41339588d9
All the test case has been updated to run with graph context, since the API expect to run in v1 graph context. PiperOrigin-RevId: 321296244 Change-Id: I045584d2003febc0dd32b94abccc7382f07eb3d8
208 lines
8.1 KiB
Python
208 lines
8.1 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 Proximal Gradient Descent operations."""
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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.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.ops import embedding_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training import gradient_descent
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from tensorflow.python.training import proximal_gradient_descent
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class ProximalGradientDescentOptimizerTest(test.TestCase):
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def doTestProximalGradientDescentwithoutRegularization(
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self, use_resource=False):
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with ops.Graph().as_default(), self.cached_session():
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if use_resource:
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var0 = resource_variable_ops.ResourceVariable([0.0, 0.0])
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var1 = resource_variable_ops.ResourceVariable([0.0, 0.0])
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else:
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var0 = variables.Variable([0.0, 0.0])
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var1 = variables.Variable([0.0, 0.0])
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grads0 = constant_op.constant([0.1, 0.2])
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grads1 = constant_op.constant([0.01, 0.02])
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opt = proximal_gradient_descent.ProximalGradientDescentOptimizer(
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3.0, l1_regularization_strength=0.0, l2_regularization_strength=0.0)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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v0_val, v1_val = self.evaluate([var0, var1])
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self.assertAllClose([0.0, 0.0], v0_val)
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self.assertAllClose([0.0, 0.0], v1_val)
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# Run 3 steps Proximal Gradient Descent.
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for _ in range(3):
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update.run()
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v0_val, v1_val = self.evaluate([var0, var1])
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self.assertAllClose(np.array([-0.9, -1.8]), v0_val)
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self.assertAllClose(np.array([-0.09, -0.18]), v1_val)
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def testProximalGradientDescentwithoutRegularization(self):
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self.doTestProximalGradientDescentwithoutRegularization(use_resource=False)
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def testResourceProximalGradientDescentwithoutRegularization(self):
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self.doTestProximalGradientDescentwithoutRegularization(use_resource=True)
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def testProximalGradientDescentwithoutRegularization2(self):
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with ops.Graph().as_default(), self.cached_session():
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var0 = variables.Variable([1.0, 2.0])
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var1 = variables.Variable([4.0, 3.0])
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grads0 = constant_op.constant([0.1, 0.2])
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grads1 = constant_op.constant([0.01, 0.02])
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opt = proximal_gradient_descent.ProximalGradientDescentOptimizer(
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3.0, l1_regularization_strength=0.0, l2_regularization_strength=0.0)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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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([4.0, 3.0], v1_val)
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# Run 3 steps Proximal Gradient Descent
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for _ in range(3):
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update.run()
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v0_val, v1_val = self.evaluate([var0, var1])
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self.assertAllClose(np.array([0.1, 0.2]), v0_val)
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self.assertAllClose(np.array([3.91, 2.82]), v1_val)
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def testMinimizeSparseResourceVariable(self):
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for dtype in [dtypes.float32, dtypes.float64]:
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with ops.Graph().as_default(), self.cached_session():
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var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
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x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
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pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
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loss = pred * pred
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sgd_op = proximal_gradient_descent.ProximalGradientDescentOptimizer(
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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]], 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([[-111, -138]],
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self.evaluate(var0),
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atol=0.01)
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def testProximalGradientDescentWithL1_L2(self):
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with ops.Graph().as_default(), self.cached_session():
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var0 = variables.Variable([1.0, 2.0])
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var1 = variables.Variable([4.0, 3.0])
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grads0 = constant_op.constant([0.1, 0.2])
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grads1 = constant_op.constant([0.01, 0.02])
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opt = proximal_gradient_descent.ProximalGradientDescentOptimizer(
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3.0, l1_regularization_strength=0.001, l2_regularization_strength=2.0)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
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self.evaluate(variables.global_variables_initializer())
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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([4.0, 3.0], v1_val)
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# Run 10 steps Proximal Gradient Descent
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for _ in range(10):
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update.run()
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v0_val, v1_val = self.evaluate([var0, var1])
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self.assertAllClose(np.array([-0.0495, -0.0995]), v0_val)
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self.assertAllClose(np.array([-0.0045, -0.0095]), v1_val)
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def applyOptimizer(self, opt, steps=5, is_sparse=False):
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if is_sparse:
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var0 = variables.Variable([[1.0], [2.0]])
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var1 = variables.Variable([[3.0], [4.0]])
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grads0 = ops.IndexedSlices(
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constant_op.constant(
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[0.1], shape=[1, 1]),
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constant_op.constant([0]),
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constant_op.constant([2, 1]))
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grads1 = ops.IndexedSlices(
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constant_op.constant(
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[0.02], shape=[1, 1]),
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constant_op.constant([1]),
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constant_op.constant([2, 1]))
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else:
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var0 = variables.Variable([1.0, 2.0])
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var1 = variables.Variable([3.0, 4.0])
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grads0 = constant_op.constant([0.1, 0.2])
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grads1 = constant_op.constant([0.01, 0.02])
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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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v0_val, v1_val = self.evaluate([var0, var1])
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if is_sparse:
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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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else:
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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 ProximalAdagrad for a few steps
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for _ in range(steps):
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update.run()
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v0_val, v1_val = self.evaluate([var0, var1])
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return v0_val, v1_val
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def testEquivSparseGradientDescentwithoutRegularization(self):
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with ops.Graph().as_default(), self.cached_session():
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val0, val1 = self.applyOptimizer(
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proximal_gradient_descent.ProximalGradientDescentOptimizer(
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3.0,
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l1_regularization_strength=0.0,
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l2_regularization_strength=0.0),
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is_sparse=True)
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val2, val3 = self.applyOptimizer(
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gradient_descent.GradientDescentOptimizer(3.0), is_sparse=True)
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self.assertAllClose(val0, val2)
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self.assertAllClose(val1, val3)
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def testEquivGradientDescentwithoutRegularization(self):
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with ops.Graph().as_default(), self.cached_session():
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val0, val1 = self.applyOptimizer(
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proximal_gradient_descent.ProximalGradientDescentOptimizer(
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3.0,
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l1_regularization_strength=0.0,
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l2_regularization_strength=0.0))
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val2, val3 = self.applyOptimizer(
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gradient_descent.GradientDescentOptimizer(3.0))
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self.assertAllClose(val0, val2)
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self.assertAllClose(val1, val3)
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if __name__ == "__main__":
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test.main()
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