594 lines
24 KiB
Python
594 lines
24 KiB
Python
# Copyright 2018 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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from absl.testing import parameterized
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import numpy as np
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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.keras import combinations
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule
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from tensorflow.python.keras.optimizer_v2 import rmsprop
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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 variables
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from tensorflow.python.platform import test
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_DATA_TYPES = [dtypes.half, dtypes.float32, dtypes.float64]
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# TODO(b/143684500): Eigen to support complex sqrt
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if not test_util.IsBuiltWithNvcc():
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_DATA_TYPES += [dtypes.complex64, dtypes.complex128]
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_TEST_PARAM_VALUES = [
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# learning_rate, rho, momentum, epsilon, centered
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[0.05, 0.9, 0.0, 1e-3, True],
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[0.05, 0.9, 0.0, 1e-3, False],
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[0.1, 0.9, 0.0, 1e-3, True],
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[0.01, 0.9, 0.0, 1e-5, True],
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[0.01, 0.9, 0.9, 1e-5, True],
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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, parameterized.TestCase):
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def _rmsprop_update_numpy(self, var, g, mg, rms, mom, lr, rho, momentum,
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epsilon, centered):
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rms_t = rms * rho + (1 - rho) * g * g
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if centered:
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mg_t = mg * rho + (1 - rho) * g
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denom_t = rms_t - mg_t * mg_t
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else:
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mg_t = mg
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denom_t = rms_t
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if momentum > 0.:
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mom_t = momentum * mom + lr * g / (np.sqrt(denom_t + epsilon))
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var_t = var - mom_t
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else:
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mom_t = mom
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var_t = var - lr * g / (np.sqrt(denom_t) + epsilon)
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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, rho, 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] * rho + (1 - rho) * gvalue * gvalue
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if centered:
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mg_t[gindex] = mg_t[gindex] * rho + (1 - rho) * gvalue
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denom_t = rms_t[gindex] - mg_t[gindex] * mg_t[gindex]
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else:
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denom_t = rms_t[gindex]
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if momentum > 0.:
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mom_t[gindex] = momentum * mom[gindex] + lr * gvalue / np.sqrt(denom_t +
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epsilon)
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var_t[gindex] = var[gindex] - mom_t[gindex]
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else:
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mom_t[gindex] = mom[gindex]
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var_t[gindex] = var[gindex] - lr * gvalue / (np.sqrt(denom_t) + epsilon)
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return var_t, mg_t, rms_t, mom_t
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def testDense(self):
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# TODO(tanzheny, omalleyt): Fix test in eager mode.
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for (dtype, learning_rate, rho, momentum, epsilon, centered) in _TESTPARAMS:
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with ops.get_default_graph().as_default(), testing_utils.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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var0 = variables.Variable(var0_np, dtype=dtype)
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var1 = variables.Variable(var1_np, dtype=dtype)
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grads0 = constant_op.constant(grads0_np, dtype=dtype)
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grads1 = constant_op.constant(grads1_np, dtype=dtype)
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opt = rmsprop.RMSprop(
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learning_rate=learning_rate,
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rho=rho,
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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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if centered:
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mg0 = opt.get_slot(var0, "mg")
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mg1 = opt.get_slot(var1, "mg")
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else:
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mg0 = None
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mg1 = None
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if momentum > 0.:
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mom0 = opt.get_slot(var0, "momentum")
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mom1 = opt.get_slot(var1, "momentum")
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else:
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mom0 = None
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mom1 = None
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rms0 = opt.get_slot(var0, "rms")
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self.assertIsNotNone(rms0)
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rms1 = opt.get_slot(var1, "rms")
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self.assertIsNotNone(rms1)
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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([0.0, 0.0], dtype=dtype.as_numpy_dtype)
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rms1_np = np.array([0.0, 0.0], 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 3 steps of RMSprop
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for _ in range(1, 4):
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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, rho,
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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, rho,
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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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if momentum > 0.:
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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(rms0_np, self.evaluate(rms0))
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self.assertAllCloseAccordingToType(rms1_np, self.evaluate(rms1))
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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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def testDenseWithLearningRateDecay(self):
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# TODO(tanzheny, omalleyt): Fix test in eager mode.
