tensorflow/python/training/rmsprop_test.py
# 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 rmsprop."""
import copy
import itertools
import math
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import indexed_slices
from tensorflow.python.framework import test_util
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import rmsprop
_DATA_TYPES = [dtypes.half, dtypes.float32]
_TEST_PARAM_VALUES = [
# learning_rate, decay, momentum, epsilon, centered, use_resource
[0.5, 0.9, 0.0, 1e-3, True, False],
[0.5, 0.9, 0.0, 1e-3, False, False],
[0.5, 0.9, 0.0, 1e-3, True, True],
[0.5, 0.9, 0.0, 1e-3, False, True],
[0.1, 0.9, 0.0, 1e-3, True, False],
[0.5, 0.95, 0.0, 1e-3, False, False],
[0.5, 0.95, 0.0, 1e-5, True, False],
[0.5, 0.95, 0.9, 1e-5, True, False],
]
_TESTPARAMS = [
[data_type] + values
for data_type, values in itertools.product(_DATA_TYPES, _TEST_PARAM_VALUES)
]
class RMSPropOptimizerTest(test.TestCase):
def _rmsprop_update_numpy(self, var, g, mg, rms, mom, lr, decay, momentum,
epsilon, centered):
rms_t = rms * decay + (1 - decay) * g * g
denom_t = rms_t + epsilon
if centered:
mg_t = mg * decay + (1 - decay) * g
denom_t -= mg_t * mg_t
else:
mg_t = mg
mom_t = momentum * mom + lr * g / np.sqrt(denom_t, dtype=denom_t.dtype)
var_t = var - mom_t
return var_t, mg_t, rms_t, mom_t
def _sparse_rmsprop_update_numpy(self, var, gindexs, gvalues, mg, rms, mom,
lr, decay, momentum, epsilon, centered):
mg_t = copy.deepcopy(mg)
rms_t = copy.deepcopy(rms)
mom_t = copy.deepcopy(mom)
var_t = copy.deepcopy(var)
for i in range(len(gindexs)):
gindex = gindexs[i]
gvalue = gvalues[i]
rms_t[gindex] = rms[gindex] * decay + (1 - decay) * gvalue * gvalue
denom_t = rms_t[gindex] + epsilon
if centered:
mg_t[gindex] = mg_t[gindex] * decay + (1 - decay) * gvalue
denom_t -= mg_t[gindex] * mg_t[gindex]
mom_t[gindex] = momentum * mom[gindex] + lr * gvalue / np.sqrt(denom_t)
var_t[gindex] = var[gindex] - mom_t[gindex]
return var_t, mg_t, rms_t, mom_t
@test_util.run_deprecated_v1
def testDense(self):
# TODO(yori): Use ParameterizedTest when available
for (dtype, learning_rate, decay, momentum,
epsilon, centered, use_resource) in _TESTPARAMS:
with test_util.use_gpu():
# Initialize variables for numpy implementation.
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype)
if use_resource:
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
else:
var0 = variables.Variable(var0_np)
var1 = variables.Variable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
opt = rmsprop.RMSPropOptimizer(
learning_rate=learning_rate,
decay=decay,
momentum=momentum,
epsilon=epsilon,
centered=centered)
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
mg0 = opt.get_slot(var0, "mg")
self.assertEqual(mg0 is not None, centered)
mg1 = opt.get_slot(var1, "mg")
self.assertEqual(mg1 is not None, centered)
rms0 = opt.get_slot(var0, "rms")
self.assertTrue(rms0 is not None)
rms1 = opt.get_slot(var1, "rms")
self.assertTrue(rms1 is not None)
mom0 = opt.get_slot(var0, "momentum")
self.assertTrue(mom0 is not None)
mom1 = opt.get_slot(var1, "momentum")
self.assertTrue(mom1 is not None)
mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Run 4 steps of RMSProp
for _ in range(1, 5):
self.evaluate(update)
var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy(
var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate,
decay, momentum, epsilon, centered)
var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy(
var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate,
decay, momentum, epsilon, centered)
# Validate updated params
if centered:
self.assertAllCloseAccordingToType(mg0_np, self.evaluate(mg0))
self.assertAllCloseAccordingToType(mg1_np, self.evaluate(mg1))
self.assertAllCloseAccordingToType(rms0_np, self.evaluate(rms0))
self.assertAllCloseAccordingToType(rms1_np, self.evaluate(rms1))
self.assertAllCloseAccordingToType(mom0_np, self.evaluate(mom0))
self.assertAllCloseAccordingToType(mom1_np, self.evaluate(mom1))
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
@test_util.run_deprecated_v1
def testMinimizeSparseResourceVariable(self):
for dtype in [dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
