wehub-resource-sync
343 lines
15 KiB
Python
343 lines
15 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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"""Functional tests for aggregate operations."""
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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 indexed_slices
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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 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 adagrad
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class AdagradOptimizerTest(test.TestCase):
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def doTestBasic(self,
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use_locking=False,
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use_resource=False,
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use_callable_params=False):
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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if use_resource:
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var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
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var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
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else:
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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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learning_rate = lambda: 3.0
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if not use_callable_params:
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learning_rate = learning_rate()
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ada_opt = adagrad.AdagradOptimizer(
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learning_rate, initial_accumulator_value=0.1, use_locking=use_locking)
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if not context.executing_eagerly():
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ada_update = ada_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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# Fetch params to validate initial values
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v0_val, v1_val = self.evaluate([var0, var1])
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self.assertAllClose([1.0, 2.0], v0_val)
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self.assertAllClose([3.0, 4.0], v1_val)
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# Run 3 steps of adagrad
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for _ in range(3):
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if not context.executing_eagerly():
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self.evaluate(ada_update)
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else:
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ada_opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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# Validate updated params
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v0_val, v1_val = self.evaluate([var0, var1])
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self.assertAllCloseAccordingToType(
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np.array([-1.6026098728179932, -0.6026098728179932]), v0_val)
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self.assertAllCloseAccordingToType(
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np.array([2.715679168701172, 3.715679168701172]), v1_val)
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def testBasic(self):
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self.doTestBasic(use_locking=False)
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@test_util.run_in_graph_and_eager_modes
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def testBasicResource(self):
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self.doTestBasic(use_locking=False, use_resource=True)
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def testBasicCallableParams(self):
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with context.eager_mode():
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self.doTestBasic(
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use_locking=False, use_resource=True, use_callable_params=True)
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def testBasicLocked(self):
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self.doTestBasic(use_locking=True)
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def testMinimizeSparseResourceVariable(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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var0 = resource_variable_ops.ResourceVariable(
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[[1.0, 2.0], [3.0, 4.0]], 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 = adagrad.AdagradOptimizer(1.0).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], [3.0, 4.0]],
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self.evaluate(var0))
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# Run 1 step of sgd
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sgd_op.run()
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# Validate updated params
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self.assertAllCloseAccordingToType([[0, 1], [3, 4]],
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self.evaluate(var0),
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atol=0.01)
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def testTensorLearningRate(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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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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ada_opt = adagrad.AdagradOptimizer(
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constant_op.constant(3.0), initial_accumulator_value=0.1)
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ada_update = ada_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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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.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 adagrad
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for _ in range(3):
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ada_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([-1.6026098728179932, -0.6026098728179932]),
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self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([2.715679168701172, 3.715679168701172]),
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self.evaluate(var1))
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def testSparseBasic(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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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 = indexed_slices.IndexedSlices(
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constant_op.constant(
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[0.1], shape=[1, 1], dtype=dtype),
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constant_op.constant([0]),
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constant_op.constant([2, 1]))
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grads1 = indexed_slices.IndexedSlices(
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constant_op.constant(
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[0.01], shape=[1, 1], dtype=dtype),
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constant_op.constant([1]),
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constant_op.constant([2, 1]))
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ada_opt = adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1)
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ada_update = ada_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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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.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 step of sgd
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for _ in range(3):
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ada_update.run()
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# Validate updated params
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self.assertAllCloseAccordingToType(
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np.array([[-1.6026098728179932], [2.0]]), self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([[3.0], [3.715679168701172]]), self.evaluate(var1))
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def testSparseRepeatedIndices(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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repeated_index_update_var = variables.Variable(
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[[1.0], [2.0]], dtype=dtype)
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aggregated_update_var = variables.Variable(
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[[1.0], [2.0]], dtype=dtype)
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grad_repeated_index = indexed_slices.IndexedSlices(
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constant_op.constant(
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[0.1, 0.1], shape=[2, 1], dtype=dtype),
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constant_op.constant([1, 1]),
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constant_op.constant([2, 1]))
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grad_aggregated = indexed_slices.IndexedSlices(
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constant_op.constant(
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[0.2], shape=[1, 1], dtype=dtype),
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constant_op.constant([1]),
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constant_op.constant([2, 1]))
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repeated_update = adagrad.AdagradOptimizer(3.0).apply_gradients(
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[(grad_repeated_index, repeated_index_update_var)])
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aggregated_update = adagrad.AdagradOptimizer(3.0).apply_gradients(
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[(grad_aggregated, aggregated_update_var)])
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self.evaluate(variables.global_variables_initializer())
