515 lines
22 KiB
Python
515 lines
22 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for rmsprop."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import itertools
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import math
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import numpy as np
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import embedding_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training import rmsprop
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_DATA_TYPES = [dtypes.half, dtypes.float32]
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_TEST_PARAM_VALUES = [
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# learning_rate, decay, momentum, epsilon, centered, use_resource
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[0.5, 0.9, 0.0, 1e-3, True, False],
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[0.5, 0.9, 0.0, 1e-3, False, False],
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[0.5, 0.9, 0.0, 1e-3, True, True],
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[0.5, 0.9, 0.0, 1e-3, False, True],
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[0.1, 0.9, 0.0, 1e-3, True, False],
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[0.5, 0.95, 0.0, 1e-3, False, False],
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[0.5, 0.95, 0.0, 1e-5, True, False],
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[0.5, 0.95, 0.9, 1e-5, True, False],
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]
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_TESTPARAMS = [
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[data_type] + values
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for data_type, values in itertools.product(_DATA_TYPES, _TEST_PARAM_VALUES)
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]
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class RMSPropOptimizerTest(test.TestCase):
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def _rmsprop_update_numpy(self, var, g, mg, rms, mom, lr, decay, momentum,
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epsilon, centered):
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rms_t = rms * decay + (1 - decay) * g * g
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denom_t = rms_t + epsilon
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if centered:
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mg_t = mg * decay + (1 - decay) * g
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denom_t -= mg_t * mg_t
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else:
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mg_t = mg
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mom_t = momentum * mom + lr * g / np.sqrt(denom_t, dtype=denom_t.dtype)
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var_t = var - mom_t
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return var_t, mg_t, rms_t, mom_t
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def _sparse_rmsprop_update_numpy(self, var, gindexs, gvalues, mg, rms, mom,
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lr, decay, momentum, epsilon, centered):
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mg_t = copy.deepcopy(mg)
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rms_t = copy.deepcopy(rms)
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mom_t = copy.deepcopy(mom)
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var_t = copy.deepcopy(var)
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for i in range(len(gindexs)):
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gindex = gindexs[i]
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gvalue = gvalues[i]
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rms_t[gindex] = rms[gindex] * decay + (1 - decay) * gvalue * gvalue
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denom_t = rms_t[gindex] + epsilon
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if centered:
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mg_t[gindex] = mg_t[gindex] * decay + (1 - decay) * gvalue
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denom_t -= mg_t[gindex] * mg_t[gindex]
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mom_t[gindex] = momentum * mom[gindex] + lr * gvalue / np.sqrt(denom_t)
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var_t[gindex] = var[gindex] - mom_t[gindex]
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return var_t, mg_t, rms_t, mom_t
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@test_util.run_deprecated_v1
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def testDense(self):
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# TODO(yori): Use ParameterizedTest when available
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for (dtype, learning_rate, decay, momentum,
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epsilon, centered, use_resource) in _TESTPARAMS:
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with test_util.use_gpu():
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# Initialize variables for numpy implementation.
