0.eval()) self.assertAllClose(var1_np, var1.eval()) def testSparseNesterovMomentum(self): for dtype in [dtypes.float32, dtypes.float64]: with self.cached_session(): var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) accum0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) accum1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) grads = [] for t in range(1, 5): grads.append(var0_np * 10) var0_np, accum0_np = self._update_nesterov_momentum_numpy( var0_np, accum0_np, var0_np * 10, 2.0, 0.9) var1_np, accum1_np = self._update_nesterov_momentum_numpy(var1_np, accum1_np, 3, 2.0, 0.9) var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) accum0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) accum1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) var0 = variables.Variable(var0_np) var1 = variables.Variable(var1_np) loss = 5 * var0 * var0 + 3 * var1 mom_op = momentum_lib.MomentumOptimizer( learning_rate=2.0, momentum=0.9, use_nesterov=True) x_feed = array_ops.placeholder(dtype) y_feed = ops.IndexedSlices( x_feed, constant_op.constant([0, 1]), constant_op.constant([2])) grads_and_vars = [(y_feed, var0), (constant_op.constant( [3.0, 3.0], dtype=dtype), var1)] opt_update = mom_op.apply_gradients(grads_and_vars) variables.global_variables_initializer().run() for t in range(1, 5): opt_update.run(feed_dict={x_feed: grads[t - 1]}) var0_np, accum0_np = self._update_nesterov_momentum_numpy( var0_np, accum0_np, var0_np * 10, 2.0, 0.9) var1_np, accum1_np = self._update_nesterov_momentum_numpy(var1_np, accum1_np, 3, 2.0, 0.9) self.assertAllClose(var0_np, var0.eval()) self.assertAllClose(var1_np, var1.eval()) @test_util.run_in_graph_and_eager_modes(reset_test=True) def testMinimizeSparseResourceVariable(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: # This test invokes the ResourceSparseApplyMomentum operation, which # did not have a registered GPU kernel as of April 2018. With graph # execution, the placement algorithm notices this and automatically # places the variable in CPU (host) memory. With eager execution, # the variable would be placed in GPU memory if available, which # would then conflict with the future invocation of the # ResourceSparseApplyMomentum operation. # To work around this discrepancy, for now we force the variable # to be placed on CPU. with ops.device("/cpu:0"): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) # pylint: disable=cell-var-from-loop def loss(): x = constant_op.constant([[4.0], [5.0]], dtype=dtype) pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) return pred * pred # pylint: enable=cell-var-from-loop opt = momentum_lib.MomentumOptimizer(learning_rate=1.0, momentum=0.0) sgd_op = opt.minimize(loss) self.evaluate(variables.global_variables_initializer()) # Run 1 step of sgd self.evaluate(sgd_op) # Validate updated params self.assertAllCloseAccordingToType([[-111, -138]], self.evaluate(var0)) @test_util.run_in_graph_and_eager_modes(reset_test=True) def testMinimizeWith2DIndiciesForEmbeddingLookup(self): # This test invokes the ResourceSparseApplyMomentum operation, which # did not have a registered GPU kernel as of April 2018. With graph # execution, the placement algorithm notices this and automatically # places the variable in CPU (host) memory. With eager execution, # the variable would be placed in GPU memory if available, which # would then conflict with the future invocation of the # ResourceSparseApplyMomentum operation. # To work around this discrepancy, for now we force the variable # to be placed on CPU. with ops.device("/cpu:0"): var0 = resource_variable_ops.ResourceVariable(array_ops.ones([2, 2])) def loss(): return