| # 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 rmsprop.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import copy |
| import itertools |
| import math |
| |
| import numpy as np |
| |
| 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 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 rmsprop |
| |
| _DATA_TYPES = [dtypes.half, dtypes.float32] |
| |
| _TEST_PARAM_VALUES = [ |
| # learning_rate, decay, momentum, epsilon, centered, use_resource |
| [0.5, 0.9, 0.0, 1e-3, True, False], |
| [0.5, 0.9, 0.0, 1e-3, False, False], |
| [0.5, 0.9, 0.0, 1e-3, True, True], |
| [0.5, 0.9, 0.0, 1e-3, False, True], |
| [0.1, 0.9, 0.0, 1e-3, True, False], |
| [0.5, 0.95, 0.0, 1e-3, False, False], |
| [0.5, 0.95, 0.0, 1e-5, True, False], |
| [0.5, 0.95, 0.9, 1e-5, True, False], |
| ] |
| |
| _TESTPARAMS = [ |
| [data_type] + values |
| for data_type, values in itertools.product(_DATA_TYPES, _TEST_PARAM_VALUES) |
| ] |
| |
| |
| class RMSPropOptimizerTest(test.TestCase): |
| |
| def _rmsprop_update_numpy(self, var, g, mg, rms, mom, lr, decay, momentum, |
| epsilon, centered): |
| rms_t = rms * decay + (1 - decay) * g * g |
| denom_t = rms_t + epsilon |
| if centered: |
| mg_t = mg * decay + (1 - decay) * g |
| denom_t -= mg_t * mg_t |
| else: |
| mg_t = mg |
| mom_t = momentum * mom + lr * g / np.sqrt(denom_t, dtype=denom_t.dtype) |
| var_t = var - mom_t |
| return var_t, mg_t, rms_t, mom_t |
| |
| def _sparse_rmsprop_update_numpy(self, var, gindexs, gvalues, mg, rms, mom, |
| lr, decay, momentum, epsilon, centered): |
| mg_t = copy.deepcopy(mg) |
| rms_t = copy.deepcopy(rms) |
| mom_t = copy.deepcopy(mom) |
| var_t = copy.deepcopy(var) |
| for i in range(len(gindexs)): |
| gindex = gindexs[i] |
| gvalue = gvalues[i] |
| rms_t[gindex] = rms[gindex] * decay + (1 - decay) * gvalue * gvalue |
| denom_t = rms_t[gindex] + epsilon |
| if centered: |
| mg_t[gindex] = mg_t[gindex] * decay + (1 - decay) * gvalue |
| denom_t -= mg_t[gindex] * mg_t[gindex] |
| mom_t[gindex] = momentum * mom[gindex] + lr * gvalue / np.sqrt(denom_t) |
| var_t[gindex] = var[gindex] - mom_t[gindex] |
| return var_t, mg_t, rms_t, mom_t |
| |
| @test_util.run_deprecated_v1 |
| def testDense(self): |
| # TODO(yori): Use ParameterizedTest when available |
| for (dtype, learning_rate, decay, momentum, |
| epsilon, centered, use_resource) in _TESTPARAMS: |
| with test_util.use_gpu(): |
| # Initialize variables for numpy implementation. |
| var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype) |
| var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype) |
| |
| if use_resource: |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| else: |
| var0 = variables.Variable(var0_np) |
| var1 = variables.Variable(var1_np) |
| grads0 = constant_op.constant(grads0_np) |
| grads1 = constant_op.constant(grads1_np) |
| opt = rmsprop.RMSPropOptimizer( |
| learning_rate=learning_rate, |
| decay=decay, |
| momentum=momentum, |
| epsilon=epsilon, |
| centered=centered) |
| |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| mg0 = opt.get_slot(var0, "mg") |
| self.assertEqual(mg0 is not None, centered) |
| mg1 = opt.get_slot(var1, "mg") |
| self.assertEqual(mg1 is not None, centered) |
| rms0 = opt.get_slot(var0, "rms") |
| self.assertTrue(rms0 is not None) |
| rms1 = opt.get_slot(var1, "rms") |
| self.assertTrue(rms1 is not None) |
| mom0 = opt.get_slot(var0, "momentum") |
| self.assertTrue(mom0 is not None) |
| mom1 = opt.get_slot(var1, "momentum") |
| self.assertTrue(mom1 is not None) |
| |
| mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) |
| rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) |
| mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([3.0, 4.0], self.evaluate(var1)) |
| |
| # Run 4 steps of RMSProp |
| for _ in range(1, 5): |
| self.evaluate(update) |
| |
| var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy( |
| var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate, |
| decay, momentum, epsilon, centered) |
| var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy( |
| var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate, |
| decay, momentum, epsilon, centered) |
| |
| # Validate updated params |
| if centered: |
| self.assertAllCloseAccordingToType(mg0_np, self.evaluate(mg0)) |
| self.assertAllCloseAccordingToType(mg1_np, self.evaluate(mg1)) |
| self.assertAllCloseAccordingToType(rms0_np, self.evaluate(rms0)) |
| self.assertAllCloseAccordingToType(rms1_np, self.evaluate(rms1)) |
| self.assertAllCloseAccordingToType(mom0_np, self.evaluate(mom0)) |
| self.assertAllCloseAccordingToType(mom1_np, self.evaluate(mom1)) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| @test_util.run_deprecated_v1 |
| 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) |
| 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 = rmsprop.RMSPropOptimizer( |
| learning_rate=1.0, |
| decay=0.0, |
| momentum=0.0, |
| epsilon=0.0, |
| centered=False).minimize(loss) |
| 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 |
| self.evaluate(sgd_op) |
| # Validate updated params |
| self.assertAllCloseAccordingToType([[0., 1.]], |
| self.evaluate(var0), |
| atol=0.01) |