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with ops.Graph().as_default():
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var0_np = np.array([1.0, 2.0])
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grads0_np = np.array([0.1, 0.2])
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var1_np = np.array([3.0, 4.0])
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grads1_np = np.array([0.01, 0.2])
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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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learning_rate = 0.01
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rho = 0.9
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momentum = 0.0
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epsilon = 1e-7
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centered = False
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decay = 0.5
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opt = rmsprop.RMSprop(
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learning_rate=learning_rate,
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rho=rho,
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momentum=momentum,
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epsilon=epsilon,
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centered=centered,
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decay=decay)
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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.assertIsNotNone(rms0)
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rms1 = opt.get_slot(var1, "rms")
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self.assertIsNotNone(rms1)
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if momentum > 0.:
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mom0 = opt.get_slot(var0, "momentum")
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mom1 = opt.get_slot(var1, "momentum")
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else:
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mom0 = None
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mom1 = None
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mg0_np = np.array([0.0, 0.0])
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mg1_np = np.array([0.0, 0.0])
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rms0_np = np.array([0.0, 0.0])
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rms1_np = np.array([0.0, 0.0])
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mom0_np = np.array([0.0, 0.0])
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mom1_np = np.array([0.0, 0.0])
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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 t in range(2):
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self.evaluate(update)
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lr = learning_rate / (1 + decay * t)
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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, lr, rho, momentum,
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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, lr, rho, momentum,
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epsilon, centered)
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# Validate updated params
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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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if momentum > 0.:
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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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def testDenseWithLearningRateInverseTimeDecay(self):
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# TODO(tanzheny, omalleyt): Fix test in eager mode.
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with ops.Graph().as_default():
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var0_np = np.array([1.0, 2.0])
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grads0_np = np.array([0.1, 0.2])
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var1_np = np.array([3.0, 4.0])
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grads1_np = np.array([0.01, 0.2])
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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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learning_rate = 0.01
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rho = 0.9
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momentum = 0.0
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epsilon = 1e-7
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centered = False
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decay = 0.5
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lr_schedule = learning_rate_schedule.InverseTimeDecay(
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learning_rate, decay_steps=1.0, decay_rate=decay)
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opt = rmsprop.RMSprop(
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learning_rate=lr_schedule,
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rho=rho,
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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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rms0 = opt.get_slot(var0, "rms")
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self.assertIsNotNone(rms0)
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rms1 = opt.get_slot(var1, "rms")
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self.assertIsNotNone(rms1)
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if momentum > 0.:
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mom0 = opt.get_slot(var0, "momentum")
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mom1 = opt.get_slot(var1, "momentum")
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else:
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mom0 = None
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mom1 = None
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mg0_np = np.array([0.0, 0.0])
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mg1_np = np.array([0.0, 0.0])
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rms0_np = np.array([0.0, 0.0])
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rms1_np = np.array([0.0, 0.0])
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mom0_np = np.array([0.0, 0.0])
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mom1_np = np.array([0.0, 0.0])
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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 t in range(2):
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self.evaluate(update)
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lr = learning_rate / (1 + decay * t)
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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, lr, rho, momentum,
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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, lr, rho, momentum,
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epsilon, centered)
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# Validate updated params
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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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if momentum > 0.:
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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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def testMinimizeSparseResourceVariable(self):
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# TODO(tanzheny, omalleyt): Fix test in eager mode.
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with ops.Graph().as_default():
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for dtype in _DATA_TYPES:
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var0 = variables.Variable([[1.0, 2.0]], dtype=dtype)
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x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
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def loss():
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pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop
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return pred * pred
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sgd_op = rmsprop.RMSprop(
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learning_rate=1.0, rho=0.0, momentum=0.0, epsilon=0.0,
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centered=False).minimize(
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loss, var_list=[var0])
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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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def testMinimizeSparseResourceVariableCentered(self):
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# TODO(tanzheny, omalleyt): Fix test in eager mode.