math_ops.reduce_sum(embedding_ops.embedding_lookup(var0, [[1]])) opt = momentum_lib.MomentumOptimizer(learning_rate=1.0, momentum=0.0) sgd_op = opt.minimize(loss) self.evaluate(variables.global_variables_initializer()) self.evaluate(sgd_op) self.assertAllCloseAccordingToType([[1, 1], [0, 0]], self.evaluate(var0)) def testTensorLearningRateAndMomentum(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with self.cached_session(): var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) mom_opt = momentum_lib.MomentumOptimizer( learning_rate=constant_op.constant(2.0), momentum=constant_op.constant(0.9)) mom_update = mom_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() # Check we have slots self.assertEqual(["momentum"], mom_opt.get_slot_names()) slot0 = mom_opt.get_slot(var0, "momentum") self.assertEquals(slot0.get_shape(), var0.get_shape()) self.assertFalse(slot0 in variables.trainable_variables()) slot1 = mom_opt.get_slot(var1, "momentum") self.assertEquals(slot1.get_shape(), var1.get_shape()) self.assertFalse(slot1 in variables.trainable_variables()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], var0.eval()) self.assertAllClose([3.0, 4.0], var1.eval()) # Step 1: the momentum accumulators where 0. So we should see a normal # update: v -= grad * learning_rate mom_update.run() # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType(np.array([0.1, 0.1]), slot0.eval()) self.assertAllCloseAccordingToType(np.array([0.01, 0.01]), slot1.eval()) # Check that the parameters have been updated. self.assertAllCloseAccordingToType( np.array([1.0 - (0.1 * 2.0), 2.0 - (0.1 * 2.0)]), var0.eval()) self.assertAllCloseAccordingToType( np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]), var1.eval()) # Step 2: the momentum accumulators contain the previous update. mom_update.run() # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType( np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]), slot0.eval()) self.assertAllCloseAccordingToType( np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]), slot1.eval()) # Check that the parameters have been updated. self.assertAllCloseAccordingToType( np.array([ 1.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0), 2.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0) ]), var0.eval()) self.assertAllCloseAccordingToType( np.array([ 2.98 - ((0.9 * 0.01 + 0.01) * 2.0), 3.98 - ( (0.9 * 0.01 + 0.01) * 2.0) ]), var1.eval()) def _dbParamsMom01(self): """Return dist-belief momentum values. Return values been generated from the dist-belief momentum unittest, running with a learning rate of 0.1 and a momentum of 0.1. These values record how a parameter vector of size 10, initialized with 0.0, gets updated with 10 consecutive momentum steps. It uses random gradients. Returns: db_grad: The gradients to apply db_out: The parameters after the momentum update. """ db_grad = [[]] * 10 db_out = [[]] * 10 # pylint: disable=line-too-long db_grad[0] = [ 0.00096264342, 0.17914793, 0.93945462, 0.41396621, 0.53037018, 0.93197989, 0.78648776, 0.50036013, 0.55345792, 0.96722615 ] db_out[0] = [ -9.6264346e-05, -0.017914793, -0.093945466, -0.041396622, -0.053037018, -0.093197994, -0.078648776, -0.050036013, -0.055345792, -0.096722618 ] db_grad[1] = [ 0.17075552, 0.88821375, 0.20873757, 0.25236958, 0.57578111, 0.15312378, 0.5513742, 0.94687688, 0.16012503, 0.22159521 ] db_out[1] = [ -0.017181443, -0.10852765, -0.12421377, -0.070773244, -0.11591884, -0.11783017, -0.14165108, -0.14972731, -0.076892875, -0.1285544 ] db_grad[2] = [ 0.35077485, 0.47304362, 0.44412705, 0.44368884, 0.078527533, 0.81223965, 0.31168157, 0.43203235, 0.16792089, 0.24644311 ] db_out[2] = [ -0.053967446, -0.1648933, -0.1716533, -0.1180798, -0.13005978, -0.20151734, -0.17911947, -0.20289968, -0.095839672, -0.15638189 ] db_grad[3] = [ 0.9694621, 0.75035888, 0.28171822, 0.83813518, 0.53807181, 0.3728098, 0.81454384, 0.03848977, 0.89759839, 0.93665648 ] db_out[3] = [ -0.15459226, -0.24556576, -0.20456907, -0.20662397, -0.18528105, -0.24716705, -0.2643207, -0.21206589, -0.18749419, -0.2528303 ] db_grad[4] = [ 0.38578293, 0.8536852, 0.88722926, 0.66276771, 0.13678469, 0.94036359, 0.69107032, 0.81897682, 0.5433259, 0.67860287 ] db_out[4] = [ -0.20323303, -0.33900154, -0.29658359, -0.28175515, -0.20448165, -0.34576839, -0.34194785, -0.29488021, -0.25099224, -0.33033544 ] db_grad[5] = [ 0.27885768, 0.76100707, 0.24625534, 0.81354135, 0.18959245, 0.48038563, 0.84163809, 0.41172323, 0.83259648, 0.44941229 ] db_out[5] = [ -0.23598288, -0.42444581, -0.33041057, -0.3706224, -0.22536094, -0.40366709, -0.43387437, -0.34433398, -0.34060168, -0.38302717 ] db_grad[6] = [ 0.27233034, 0.056316052, 0.5039115, 0.24105175, 0.35697976, 0.75913221, 0.73577434, 0.16014607, 0.57500273, 0.071136251 ] db_out[6] = [ -0.26649091, -0.43862185, -0.38418442, -0.40361428, -0.26314685, -0.48537019, -0.51664448, -0.36529395, -0.40706289, -0.39540997 ] db_grad[7] = [ 0.58697265, 0.2494842, 0.08106143, 0.39954534, 0.15892942, 0.12683646, 0.74053431, 0.16033, 0.66625422, 0.73515922 ] db_out[7] = [ -0.32823896, -0.46498787, -0.39766794, -0.446868, -0.28281838, -0.50622416, -0.59897494, -0.38342294, -0.48033443, -0.47016418 ] db_grad[8] = [ 0.8215279, 0.41994119, 0.95172721, 0.68000203, 0.79439718, 0.43384039, 0.55561525, 0.22567581, 0.93331909, 0.29438227 ] db_out[8] = [ -0.41656655, -0.50961858, -0.49418902, -0.51919359, -0.36422527, -0.55169362, -0.6627695, -0.40780342, -0.58099347, -0.50707781 ] db_grad[9] = [ 0.68297005, 0.67758518, 0.1748755, 0.13266537, 0.70697063, 0.055731893, 0.68593478, 0.50580865, 0.12602448, 0.093537711 ] db_out[9] = [ -0.49369633, -0.58184016, -0.52132869, -0.5396927, -0.44306302, -0.56181377, -0.73774242, -0.46082234, -0.60366184, -0.52012295 ] # pylint: enable=line-too-long return db_grad, db_out def testLikeDistBeliefMom01(self): with self.cached_session(): db_grad, db_out = self._dbParamsMom01() num_samples = len(db_grad) var0 = variables.Variable([0.0] * num_samples) grads0 = constant_op.constant([0.0] * num_samples) mom_opt = momentum_lib.MomentumOptimizer(learning_rate=0.1, momentum=0.1) mom_update = mom_opt.apply_gradients(zip([grads0], [var0])) variables.global_variables_initializer().run() for i in xrange(num_samples): mom_update.run(feed_dict={grads0: db_grad[i]}) self.assertAllClose(np.array(db_out[i]), var0.eval()) def testSparse(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with self.cached_session(): var0 = variables.Variable(array_ops.zeros([4, 2], dtype=dtype)) var1 = variables.Variable(constant_op.constant(1.0, dtype, [4, 2])) grads0 = ops.IndexedSlices( constant_op.constant( [[.1, .1]], dtype=dtype), constant_op.constant([1]), constant_op.constant([4, 2])) grads1 = ops.IndexedSlices( constant_op.constant( [[.01, .01], [.01, .01]], dtype=dtype), constant_op.constant([2, 3]), constant_op.constant([4, 2])) mom_opt = momentum_lib.MomentumOptimizer( learning_rate=2.0, momentum=0.9) mom_update = mom_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() # Check we have slots self.assertEqual(["momentum"], mom_opt.get_slot_names()) slot0 = mom_opt.get_slot(var0, "momentum") self.assertEquals(slot0.get_shape(), var0.get_shape()) slot1 = mom_opt.get_slot(var1, "momentum") self.assertEquals(slot1.get_shape(), var1.get_shape()) # Fetch params to validate initial values self.assertAllClose([0, 0], var0.eval()[0]) self.assertAllClose([0, 