| |
| @test_util.run_deprecated_v1 |
| def testMinimizeSparseResourceVariableCentered(self): |
| for dtype in [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 = rmsprop.RMSPropOptimizer( |
| learning_rate=1.0, |
| decay=0.0, |
| momentum=0.0, |
| epsilon=1.0, |
| centered=True).minimize(loss) |
| 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 |
| self.evaluate(sgd_op) |
| # Validate updated params |
| self.assertAllCloseAccordingToType([[-111, -138]], |
| self.evaluate(var0), |
| atol=0.01) |
| |
| @test_util.run_deprecated_v1 |
| def testSparse(self): |
| # TODO(yori): Use ParameterizedTest when available |
| for (dtype, learning_rate, decay, |
| momentum, epsilon, centered, _) in _TESTPARAMS: |
| with test_util.use_gpu(): |
| # Initialize variables for numpy implementation. |
| var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([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], dtype=dtype.as_numpy_dtype) |
| |
| var0 = variables.Variable(var0_np) |
| var1 = variables.Variable(var1_np) |
| grads0_np_indices = np.array([0], dtype=np.int32) |
| grads0 = ops.IndexedSlices( |
| constant_op.constant(grads0_np), |
| constant_op.constant(grads0_np_indices), constant_op.constant([1])) |
| grads1_np_indices = np.array([1], dtype=np.int32) |
| grads1 = ops.IndexedSlices( |
| constant_op.constant(grads1_np), |
| constant_op.constant(grads1_np_indices), constant_op.constant([1])) |
| opt = rmsprop.RMSPropOptimizer( |
| learning_rate=learning_rate, |
| decay=decay, |
| momentum=momentum, |
| epsilon=epsilon, |
| centered=centered) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| mg0 = opt.get_slot(var0, "mg") |
| self.assertEqual(mg0 is not None, centered) |
| mg1 = opt.get_slot(var1, "mg") |
| self.assertEqual(mg1 is not None, centered) |
| rms0 = opt.get_slot(var0, "rms") |
| self.assertTrue(rms0 is not None) |
| rms1 = opt.get_slot(var1, "rms") |
| self.assertTrue(rms1 is not None) |
| mom0 = opt.get_slot(var0, "momentum") |
| self.assertTrue(mom0 is not None) |
| mom1 = opt.get_slot(var1, "momentum") |
| self.assertTrue(mom1 is not None) |
| |
| mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) |
| rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) |
| mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) |
| |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([3.0, 4.0], self.evaluate(var1)) |
| |
| # Run 4 steps of RMSProp |
| for _ in range(1, 5): |
| self.evaluate(update) |
| |
| var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy( |
| var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np, |
| learning_rate, decay, momentum, epsilon, centered) |
| var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy( |
| var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np, |
| learning_rate, decay, momentum, epsilon, centered) |
| |
| # Validate updated params |
| if centered: |
| self.assertAllCloseAccordingToType(mg0_np, self.evaluate(mg0)) |
| self.assertAllCloseAccordingToType(mg1_np, self.evaluate(mg1)) |
| self.assertAllCloseAccordingToType(rms0_np, self.evaluate(rms0)) |
| self.assertAllCloseAccordingToType(rms1_np, self.evaluate(rms1)) |
| self.assertAllCloseAccordingToType(mom0_np, self.evaluate(mom0)) |
| self.assertAllCloseAccordingToType(mom1_np, self.evaluate(mom1)) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| @test_util.run_deprecated_v1 |
| def testWithoutMomentum(self): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with test_util.use_gpu(): |
| 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) |
| opt = rmsprop.RMSPropOptimizer( |
| learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| rms0 = opt.get_slot(var0, "rms") |
| self.assertTrue(rms0 is not None) |
| rms1 = opt.get_slot(var1, "rms") |
| self.assertTrue(rms1 is not None) |
| mom0 = opt.get_slot(var0, "momentum") |
| self.assertTrue(mom0 is not None) |
| mom1 = opt.get_slot(var1, "momentum") |
| self.assertTrue(mom1 is not None) |
| |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([3.0, 4.0], self.evaluate(var1)) |
| # Step 1: the rms accumulators where 1. So we should see a normal |
| # update: v -= grad * learning_rate |
| self.evaluate(update) |
| # Check the root mean square accumulators. |
| self.assertAllCloseAccordingToType( |
| np.array([0.901, 0.901]), self.evaluate(rms0)) |
| self.assertAllCloseAccordingToType( |
| np.array([0.90001, 0.90001]), self.evaluate(rms1)) |
| # Check the parameters. |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)), |
| 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) |
| ]), self.evaluate(var0)) |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)), |
| 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) |
| ]), self.evaluate(var1)) |
| # Step 2: the root mean square accumulators contain the previous update. |
| self.evaluate(update) |
| # Check the rms accumulators. |
| self.assertAllCloseAccordingToType( |
| np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), |
| self.evaluate(rms0)) |
| self.assertAllCloseAccordingToType( |
| np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), |
| self.evaluate(rms1)) |
