/
training_ops_test.py
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/
training_ops_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.
# ==============================================================================
"""Tests for tensorflow.learning.training_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework.test_util import TensorFlowTestCase
# Import resource_variable_ops for the variables-to-tensor implicit conversion.
from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
from tensorflow.python.training import training_ops
class TrainingOpsTest(TensorFlowTestCase):
def _toType(self, dtype):
if dtype == np.float16:
return dtypes.float16
elif dtype == np.float32:
return dtypes.float32
elif dtype == np.float64:
return dtypes.float64
elif dtype == np.int32:
return dtypes.int32
elif dtype == np.int64:
return dtypes.int64
else:
assert False, (dtype)
def _testTypes(self, x, alpha, delta, use_gpu=None):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var = variables.Variable(x)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(x, var.eval())
apply_sgd = training_ops.apply_gradient_descent(var, alpha, delta)
out = apply_sgd.eval()
self.assertShapeEqual(out, apply_sgd)
self.assertAllCloseAccordingToType(x - alpha * delta, out)
def testApplyGradientDescent(self):
for (dtype, use_gpu) in itertools.product(
[np.float16, np.float32, np.float64], [False, True]):
x = np.arange(100).astype(dtype)
alpha = np.array(2.0).astype(dtype)
delta = np.arange(100).astype(dtype)
self._testTypes(x, alpha, delta, use_gpu)
def _testTypesForAdagrad(self, x, y, lr, grad, use_gpu=None):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var = variables.Variable(x)
accum = variables.Variable(y)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(x, var.eval())
apply_adagrad = training_ops.apply_adagrad(var, accum, lr, grad)
out = apply_adagrad.eval()
self.assertShapeEqual(out, apply_adagrad)
self.assertAllCloseAccordingToType(x - lr * grad * (y + grad * grad)**
(-0.5), out)
self.assertAllCloseAccordingToType(y + grad * grad, accum.eval())
def _testTypesForFtrl(self,
x,
y,
z,
lr,
grad,
use_gpu=None,
l1=0.0,
l2=0.0,
lr_power=-0.5):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var = variables.Variable(x)
accum = variables.Variable(y)
linear = variables.Variable(z)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(x, var.eval())
apply_ftrl = training_ops.apply_ftrl(var, accum, linear, grad, lr, l1, l2,
lr_power)
out = apply_ftrl.eval()
self.assertShapeEqual(out, apply_ftrl)
accum_update = y + grad * grad
linear_update = z + grad - (accum_update**(-lr_power) - y**
(-lr_power)) / lr * x
quadratic = 1.0 / (accum_update**(lr_power) * lr) + 2 * l2
expected_out = np.array([(
np.sign(linear_update[i]) * l1 - linear_update[i]) / (quadratic[i]) if
np.abs(linear_update[i]) > l1 else 0.0
for i in range(linear_update.size)])
self.assertAllCloseAccordingToType(accum_update, accum.eval())
if x.dtype == np.float16:
# The calculations here really are not very precise in float16.
self.assertAllClose(linear_update, linear.eval(), rtol=2e-2, atol=2e-2)
self.assertAllClose(expected_out, out, rtol=2e-2, atol=2e-2)
else:
self.assertAllClose(linear_update, linear.eval())
self.assertAllClose(expected_out, out)
def testApplyAdagrad(self):
for (dtype, use_gpu) in itertools.product(
[np.float16, np.float32, np.float64], [False, True]):
x = np.arange(100).astype(dtype)
y = np.arange(1, 101).astype(dtype)
lr = np.array(2.0).astype(dtype)
grad = np.arange(100).astype(dtype)
self._testTypesForAdagrad(x, y, lr, grad, use_gpu)
def testApplyFtrl(self):
for dtype in [np.float16, np.float32, np.float64]:
x = np.arange(100).astype(dtype)
y = np.arange(1, 101).astype(dtype)
z = np.arange(102, 202).astype(dtype)
lr = np.array(2.0).astype(dtype)
l1 = np.array(3.0).astype(dtype)
l2 = np.array(4.0).astype(dtype)
grad = np.arange(100).astype(dtype)
self._testTypesForFtrl(x, y, z, lr, grad, use_gpu=False, l1=l1, l2=l2)
def _testTypesForSparseAdagrad(self, x, y, lr, grad, indices):
self.setUp()
with self.test_session(use_gpu=False):
var = variables.Variable(x)
accum = variables.Variable(y)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(x, var.eval())
sparse_apply_adagrad = training_ops.sparse_apply_adagrad(
var, accum, lr, grad,
constant_op.constant(indices, self._toType(indices.dtype)))
out = sparse_apply_adagrad.eval()
self.assertShapeEqual(out, sparse_apply_adagrad)
for (i, index) in enumerate(indices):