0], var0.eval()[1]) self.assertAllClose([1, 1], var1.eval()[2]) # Step 1: the momentum accumulators are 0. So we should see a normal # update: v -= grad * learning_rate mom_update.run() # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType(np.array([0, 0]), slot0.eval()[0]) self.assertAllCloseAccordingToType(np.array([.1, .1]), slot0.eval()[1]) self.assertAllCloseAccordingToType( np.array([.01, .01]), slot1.eval()[2]) # Check that the parameters have been updated. self.assertAllCloseAccordingToType(np.array([0, 0]), var0.eval()[0]) self.assertAllCloseAccordingToType( np.array([-(0.1 * 2.0), -(0.1 * 2.0)]), var0.eval()[1]) self.assertAllCloseAccordingToType( np.array([1.0 - (0.01 * 2.0), 1.0 - (0.01 * 2.0)]), var1.eval()[2]) # Step 2: the momentum accumulators contain the previous update. mom_update.run() # Check that the momentum accumulators have been updated. self.assertAllClose(np.array([0, 0]), slot0.eval()[0]) self.assertAllCloseAccordingToType( np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]), slot0.eval()[1]) self.assertAllCloseAccordingToType( np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]), slot1.eval()[2]) # Check that the parameters have been updated. self.assertAllClose(np.array([0, 0]), var0.eval()[0]) self.assertAllCloseAccordingToType( np.array([ -(0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0), -(0.1 * 2.0) - ( (0.9 * 0.1 + 0.1) * 2.0) ]), var0.eval()[1]) self.assertAllCloseAccordingToType( np.array([ 0.98 - ((0.9 * 0.01 + 0.01) * 2.0), 0.98 - ( (0.9 * 0.01 + 0.01) * 2.0) ]), var1.eval()[2]) def testSharing(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: with self.cached_session(): var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) mom_opt = momentum_lib.MomentumOptimizer( learning_rate=2.0, momentum=0.9) mom_update1 = mom_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) mom_update2 = mom_opt.apply_gradients( zip([grads0, grads1], [var0, var1])) variables.global_variables_initializer().run() self.assertEqual(["momentum"], mom_opt.get_slot_names()) slot0 = mom_opt.get_slot(var0, "momentum") self.assertEquals(slot0.get_shape(), var0.get_shape()) slot1 = mom_opt.get_slot(var1, "momentum") self.assertEquals(slot1.get_shape(), var1.get_shape()) # Fetch params to validate initial values self.assertAllClose([1.0, 2.0], var0.eval()) self.assertAllClose([3.0, 4.0], var1.eval()) # Step 1: the momentum accumulators where 0. So we should see a normal # update: v -= grad * learning_rate mom_update1.run() # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType(np.array([0.1, 0.1]), slot0.eval()) self.assertAllCloseAccordingToType(np.array([0.01, 0.01]), slot1.eval()) # Check that the parameters have been updated. self.assertAllCloseAccordingToType( np.array([1.0 - (0.1 * 2.0), 2.0 - (0.1 * 2.0)]), var0.eval()) self.assertAllCloseAccordingToType( np.array([3.0 - (0.01 * 2.0), 4.0 - (0.01 * 2.0)]), var1.eval()) # Step 2: the second momentum accumulators contain the previous update. mom_update2.run() # Check that the momentum accumulators have been updated. self.assertAllCloseAccordingToType( np.array([(0.9 * 0.1 + 0.1), (0.9 * 0.1 + 0.1)]), slot0.eval()) self.assertAllCloseAccordingToType( np.array([(0.9 * 0.01 + 0.01), (0.9 * 0.01 + 0.01)]), slot1.eval()) # Check that the parameters have been updated. self.assertAllCloseAccordingToType( np.array([ 1.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0), 2.0 - (0.1 * 2.0) - ((0.9 * 0.1 + 0.1) * 2.0) ]), var0.eval()) self.assertAllCloseAccordingToType( np.array([ 2.98 - ((0.9 * 0.01 + 0.01) * 2.0), 3.98 - ( (0.9 * 0.01 + 0.01) * 2.0) ]), var1.eval()) if __name__ == "__main__": test.main()