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- # 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 lenet"""
- import numpy as np
-
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.common.api import _executor
- from mindspore.ops import operations as P
- from ....train_step_wrap import train_step_with_loss_warp, train_step_with_sens
-
- context.set_context(mode=context.GRAPH_MODE)
-
-
- class LeNet5(nn.Cell):
- """LeNet5 definition"""
-
- def __init__(self):
- super(LeNet5, self).__init__()
- self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
- self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
- self.fc1 = nn.Dense(16 * 5 * 5, 120)
- self.fc2 = nn.Dense(120, 84)
- self.fc3 = nn.Dense(84, 10)
- self.relu = nn.ReLU()
- self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
- self.flatten = P.Flatten()
-
- def construct(self, x):
- x = self.max_pool2d(self.relu(self.conv1(x)))
- x = self.max_pool2d(self.relu(self.conv2(x)))
- x = self.flatten(x)
- x = self.relu(self.fc1(x))
- x = self.relu(self.fc2(x))
- x = self.fc3(x)
- return x
-
-
- def test_lenet5_train_step():
- predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
- label = Tensor(np.zeros([1, 10]).astype(np.float32))
- net = train_step_with_loss_warp(LeNet5())
- _executor.compile(net, predict, label)
-
-
- def test_lenet5_train_sens():
- predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
- sens = Tensor(np.ones([1, 10]).astype(np.float32))
- net = train_step_with_sens(LeNet5(), sens)
- _executor.compile(net, predict)
-
-
- def test_lenet5_train_step_training():
- predict = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
- label = Tensor(np.zeros([1, 10]).astype(np.float32))
- net = train_step_with_loss_warp(LeNet5())
- net.set_train()
- _executor.compile(net, predict, label)
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