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with ops.Graph().as_default():
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for dtype in _DATA_TYPES:
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if test_util.is_xla_enabled() and dtype.is_complex:
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self.skipTest("b/143578550")
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var0 = variables.Variable([[1.0, 2.0]], dtype=dtype)
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x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
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def loss():
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pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop
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return pred * pred
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# loss = lambda: pred * pred # pylint: disable=cell-var-from-loop
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sgd_op = rmsprop.RMSprop(
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learning_rate=1.0, rho=0.0, momentum=0.0, epsilon=1.0,
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centered=True).minimize(
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loss, var_list=[var0])
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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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def testSparse(self):
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# TODO(tanzheny, omalleyt): Fix test in eager mode.
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for (dtype, learning_rate, rho, momentum, epsilon, centered) in _TESTPARAMS:
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with ops.get_default_graph().as_default(), testing_utils.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.RMSprop(
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learning_rate=learning_rate,
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rho=rho,
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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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if centered:
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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)
|
|
else:
|
|
mg0 = None
|
|
mg1 = None
|
|
rms0 = opt.get_slot(var0, "rms")
|
|
self.assertIsNotNone(rms0)
|
|
rms1 = opt.get_slot(var1, "rms")
|
|
self.assertIsNotNone(rms1)
|
|
if momentum > 0.:
|
|
mom0 = opt.get_slot(var0, "momentum")
|
|
mom1 = opt.get_slot(var1, "momentum")
|
|
else:
|
|
mom0 = None
|
|
mom1 = 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([0.0, 0.0], dtype=dtype.as_numpy_dtype)
|
|
rms1_np = np.array([0.0, 0.0], 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 3 steps of RMSprop
|
|
for _ in range(1, 4):
|
|
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, rho, 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, rho, 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))
|
|
if momentum > 0.:
|
|
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))
|
|
|
|
@combinations.generate(combinations.combine(mode=["eager"]))
|
|
def testCallableParams(self):
|
|
for dtype in _DATA_TYPES:
|
|
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
|
|
rho = lambda: 0.9
|
|
momentum = lambda: 0.0
|
|
epsilon = 1.0
|
|
opt = rmsprop.RMSprop(learning_rate, rho, 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(0.001 + 1.0)),
|
|
2.0 - (0.1 * 2.0 / math.sqrt(0.001 + 1.0))
|
|
]), self.evaluate(var0))
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([
|
|
3.0 - (0.01 * 2.0 / math.sqrt(0.00001 + 1.0)),
|
|
4.0 - (0.01 * 2.0 / math.sqrt(0.00001 + 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(0.001 + 1.0)) -
|
|
(0.1 * 2.0 / math.sqrt(0.001 * 0.9 + 0.001 + 1.0)),
|
|
2.0 - (0.1 * 2.0 / math.sqrt(0.001 + 1.0)) -
|
|
(0.1 * 2.0 / math.sqrt(0.001 * 0.9 + 0.001 + 1.0))
|
|
]), self.evaluate(var0))
|
|
self.assertAllCloseAccordingToType(
|
|
np.array([
|
|
3.0 - (0.01 * 2.0 / math.sqrt(0.00001 + 1.0)) -
|
|
(0.01 * 2.0 / math.sqrt(0.00001 * 0.9 + 1e-5 + 1.0)),
|
|
4.0 - (0.01 * 2.0 / math.sqrt(0.00001 + 1.0)) -
|
|
(0.01 * 2.0 / math.sqrt(0.00001 * 0.9 + 1e-5 + 1.0))
|
|
]), self.evaluate(var1))
|
|
|
|
def testConstructRMSpropWithLR(self):
|
|
opt = rmsprop.RMSprop(lr=1.0)
|
|
opt_2 = rmsprop.RMSprop(learning_rate=0.1, lr=1.0)
|
|
opt_3 = rmsprop.RMSprop(learning_rate=0.1)
|
|
self.assertIsInstance(opt.lr, variables.Variable)
|
|
self.assertIsInstance(opt_2.lr, variables.Variable)
|
|
self.assertIsInstance(opt_3.lr, variables.Variable)
|
|
|
|
self.evaluate(variables.global_variables_initializer())
|
|
self.assertAllClose(self.evaluate(opt.lr), (1.0))
|
|
self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
|
|
self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
|
|
|
|
@combinations.generate(combinations.combine(mode=["eager"]))
|
|
def testSlotsUniqueEager(self):
|
|
v1 = variables.Variable(1.)