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var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
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grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
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grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype)
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if use_resource:
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var0 = resource_variable_ops.ResourceVariable(var0_np)
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var1 = resource_variable_ops.ResourceVariable(var1_np)
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else:
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var0 = variables.Variable(var0_np)
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var1 = variables.Variable(var1_np)
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grads0 = constant_op.constant(grads0_np)
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grads1 = constant_op.constant(grads1_np)
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opt = rmsprop.RMSPropOptimizer(
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learning_rate=learning_rate,
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decay=decay,
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momentum=momentum,
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epsilon=epsilon,
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centered=centered)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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mg0 = opt.get_slot(var0, "mg")
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self.assertEqual(mg0 is not None, centered)
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mg1 = opt.get_slot(var1, "mg")
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self.assertEqual(mg1 is not None, centered)
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rms0 = opt.get_slot(var0, "rms")
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self.assertTrue(rms0 is not None)
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rms1 = opt.get_slot(var1, "rms")
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self.assertTrue(rms1 is not None)
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mom0 = opt.get_slot(var0, "momentum")
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self.assertTrue(mom0 is not None)
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mom1 = opt.get_slot(var1, "momentum")
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self.assertTrue(mom1 is not None)
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mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
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rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
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mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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# Run 4 steps of RMSProp
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for _ in range(1, 5):
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self.evaluate(update)
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var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy(
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var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate,
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decay, momentum, epsilon, centered)
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var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy(
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var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate,
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decay, momentum, epsilon, centered)
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# Validate updated params
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if centered:
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self.assertAllCloseAccordingToType(mg0_np, self.evaluate(mg0))
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self.assertAllCloseAccordingToType(mg1_np, self.evaluate(mg1))
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self.assertAllCloseAccordingToType(rms0_np, self.evaluate(rms0))
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self.assertAllCloseAccordingToType(rms1_np, self.evaluate(rms1))
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self.assertAllCloseAccordingToType(mom0_np, self.evaluate(mom0))
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self.assertAllCloseAccordingToType(mom1_np, self.evaluate(mom1))
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self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
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self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
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@test_util.run_deprecated_v1
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def testMinimizeSparseResourceVariable(self):
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for dtype in [dtypes.float32, dtypes.float64]:
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with self.cached_session():
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var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
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x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
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pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
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loss = pred * pred
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sgd_op = rmsprop.RMSPropOptimizer(
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learning_rate=1.0,
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decay=0.0,
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momentum=0.0,
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epsilon=0.0,
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centered=False).minimize(loss)
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self.evaluate(variables.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0))
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# Run 1 step of sgd
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self.evaluate(sgd_op)
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# Validate updated params
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self.assertAllCloseAccordingToType([[0., 1.]],
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self.evaluate(var0),
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atol=0.01)
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@test_util.run_deprecated_v1
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def testMinimizeSparseResourceVariableCentered(self):
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for dtype in [dtypes.float32, dtypes.float64]:
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with self.cached_session():
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var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
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x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
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pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
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loss = pred * pred
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sgd_op = rmsprop.RMSPropOptimizer(
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learning_rate=1.0,
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decay=0.0,
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momentum=0.0,
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epsilon=1.0,
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centered=True).minimize(loss)
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self.evaluate(variables.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0))
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# Run 1 step of sgd
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self.evaluate(sgd_op)
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# Validate updated params
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self.assertAllCloseAccordingToType([[-111, -138]],
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self.evaluate(var0),
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atol=0.01)
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@test_util.run_deprecated_v1
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def testSparse(self):
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# TODO(yori): Use ParameterizedTest when available
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for (dtype, learning_rate, decay,
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momentum, epsilon, centered, _) in _TESTPARAMS:
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with test_util.use_gpu():
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# Initialize variables for numpy implementation.
