199 lines
7.9 KiB
Python
199 lines
7.9 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 Adadelta Optimizer."""
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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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from absl.testing import parameterized
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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.keras import combinations
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from tensorflow.python.keras.optimizer_v2 import adadelta
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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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class AdadeltaOptimizerTest(test.TestCase, parameterized.TestCase):
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def doTestBasic(self, use_resource=False, use_callable_params=False):
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num_updates = 4 # number of ADADELTA steps to perform
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for dtype in _DATA_TYPES:
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for grad in [0.2, 0.1, 0.01]:
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for lr in [1.0, 0.5, 0.1]:
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var0_init = [1.0, 2.0]
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var1_init = [3.0, 4.0]
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if use_resource:
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var0 = variables.Variable(var0_init, dtype=dtype)
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var1 = variables.Variable(var1_init, dtype=dtype)
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else:
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var0 = variables.Variable(var0_init, dtype=dtype)
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var1 = variables.Variable(var1_init, dtype=dtype)
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grads = constant_op.constant([grad, grad], dtype=dtype)
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accum = 0.0
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accum_update = 0.0
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# ADADELTA gradient optimizer
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rho = 0.95
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epsilon = 1e-8
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if use_callable_params:
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adadelta_opt = adadelta.Adadelta(
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learning_rate=lambda: lr, # pylint: disable=cell-var-from-loop
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rho=lambda: rho, # pylint: disable=cell-var-from-loop
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epsilon=epsilon) # pylint: disable=cell-var-from-loop
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else:
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adadelta_opt = adadelta.Adadelta(
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learning_rate=lr, rho=rho, epsilon=epsilon)
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if not context.executing_eagerly():
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adadelta_update = adadelta_opt.apply_gradients(
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zip([grads, grads], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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# Assign slots
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slot = [None] * 2
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slot_update = [None] * 2
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slot[0] = adadelta_opt.get_slot(var0, "accum_grad")
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self.assertEqual(slot[0].shape, var0.shape)
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slot_update[0] = adadelta_opt.get_slot(var0, "accum_var")
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self.assertEqual(slot_update[0].shape, var0.shape)
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slot[1] = adadelta_opt.get_slot(var1, "accum_grad")
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self.assertEqual(slot[1].shape, var1.shape)
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slot_update[1] = adadelta_opt.get_slot(var1, "accum_var")
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self.assertEqual(slot_update[1].shape, var1.shape)
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# Fetch params to validate initial values
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self.assertAllClose(var0_init, self.evaluate(var0))
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self.assertAllClose(var1_init, self.evaluate(var1))
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update = [None] * num_updates
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tot_update = 0
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for step in range(num_updates):
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# Run adadelta update for comparison
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if not context.executing_eagerly():
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self.evaluate(adadelta_update)
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else:
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adadelta_opt.apply_gradients(zip([grads, grads], [var0, var1]))
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# Perform initial update without previous accum values
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accum = accum * rho + (grad**2) * (1 - rho)
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update[step] = (
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np.sqrt(accum_update + epsilon) *
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(1. / np.sqrt(accum + epsilon)) * grad)
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accum_update = (
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accum_update * rho + (update[step]**2) * (1.0 - rho))
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tot_update += update[step] * lr
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if not context.executing_eagerly():
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# Check that the accumulators have been updated
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# TODO(lxuechen): This is hard to test in eager mode
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for slot_idx in range(2):
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self.assertAllCloseAccordingToType(
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np.array([accum, accum], dtype=dtype.as_numpy_dtype(0)),
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self.evaluate(slot[slot_idx]),
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rtol=1e-5)
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self.assertAllCloseAccordingToType(
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np.array(
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[accum_update, accum_update],
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dtype=dtype.as_numpy_dtype(0)),
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self.evaluate(slot_update[slot_idx]),
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rtol=1e-5)
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# Check that the parameters have been updated
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self.assertAllCloseAccordingToType(
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np.array(
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[var0_init[0] - tot_update, var0_init[1] - tot_update],
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dtype=dtype.as_numpy_dtype(0)),
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self.evaluate(var0),
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rtol=1e-5)
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self.assertAllCloseAccordingToType(
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np.array(
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[var1_init[0] - tot_update, var1_init[1] - tot_update],
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dtype=dtype.as_numpy_dtype(0)),
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self.evaluate(var1),
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rtol=1e-5)
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@combinations.generate(combinations.combine(mode=["graph", "eager"]))
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def testResourceBasic(self):
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self.doTestBasic(use_resource=True)
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@combinations.generate(combinations.combine(mode=["eager"]))
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def testBasicCallableParams(self):
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self.doTestBasic(use_resource=True, use_callable_params=True)
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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 = adadelta.Adadelta(1.0, 1.0, 1.0).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]], self.evaluate(var0))
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def testConstructAdadeltaWithLR(self):
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opt = adadelta.Adadelta(lr=1.0, rho=0.9, epsilon=1.)
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opt_2 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1., lr=1.0)
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opt_3 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1.)
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self.assertIsInstance(opt.lr, variables.Variable)
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self.assertIsInstance(opt_2.lr, variables.Variable)
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self.assertIsInstance(opt_3.lr, variables.Variable)
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self.evaluate(variables.global_variables_initializer())
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self.assertAllClose(self.evaluate(opt.lr), (1.0))
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self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
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self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
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def testConstructAdadeltaWithEpsilonValues(self):
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opt = adadelta.Adadelta(epsilon=None)
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self.assertEqual(opt.epsilon, 1e-7)
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opt = adadelta.Adadelta(epsilon=1e-8)
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self.assertEqual(opt.epsilon, 1e-8)
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if __name__ == "__main__":
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test.main()
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