|
|
v2 = variables.Variable(1.)
|
|
|
|
opt = rmsprop.RMSprop(1., momentum=0., centered=False)
|
|
opt.minimize(lambda: v1 + v2, var_list=[v1, v2])
|
|
# There should be iteration, and one unique slot variable for v1 and v2.
|
|
self.assertLen(set({id(v) for v in opt.variables()}), 3)
|
|
self.assertEqual(
|
|
self.evaluate(opt.variables()[0]), self.evaluate(opt.iterations))
|
|
|
|
opt = rmsprop.RMSprop(learning_rate=1., momentum=0.2, centered=False)
|
|
opt.minimize(lambda: v1 + v2, var_list=[v1, v2])
|
|
# There should be iteration, and two unique slot variables for v1 and v2.
|
|
self.assertLen(set({id(v) for v in opt.variables()}), 5)
|
|
self.assertEqual(
|
|
self.evaluate(opt.variables()[0]), self.evaluate(opt.iterations))
|
|
|
|
opt = rmsprop.RMSprop(learning_rate=1., momentum=0.2, centered=True)
|
|
opt.minimize(lambda: v1 + v2, var_list=[v1, v2])
|
|
# There should be iteration, and three unique slot variables for v1 and v2
|
|
self.assertLen(set({id(v) for v in opt.variables()}), 7)
|
|
self.assertEqual(
|
|
self.evaluate(opt.variables()[0]), self.evaluate(opt.iterations))
|
|
|
|
|
|
@combinations.generate(combinations.combine(mode=["graph", "eager"]))
|
|
class SlotColocationTest(test.TestCase, parameterized.TestCase):
|
|
|
|
@parameterized.parameters([True, False])
|
|
@test_util.run_gpu_only
|
|
def testRunMinimizeOnGPUForCPUVariables(self, use_resource):
|
|
with ops.device("/device:CPU:0"):
|
|
if use_resource:
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
|
|
var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
|
|
else:
|
|
var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
|
|
var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
|
|
|
|
def loss():
|
|
return 5 * var0 + 3 * var1
|
|
|
|
opt = rmsprop.RMSprop(
|
|
learning_rate=1.0, decay=0.9, momentum=0.5, epsilon=1.0)
|
|
|
|
# Fetch params to validate initial values
|
|
self.evaluate(variables.global_variables_initializer())
|
|
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
|
|
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
|
|
|
|
# Run 1 step through optimizer on GPU.
|
|
# Slot variables are created the first time optimizer is used on some
|
|
# variable. This tests that slot variables will be colocated with the base
|
|
# variable.
|
|
with ops.device("/device:GPU:0"):
|
|
# Note that for eager execution, minimize expects a function instead of a
|
|
# Tensor.
|
|
opt_op = opt.minimize(loss, [var0, var1])
|
|
self.evaluate(variables.global_variables_initializer())
|
|
self.evaluate(opt_op)
|
|
|
|
# Validate updated params, All variables should have decreased.
|
|
self.assertTrue(all(v < 0.0 for v in self.evaluate(var0)),
|
|
msg="updated variables: %s" % self.evaluate(var0))
|
|
self.assertTrue(all(v < 2.0 for v in self.evaluate(var1)),
|
|
msg="updated variables: %s" % self.evaluate(var1))
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|