loss = pred * pred
sgd_op = rmsprop.RMSPropOptimizer(
learning_rate=1.0,
decay=0.0,
momentum=0.0,
epsilon=0.0,
centered=False).minimize(loss)
self.evaluate(variables.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0))
# Run 1 step of sgd
self.evaluate(sgd_op)
# Validate updated params
self.assertAllCloseAccordingToType([[0., 1.]],
self.evaluate(var0),
atol=0.01)
@test_util.run_deprecated_v1
def testMinimizeSparseResourceVariableCentered(self):
for dtype in [dtypes.float32, dtypes.float64]:
with self.cached_session():
var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
loss = pred * pred
sgd_op = rmsprop.RMSPropOptimizer(
learning_rate=1.0,
decay=0.0,
momentum=0.0,
epsilon=1.0,
centered=True).minimize(loss)
self.evaluate(variables.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0))
# Run 1 step of sgd
self.evaluate(sgd_op)
# Validate updated params
self.assertAllCloseAccordingToType([[-111, -138]],
self.evaluate(var0),
atol=0.01)
@test_util.run_deprecated_v1
def testSparse(self):
# TODO(yori): Use ParameterizedTest when available
for (dtype, learning_rate, decay,
momentum, epsilon, centered, _) in _TESTPARAMS:
with test_util.use_gpu():
# Initialize variables for numpy implementation.
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01], dtype=dtype.as_numpy_dtype)
var0 = variables.Variable(var0_np)
var1 = variables.Variable(var1_np)
grads0_np_indices = np.array([0], dtype=np.int32)
grads0 = indexed_slices.IndexedSlices(
constant_op.constant(grads0_np),
constant_op.constant(grads0_np_indices), constant_op.constant([1]))
grads1_np_indices = np.array([1], dtype=np.int32)
grads1 = indexed_slices.IndexedSlices(
constant_op.constant(grads1_np),
constant_op.constant(grads1_np_indices), constant_op.constant([1]))
opt = rmsprop.RMSPropOptimizer(
learning_rate=learning_rate,
decay=decay,
momentum=momentum,
epsilon=epsilon,
centered=centered)
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
mg0 = opt.get_slot(var0, "mg")
self.assertEqual(mg0 is not None, centered)
mg1 = opt.get_slot(var1, "mg")
self.assertEqual(mg1 is not None, centered)
rms0 = opt.get_slot(var0, "rms")
self.assertTrue(rms0 is not None)
rms1 = opt.get_slot(var1, "rms")
self.assertTrue(rms1 is not None)
mom0 = opt.get_slot(var0, "momentum")
self.assertTrue(mom0 is not None)
mom1 = opt.get_slot(var1, "momentum")
self.assertTrue(mom1 is not None)
mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype)
mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Run 4 steps of RMSProp
for _ in range(1, 5):
self.evaluate(update)
var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy(
var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np,
learning_rate, decay, momentum, epsilon, centered)
var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy(
var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np,
learning_rate, decay, momentum, epsilon, centered)
# Validate updated params
if centered:
self.assertAllCloseAccordingToType(mg0_np, self.evaluate(mg0))
self.assertAllCloseAccordingToType(mg1_np, self.evaluate(mg1))
self.assertAllCloseAccordingToType(rms0_np, self.evaluate(rms0))
self.assertAllCloseAccordingToType(rms1_np, self.evaluate(rms1))
self.assertAllCloseAccordingToType(mom0_np, self.evaluate(mom0))
self.assertAllCloseAccordingToType(mom1_np, self.evaluate(mom1))
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
@test_util.run_deprecated_v1
def testWithoutMomentum(self):
for dtype in [dtypes.half, dtypes.float32]:
with test_util.use_gpu():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
opt = rmsprop.RMSPropOptimizer(
learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0)
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
rms0 = opt.get_slot(var0, "rms")
self.assertTrue(rms0 is not None)
rms1 = opt.get_slot(var1, "rms")
self.assertTrue(rms1 is not None)
mom0 = opt.get_slot(var0, "momentum")
self.assertTrue(mom0 is not None)
mom1 = opt.get_slot(var1, "momentum")
self.assertTrue(mom1 is not None)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Step 1: the rms accumulators where 1. So we should see a normal
# update: v -= grad * learning_rate
self.evaluate(update)