| # Check the parameters. |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)), |
| 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)) |
| ]), self.evaluate(var0)) |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)), |
| 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)) |
| ]), self.evaluate(var1)) |
| |
| @test_util.run_deprecated_v1 |
| def testWithMomentum(self): |
| for dtype in [dtypes.half, dtypes.float32]: |
| with test_util.use_gpu(): |
| 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) |
| |
| opt = rmsprop.RMSPropOptimizer( |
| learning_rate=2.0, decay=0.9, momentum=0.5, epsilon=1e-5) |
| update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| rms0 = opt.get_slot(var0, "rms") |
| self.assertTrue(rms0 is not None) |
| rms1 = opt.get_slot(var1, "rms") |
| self.assertTrue(rms1 is not None) |
| mom0 = opt.get_slot(var0, "momentum") |
| self.assertTrue(mom0 is not None) |
| mom1 = opt.get_slot(var1, "momentum") |
| self.assertTrue(mom1 is not None) |
| |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([3.0, 4.0], self.evaluate(var1)) |
| # Step 1: rms = 1, mom = 0. So we should see a normal |
| # update: v -= grad * learning_rate |
| self.evaluate(update) |
| # Check the root mean square accumulators. |
| self.assertAllCloseAccordingToType( |
| np.array([0.901, 0.901]), self.evaluate(rms0)) |
| self.assertAllCloseAccordingToType( |
| np.array([0.90001, 0.90001]), self.evaluate(rms1)) |
| # Check the momentum accumulators |
| self.assertAllCloseAccordingToType( |
| np.array([(0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), |
| (0.1 * 2.0 / math.sqrt(0.901 + 1e-5))]), |
| self.evaluate(mom0)) |
| self.assertAllCloseAccordingToType( |
| np.array([(0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), |
| (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5))]), |
| self.evaluate(mom1)) |
| |
| # Check that the parameters. |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), |
| 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) |
| ]), self.evaluate(var0)) |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), |
| 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) |
| ]), self.evaluate(var1)) |
| |
| # Step 2: the root mean square accumulators contain the previous update. |
| self.evaluate(update) |
| # Check the rms accumulators. |
| self.assertAllCloseAccordingToType( |
| np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), |
| self.evaluate(rms0)) |
| self.assertAllCloseAccordingToType( |
| np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), |
| self.evaluate(rms1)) |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)), |
| 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)) |
| ]), self.evaluate(mom0)) |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)), |
| 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)) |
| ]), self.evaluate(mom1)) |
| |
| # Check the parameters. |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - |
| (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))), |
| 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - |
| (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))) |
| ]), self.evaluate(var0)) |
| |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - |
| (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))), |
| 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - |
| (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))) |
| ]), self.evaluate(var1)) |
| |
| def testCallableParams(self): |
| with context.eager_mode(): |
| for dtype in [dtypes.half, dtypes.float32]: |
| var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) |
| var1 = resource_variable_ops.ResourceVariable([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 |
| decay = lambda: 0.9 |
| momentum = lambda: 0.0 |
| epsilon = lambda: 1.0 |
| opt = rmsprop.RMSPropOptimizer(learning_rate, decay, momentum, epsilon) |
| |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| self.assertAllClose([3.0, 4.0], self.evaluate(var1)) |
| # Step 1: the rms accumulators where 1. So we should see a normal |
| # update: v -= grad * learning_rate |
| opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| # Check the parameters. |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)), |
| 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) |
| ]), self.evaluate(var0)) |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)), |
| 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) |
| ]), self.evaluate(var1)) |
| # Step 2: the root mean square accumulators contain the previous update. |
| opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| # Check the parameters. |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)), |
| 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - |
| (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)) |
| ]), self.evaluate(var0)) |
| self.assertAllCloseAccordingToType( |
| np.array([ |
| 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)), |
| 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - |
| (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)) |
| ]), self.evaluate(var1)) |
| |
| |
| if __name__ == "__main__": |
| test.main() |
