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optimizer_test.py
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optimizer_test.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functional test for optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import gradient_descent
class OptimizerTest(test.TestCase):
def testBasic(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
cost = 5 * var0 + 3 * var1
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name='global_step')
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
opt_op = sgd_op.minimize(cost, global_step, [var0, var1])
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 1 step of sgd through optimizer
opt_op.run()
# Validate updated params
self.assertAllClose([-14., -13.], var0.eval())
self.assertAllClose([-6., -5.], var1.eval())
def testAggregationMethod(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
cost = 5 * var0 + 3 * var1
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name='global_step')
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
opt_op = sgd_op.minimize(
cost,
global_step, [var0, var1],
aggregation_method=gradients_impl.AggregationMethod.
EXPERIMENTAL_ACCUMULATE_N)
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 1 step of sgd through optimizer
opt_op.run()
# Validate updated params
self.assertAllClose([-14., -13.], var0.eval())
self.assertAllClose([-6., -5.], var1.eval())
def testPrecomputedGradient(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
cost = 5 * var0 + 3 * var1
grad_loss = constant_op.constant([42, -42], dtype=dtype)
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name='global_step')
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
opt_op = sgd_op.minimize(
cost, global_step, [var0, var1], grad_loss=grad_loss)
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 1 step of sgd through optimizer
opt_op.run()
# Validate updated params
self.assertAllClose([1.0 - 3 * 5 * 42.0, 2.0 - 3 * 5 * (-42.0)],
var0.eval())
self.assertAllClose([3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)],
var1.eval())
def testNoVariables(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtype, trainable=False)
var1 = variables.Variable([3.0, 4.0], dtype=dtype, trainable=False)
cost = 5 * var0 + var1
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
with self.assertRaisesRegexp(ValueError, 'No variables'):
sgd_op.minimize(cost)
def testNoGradients(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
cost = 5 * var0
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name='global_step')
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
with self.assertRaisesRegexp(ValueError, 'No gradients'):
# var1 has no gradient
sgd_op.minimize(cost, global_step, [var1])
def testNoGradientsForAnyVariables_Minimize(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
cost = constant_op.constant(5.0)
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name='global_step')
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
with self.assertRaisesRegexp(ValueError,
'No gradients provided for any variable'):
sgd_op.minimize(cost, global_step, [var0, var1])
def testNoGradientsForAnyVariables_ApplyGradients(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
with self.assertRaisesRegexp(ValueError,
'No gradients provided for any variable'):
sgd_op.apply_gradients([(None, var0), (None, var1)])
def testGradientsAsVariables(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.test_session() as sess:
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
cost = 5 * var0 + 3 * var1
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name='global_step')
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
grads_and_vars = sgd_op.compute_gradients(cost, [var0, var1])
# Convert gradients to tf.Variables
converted_grads = [
variables.Variable(array_ops.zeros([2], dtype))
for i in grads_and_vars
]
convert_ops = [
state_ops.assign(converted_grads[i], gv[0])
for i, gv in enumerate(grads_and_vars)
]
converted_grads_and_vars = list(zip(converted_grads, [var0, var1]))
opt_op = sgd_op.apply_gradients(converted_grads_and_vars, global_step)
variables.global_variables_initializer().run()
# Run convert_ops to achieve the gradietns converting
sess.run(convert_ops)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 1 step of sgd through optimizer
opt_op.run()
# Validate updated params
self.assertAllClose([-14., -13.], var0.eval())
self.assertAllClose([-6., -5.], var1.eval())
def testTrainOp(self):
with self.test_session():
var0 = variables.Variable([1.0, 2.0])
var1 = variables.Variable([3.0, 4.0])
cost = 5 * var0 + 3 * var1
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name='global_step')
sgd_op = gradient_descent.GradientDescentOptimizer(3.0)
opt_op = sgd_op.minimize(cost, global_step, [var0, var1])
self.assertTrue(opt_op in ops.get_collection(ops.GraphKeys.TRAIN_OP))
if __name__ == '__main__':
test.main()