You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
76 lines
2.9 KiB
76 lines
2.9 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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.
|
|
# ============================================================================
|
|
""" test lazy adam """
|
|
import numpy as np
|
|
from mindspore.nn.optim import LazyAdam, FTRL, Adam, ProximalAdagrad
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, Parameter, context
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(enable_sparse=True)
|
|
|
|
|
|
class NetWithSparseGatherV2(nn.Cell):
|
|
""" NetWithSparseGatherV2 definition """
|
|
def __init__(self):
|
|
super(NetWithSparseGatherV2, self).__init__()
|
|
self.weight1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="weight1")
|
|
self.weight2 = Parameter(Tensor(np.ones([2, 1, 2]).astype((np.float32))), name="weight2")
|
|
self.axis = 0
|
|
self.gather = P.SparseGatherV2()
|
|
|
|
def construct(self, indices, label):
|
|
return self.gather(self.weight1, indices, self.axis) + self.weight2
|
|
|
|
|
|
def test_ftrl_target():
|
|
""" test_ftrl_target """
|
|
net = NetWithSparseGatherV2()
|
|
net.set_train()
|
|
|
|
optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
|
|
if optimizer.target not in ('CPU', 'Ascend'):
|
|
raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
|
|
|
|
|
|
def test_lazyadam_target():
|
|
""" test_lazyadam_target """
|
|
net = NetWithSparseGatherV2()
|
|
net.set_train()
|
|
|
|
optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
|
|
if optimizer.target not in ('CPU', 'Ascend'):
|
|
raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
|
|
|
|
|
|
def test_adam_target():
|
|
""" test_adam_target """
|
|
net = NetWithSparseGatherV2()
|
|
net.set_train()
|
|
|
|
optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
|
|
if optimizer.target not in ('CPU', 'Ascend'):
|
|
raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
|
|
|
|
|
|
def test_proximal_target():
|
|
""" test_proximal_target """
|
|
net = NetWithSparseGatherV2()
|
|
net.set_train()
|
|
|
|
optimizer = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
|
|
if optimizer.target not in ('CPU', 'Ascend'):
|
|
raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
|