bf9c196f37
We do not support complex with certain optimizers such as Ftrl, FtrlV2, AdamWithAmsgrad, AdaMax, AddSign & PowerSign since they may use missing operations on complex values such as sqrt. Fixes #32774 PiperOrigin-RevId: 277953548 Change-Id: Ia075aa5c3f944de932d71b9741d626f7ebe5416f
190 lines
7.9 KiB
Python
190 lines
7.9 KiB
Python
# Copyright 2017 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 Adam."""
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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 numpy as np
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from tensorflow.compiler.tests import xla_test
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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.ops import array_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variable_scope
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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 adam
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def adam_update_numpy(param,
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g_t,
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t,
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m,
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v,
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alpha=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8):
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alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
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m_t = beta1 * m + (1 - beta1) * g_t
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v_t = beta2 * v + (1 - beta2) * g_t * g_t
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param_t = param - alpha_t * m_t / (np.sqrt(v_t) + epsilon)
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return param_t, m_t, v_t
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class AdamOptimizerTest(xla_test.XLATestCase):
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def testBasic(self):
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for dtype in self.float_types | self.complex_types:
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# TODO: test fails for float16 due to excessive precision requirements.
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if dtype in [np.float16, dtypes.bfloat16.as_numpy_dtype]:
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continue
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with self.session(), self.test_scope():
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variable_scope.get_variable_scope().set_use_resource(True)
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# Initialize variables for numpy implementation.
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m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype)
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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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grads0 = array_ops.placeholder(dtype)
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grads1 = array_ops.placeholder(dtype)
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opt = adam.AdamOptimizer()
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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variables.global_variables_initializer().run()
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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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beta1_power, beta2_power = opt._get_beta_accumulators()
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# Run 3 steps of Adam
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for t in range(1, 4):
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self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power))
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self.assertAllCloseAccordingToType(0.999**t,
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self.evaluate(beta2_power))
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update.run(feed_dict={grads0: grads0_np, grads1: grads1_np})
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var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
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var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
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# Validate updated params
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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 testTensorLearningRate(self):
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for dtype in self.float_types | self.complex_types:
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# TODO: test fails for float16 due to excessive precision requirements.
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if dtype in [np.float16, dtypes.bfloat16.as_numpy_dtype]:
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continue
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with self.session(), self.test_scope():
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variable_scope.get_variable_scope().set_use_resource(True)
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# Initialize variables for numpy implementation.
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m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype)
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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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grads0 = array_ops.placeholder(dtype)
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grads1 = array_ops.placeholder(dtype)
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opt = adam.AdamOptimizer(constant_op.constant(0.001))
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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variables.global_variables_initializer().run()
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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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beta1_power, beta2_power = opt._get_beta_accumulators()
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# Run 3 steps of Adam
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for t in range(1, 4):
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self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power))
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self.assertAllCloseAccordingToType(0.999**t,
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self.evaluate(beta2_power))
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update.run(feed_dict={grads0: grads0_np, grads1: grads1_np})
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var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
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var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
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# Validate updated params
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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 testSharing(self):
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for dtype in self.float_types | self.complex_types:
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# TODO: test fails for float16 due to excessive precision requirements.
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if dtype in [np.float16, dtypes.bfloat16.as_numpy_dtype]:
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continue
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with self.session(), self.test_scope():
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variable_scope.get_variable_scope().set_use_resource(True)
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# Initialize variables for numpy implementation.
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m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype)
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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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grads0 = array_ops.placeholder(dtype)
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grads1 = array_ops.placeholder(dtype)
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opt = adam.AdamOptimizer()
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update1 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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update2 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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variables.global_variables_initializer().run()
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beta1_power, beta2_power = opt._get_beta_accumulators()
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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 intertwined Adam1 and Adam2.
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for t in range(1, 4):
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self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power))
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self.assertAllCloseAccordingToType(0.999**t,
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self.evaluate(beta2_power))
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if t % 2 == 0:
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update1.run(feed_dict={grads0: grads0_np, grads1: grads1_np})
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else:
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update2.run(feed_dict={grads0: grads0_np, grads1: grads1_np})
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var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
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var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
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# Validate updated params
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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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if __name__ == "__main__":
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test.main()
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