# Check the root mean square accumulators.
self.assertAllCloseAccordingToType(
np.array([0.901, 0.901]), self.evaluate(rms0))
self.assertAllCloseAccordingToType(
np.array([0.90001, 0.90001]), self.evaluate(rms1))
# Check the parameters.
self.assertAllCloseAccordingToType(
np.array([
1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)),
2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0))
]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([
3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)),
4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0))
]), self.evaluate(var1))
# Step 2: the root mean square accumulators contain the previous update.
self.evaluate(update)
# Check the rms accumulators.
self.assertAllCloseAccordingToType(
np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]),
self.evaluate(rms0))
self.assertAllCloseAccordingToType(
np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]),
self.evaluate(rms1))
# Check the parameters.
self.assertAllCloseAccordingToType(
np.array([
1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) -
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)),
2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) -
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0))
]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([
3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) -
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)),
4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) -
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0))
]), self.evaluate(var1))
@test_util.run_deprecated_v1
def testWithMomentum(self):
for dtype in [dtypes.half, dtypes.float32]:
with test_util.use_gpu():
var0 = variables.Variable([1.0, 2.0], dtype=dtype)
var1 = variables.Variable([3.0, 4.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
opt = rmsprop.RMSPropOptimizer(
learning_rate=2.0, decay=0.9, momentum=0.5, epsilon=1e-5)
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
rms0 = opt.get_slot(var0, "rms")
self.assertTrue(rms0 is not None)
rms1 = opt.get_slot(var1, "rms")
self.assertTrue(rms1 is not None)
mom0 = opt.get_slot(var0, "momentum")
self.assertTrue(mom0 is not None)
mom1 = opt.get_slot(var1, "momentum")
self.assertTrue(mom1 is not None)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Step 1: rms = 1, mom = 0. So we should see a normal
# update: v -= grad * learning_rate
self.evaluate(update)
# Check the root mean square accumulators.
self.assertAllCloseAccordingToType(
np.array([0.901, 0.901]), self.evaluate(rms0))
self.assertAllCloseAccordingToType(
np.array([0.90001, 0.90001]), self.evaluate(rms1))
# Check the momentum accumulators
self.assertAllCloseAccordingToType(
np.array([(0.1 * 2.0 / math.sqrt(0.901 + 1e-5)),
(0.1 * 2.0 / math.sqrt(0.901 + 1e-5))]),
self.evaluate(mom0))
self.assertAllCloseAccordingToType(
np.array([(0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)),
(0.01 * 2.0 / math.sqrt(0.90001 + 1e-5))]),
self.evaluate(mom1))
# Check that the parameters.
self.assertAllCloseAccordingToType(
np.array([
1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)),
2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5))
]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([
3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)),
4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5))
]), self.evaluate(var1))
# Step 2: the root mean square accumulators contain the previous update.
self.evaluate(update)
# Check the rms accumulators.
self.assertAllCloseAccordingToType(
np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]),
self.evaluate(rms0))
self.assertAllCloseAccordingToType(
np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]),
self.evaluate(rms1))
self.assertAllCloseAccordingToType(
np.array([
0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) +
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)),
0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) +
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))
]), self.evaluate(mom0))
self.assertAllCloseAccordingToType(
np.array([
0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) +
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)),
0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) +
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))
]), self.evaluate(mom1))
# Check the parameters.
self.assertAllCloseAccordingToType(
np.array([
1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) -
(0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) +
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))),
2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) -
(0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) +
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)))
]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([
3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) -
(0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) +
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))),
4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) -
(0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) +
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)))
]), self.evaluate(var1))
def testCallableParams(self):
with context.eager_mode():
for dtype in [dtypes.half, dtypes.float32]:
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
learning_rate = lambda: 2.0
decay = lambda: 0.9
momentum = lambda: 0.0
epsilon = lambda: 1.0
opt = rmsprop.RMSPropOptimizer(learning_rate, decay, momentum, epsilon)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Step 1: the rms accumulators where 1. So we should see a normal
# update: v -= grad * learning_rate
opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
# Check the parameters.
self.assertAllCloseAccordingToType(
np.array([
1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)),
2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0))
]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([
3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)),
4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0))
]), self.evaluate(var1))
# Step 2: the root mean square accumulators contain the previous update.
opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
# Check the parameters.
self.assertAllCloseAccordingToType(
np.array([
1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) -
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)),
2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) -
(0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0))
]), self.evaluate(var0))
self.assertAllCloseAccordingToType(
np.array([
3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) -
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)),
4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) -
(0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0))
]), self.evaluate(var1))
if __name__ == "__main__":
test.main()