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self.assertAllClose(aggregated_update_var,
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self.evaluate(repeated_index_update_var))
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for _ in range(3):
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repeated_update.run()
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aggregated_update.run()
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self.assertAllClose(aggregated_update_var,
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self.evaluate(repeated_index_update_var))
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def testSparseRepeatedIndicesResourceVariable(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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var_repeated = resource_variable_ops.ResourceVariable(
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[1.0, 2.0], dtype=dtype)
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loss_repeated = math_ops.reduce_sum(
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embedding_ops.embedding_lookup(var_repeated, [0, 0]))
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var_aggregated = resource_variable_ops.ResourceVariable(
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[1.0, 2.0], dtype=dtype)
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loss_aggregated = 2 * math_ops.reduce_sum(
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embedding_ops.embedding_lookup(var_aggregated, [0]))
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update_op_repeated = adagrad.AdagradOptimizer(
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2.0).minimize(loss_repeated)
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update_op_aggregated = adagrad.AdagradOptimizer(
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2.0).minimize(loss_aggregated)
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self.evaluate(variables.global_variables_initializer())
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self.assertAllCloseAccordingToType(
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self.evaluate(var_repeated), self.evaluate(var_aggregated))
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for _ in range(3):
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update_op_repeated.run()
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update_op_aggregated.run()
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self.assertAllCloseAccordingToType(
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self.evaluate(var_repeated), self.evaluate(var_aggregated))
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def testSparseStability(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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shape = [1, 6]
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var0 = variables.Variable(
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[[
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0.00872496, -0.106952, 0.110467, 0.226505, -0.0147257,
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-0.0105945
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]],
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dtype=dtype)
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grads0 = indexed_slices.IndexedSlices(
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constant_op.constant(
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[[
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-5.91278e-05, 5.31673e-05, -2.5779e-06, 4.29153e-05,
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-8.4877e-05, -9.48906e-05
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]],
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shape=shape,
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dtype=dtype),
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constant_op.constant([0]),
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constant_op.constant(shape))
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ada_opt = adagrad.AdagradOptimizer(1.0, initial_accumulator_value=0.1)
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ada_update = ada_opt.apply_gradients(zip([grads0], [var0]))
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self.assertEqual(["accumulator"], ada_opt.get_slot_names())
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slot0 = ada_opt.get_slot(var0, "accumulator")
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init = variables.global_variables_initializer()
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for _ in range(100):
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init.run()
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ada_update.run()
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self.assertAllCloseAccordingToType(
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np.array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]),
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self.evaluate(slot0))
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self.assertAllCloseAccordingToType(
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np.array([[
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0.00891194, -0.10712013, 0.11047515, 0.22636929, -0.0144573,
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-0.01029443
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]]), self.evaluate(var0))
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def testSharing(self):
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with ops.Graph().as_default():
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for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
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with self.cached_session():
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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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ada_opt = adagrad.AdagradOptimizer(3.0)
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# Apply the optimizer twice. Both applications will use
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# the same accums.
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ada_update1 = ada_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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ada_update2 = ada_opt.apply_gradients(
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zip([grads0, grads1], [var0, var1]))
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self.assertEqual(["accumulator"], ada_opt.get_slot_names())
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slot0 = ada_opt.get_slot(var0, "accumulator")
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self.assertEqual(slot0.get_shape(), var0.get_shape())
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slot1 = ada_opt.get_slot(var1, "accumulator")
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self.assertEqual(slot1.get_shape(), var1.get_shape())
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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.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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# Mix the first and the second adagrad for 3 steps.
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ada_update1.run()
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ada_update2.run()
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ada_update1.run()
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# Validate updated params (the same as with only 1 Adagrad).
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self.assertAllCloseAccordingToType(
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np.array([-1.6026098728179932, -0.6026098728179932]),
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self.evaluate(var0))
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self.assertAllCloseAccordingToType(
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np.array([2.715679168701172, 3.715679168701172]),
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self.evaluate(var1))
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def testDynamicShapeVariableWithCallableInit(self):
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with ops.Graph().as_default():
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var0 = variable_scope.get_variable("var0",
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initializer=constant_op.constant(1.),
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validate_shape=False)
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grads0 = constant_op.constant(0.1, dtype=dtypes.float32)
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learning_rate = lambda: 3.0
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ada_opt = adagrad.AdagradOptimizer(
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learning_rate, initial_accumulator_value=0.1, use_locking=True)
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if not context.executing_eagerly():
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ada_update = ada_opt.apply_gradients(
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zip([grads0], [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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v0_val = self.evaluate([var0])
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self.assertAllClose([1.0], v0_val)
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# Run 3 steps of adagrad
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for _ in range(3):
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if not context.executing_eagerly():
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self.evaluate(ada_update)
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else:
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ada_opt.apply_gradients(zip([grads0], [var0]))
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# Validate updated params
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v0_val = self.evaluate([var0])
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self.assertAllCloseAccordingToType(
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np.array([-1.6026098728179932]), v0_val)
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if __name__ == "__main__":
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test.main()
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