self.assertAllCloseAccordingToType(
x[index] - lr * grad[i] * (y[index] + grad[i] * grad[i])**(-0.5),
var.eval()[index])
self.assertAllCloseAccordingToType(y[index] + grad[i] * grad[i],
accum.eval()[index])
def _testTypesForSparseFtrl(self,
x,
y,
z,
lr,
grad,
indices,
l1=0.0,
l2=0.0,
lr_power=-0.5):
self.setUp()
with self.test_session(use_gpu=False):
var = variables.Variable(x)
accum = variables.Variable(y)
linear = variables.Variable(z)
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(x, var.eval())
sparse_apply_ftrl = training_ops.sparse_apply_ftrl(
var,
accum,
linear,
grad,
constant_op.constant(indices, self._toType(indices.dtype)),
lr,
l1,
l2,
lr_power=lr_power)
out = sparse_apply_ftrl.eval()
self.assertShapeEqual(out, sparse_apply_ftrl)
for (i, index) in enumerate(indices):
self.assertAllCloseAccordingToType(x[index] - lr * grad[i] *
(y[index] + grad[i] * grad[i])**
(lr_power), var.eval()[index])
self.assertAllCloseAccordingToType(y[index] + grad[i] * grad[i],
accum.eval()[index])
def testSparseApplyAdagrad(self):
for (dtype, index_type) in itertools.product(
[np.float16, np.float32, np.float64], [np.int32, np.int64]):
x_val = [np.arange(10), np.arange(10, 20), np.arange(20, 30)]
y_val = [np.arange(1, 11), np.arange(11, 21), np.arange(21, 31)]
x = np.array(x_val).astype(dtype)
y = np.array(y_val).astype(dtype)
lr = np.array(2.0).astype(dtype)
grad_val = [np.arange(10), np.arange(10)]
grad = np.array(grad_val).astype(dtype)
indices = np.array([0, 2]).astype(index_type)
self._testTypesForSparseAdagrad(x, y, lr, grad, indices)
def testSparseApplyAdagradDim1(self):
for (dtype, index_type) in itertools.product(
[np.float16, np.float32, np.float64], [np.int32, np.int64]):
x_val = [[1.0], [2.0], [3.0]]
y_val = [[4.0], [5.0], [6.0]]
x = np.array(x_val).astype(dtype)
y = np.array(y_val).astype(dtype)
lr = np.array(2.0).astype(dtype)
grad_val = [[1.5], [2.5]]
grad = np.array(grad_val).astype(dtype)
indices = np.array([0, 2]).astype(index_type)
self._testTypesForSparseAdagrad(x, y, lr, grad, indices)
def testSparseApplyFtrlDim1(self):
for (dtype, index_type) in itertools.product(
[np.float16, np.float32, np.float64], [np.int32, np.int64]):
x_val = [[0.0], [0.0], [0.0]]
y_val = [[4.0], [5.0], [6.0]]
z_val = [[0.0], [0.0], [0.0]]
x = np.array(x_val).astype(dtype)
y = np.array(y_val).astype(dtype)
z = np.array(z_val).astype(dtype)
lr = np.array(2.0).astype(dtype)
grad_val = [[1.5], [2.5]]
grad = np.array(grad_val).astype(dtype)
indices = np.array([0, 2]).astype(index_type)
self._testTypesForSparseFtrl(x, y, z, lr, grad, indices)
def testApplyAdam(self):
for dtype, use_gpu in itertools.product(
[np.float16, np.float32, np.float64], [False, True]):
var = np.arange(100).astype(dtype)
m = np.arange(1, 101).astype(dtype)
v = np.arange(101, 201).astype(dtype)
grad = np.arange(100).astype(dtype)
self._testTypesForAdam(var, m, v, grad, use_gpu)
def _testTypesForAdam(self, var, m, v, grad, use_gpu):
self.setUp()
with self.test_session(use_gpu=use_gpu):
var_t = variables.Variable(var)
m_t = variables.Variable(m)
v_t = variables.Variable(v)
t = 1
beta1 = np.array(0.9, dtype=var.dtype)
beta2 = np.array(0.999, dtype=var.dtype)
beta1_power = beta1**t
beta2_power = beta2**t
lr = np.array(0.001, dtype=var.dtype)
epsilon = np.array(1e-8, dtype=var.dtype)
beta1_t = constant_op.constant(beta1, self._toType(var.dtype), [])
beta2_t = constant_op.constant(beta2, self._toType(var.dtype), [])
beta1_power_t = variables.Variable(beta1_power)
beta2_power_t = variables.Variable(beta2_power)
lr_t = constant_op.constant(lr, self._toType(var.dtype), [])
epsilon_t = constant_op.constant(epsilon, self._toType(var.dtype), [])
variables.global_variables_initializer().run()
self.assertAllCloseAccordingToType(var, var_t.eval())
new_var, _, _ = self._adamUpdateNumpy(var, grad, t, m, v, lr, beta1,
beta2, epsilon)
apply_adam = training_ops.apply_adam(var_t, m_t, v_t, beta1_power_t,
beta2_power_t, lr_t, beta1_t,
beta2_t, epsilon_t, grad)
out = apply_adam.eval()
self.assertShapeEqual(out, apply_adam)
self.assertAllCloseAccordingToType(new_var, out)
def _adamUpdateNumpy(self, param, g_t, t, m, v, alpha, beta1, beta2, epsilon):
alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
m_t = beta1 * m + (1 - beta1) * g_t
v_t = beta2 * v + (1 - beta2) * g_t * g_t
param_t = param - alpha_t * m_t / (np.sqrt(v_t) + epsilon)
return param_t, m_t, v_t
if __name__ == '__main__':
googletest.main()