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var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
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grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
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grads1_np = np.array([0.01], dtype=dtype.as_numpy_dtype)
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var0 = variables.Variable(var0_np)
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var1 = variables.Variable(var1_np)
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grads0_np_indices = np.array([0], dtype=np.int32)
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grads0 = ops.IndexedSlices(
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constant_op.constant(grads0_np),
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constant_op.constant(grads0_np_indices), constant_op.constant([1]))
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grads1_np_indices = np.array([1], dtype=np.int32)
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grads1 = ops.IndexedSlices(
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constant_op.constant(grads1_np),
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constant_op.constant(grads1_np_indices), constant_op.constant([1]))
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opt = rmsprop.RMSPropOptimizer(
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learning_rate=learning_rate,
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decay=decay,
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momentum=momentum,
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epsilon=epsilon,
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centered=centered)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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mg0 = opt.get_slot(var0, "mg")
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self.assertEqual(mg0 is not None, centered)
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mg1 = opt.get_slot(var1, "mg")
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self.assertEqual(mg1 is not None, centered)
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rms0 = opt.get_slot(var0, "rms")
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self.assertTrue(rms0 is not None)
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rms1 = opt.get_slot(var1, "rms")
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self.assertTrue(rms1 is not None)
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mom0 = opt.get_slot(var0, "momentum")
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self.assertTrue(mom0 is not None)
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mom1 = opt.get_slot(var1, "momentum")
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self.assertTrue(mom1 is not None)
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mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
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rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
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mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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# Run 4 steps of RMSProp
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for _ in range(1, 5):
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self.evaluate(update)
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var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy(
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var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np,
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learning_rate, decay, momentum, epsilon, centered)
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var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy(
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var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np,
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learning_rate, decay, momentum, epsilon, centered)
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# Validate updated params
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if centered:
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self.assertAllCloseAccordingToType(mg0_np, self.evaluate(mg0))
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self.assertAllCloseAccordingToType(mg1_np, self.evaluate(mg1))
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self.assertAllCloseAccordingToType(rms0_np, self.evaluate(rms0))
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self.assertAllCloseAccordingToType(rms1_np, self.evaluate(rms1))
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self.assertAllCloseAccordingToType(mom0_np, self.evaluate(mom0))
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self.assertAllCloseAccordingToType(mom1_np, self.evaluate(mom1))
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self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
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self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
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@test_util.run_deprecated_v1
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def testWithoutMomentum(self):
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for dtype in [dtypes.half, dtypes.float32]:
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with test_util.use_gpu():
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var0 = variables.Variable([1.0, 2.0], dtype=dtype)
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var1 = variables.Variable([3.0, 4.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
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opt = rmsprop.RMSPropOptimizer(
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learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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rms0 = opt.get_slot(var0, "rms")
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self.assertTrue(rms0 is not None)
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rms1 = opt.get_slot(var1, "rms")
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self.assertTrue(rms1 is not None)
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mom0 = opt.get_slot(var0, "momentum")
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self.assertTrue(mom0 is not None)
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mom1 = opt.get_slot(var1, "momentum")
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self.assertTrue(mom1 is not None)
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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self.assertAllClose([3.0, 4.0], self.evaluate(var1))
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# Step 1: the rms accumulators where 1. So we should see a normal
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# update: v -= grad * learning_rate
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self.evaluate(update)
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# Check the root mean square accumulators.
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self.assertAllCloseAccordingToType(
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np.array([0.901, 0.901]), self.evaluate(rms0))
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self.assertAllCloseAccordingToType(
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np.array([0.90001, 0.90001]), self.evaluate(rms1))
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# Check the parameters.
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self.assertAllCloseAccordingToType(
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np.array([
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1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)),
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2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0))
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]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([
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3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)),
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4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0))
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]), self.evaluate(var1))
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# Step 2: the root mean square accumulators contain the previous update.
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self.evaluate(update)
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# Check the rms accumulators.
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self.assertAllCloseAccordingToType(
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np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]),
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self.evaluate(rms0))
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self.assertAllCloseAccordingToType(
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np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]),
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self.evaluate(rms1))
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# Check the parameters.
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self.assertAllCloseAccordingToType(
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np.array([
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1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) -
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(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)),
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2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) -
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(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0))
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]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([
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3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) -
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(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)),
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4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) -
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(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0))
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]), self.evaluate(var1))
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@test_util.run_deprecated_v1
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def testWithMomentum(self):
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for dtype in [dtypes.half, dtypes.float32]:
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with test_util.use_gpu():
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var0 = variables.Variable([1.0, 2.0], dtype=dtype)
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var1 = variables.Variable([3.0, 4.0], dtype=dtype)
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grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
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grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
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opt = rmsprop.RMSPropOptimizer(
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learning_rate=2.0, decay=0.9, momentum=0.5, epsilon=1e-5)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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rms0 = opt.get_slot(var0, "rms")
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self.assertTrue(rms0 is not None)
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rms1 = opt.get_slot(var1, "rms")
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self.assertTrue(rms1 is not None)
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mom0 = opt.get_slot(var0, "momentum")
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self.assertTrue(mom0 is not None)
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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()
|