wehub-resource-sync
144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import paddle
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from paddle import Model
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from paddle.metric import Accuracy
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from paddle.nn.layer.loss import CrossEntropyLoss
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from paddle.static import InputSpec
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from paddle.vision.datasets import MNIST
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from paddle.vision.models import LeNet
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class MnistDataset(MNIST):
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def __init__(self, mode, return_label=True, sample_num=None):
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super().__init__(mode=mode)
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self.return_label = return_label
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if sample_num:
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self.images = self.images[:sample_num]
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self.labels = self.labels[:sample_num]
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def __getitem__(self, idx):
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img, label = self.images[idx], self.labels[idx]
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img = np.reshape(img, [1, 28, 28])
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if self.return_label:
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return img, np.array(self.labels[idx]).astype('int64')
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return (img,)
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def __len__(self):
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return len(self.images)
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class TestCallbacks(unittest.TestCase):
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def setUp(self):
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self.save_dir = tempfile.mkdtemp()
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def tearDown(self):
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shutil.rmtree(self.save_dir)
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def test_earlystopping(self):
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paddle.seed(2020)
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for dynamic in [True, False]:
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paddle.enable_static() if not dynamic else None
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device = paddle.set_device('cpu')
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sample_num = 100
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train_dataset = MnistDataset(mode='train', sample_num=sample_num)
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val_dataset = MnistDataset(mode='test', sample_num=sample_num)
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net = LeNet()
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optim = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=net.parameters()
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)
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inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
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labels = [InputSpec([None, 1], 'int64', 'label')]
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model = Model(net, inputs=inputs, labels=labels)
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model.prepare(
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optim,
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loss=CrossEntropyLoss(reduction="sum"),
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metrics=[Accuracy()],
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)
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callbacks_0 = paddle.callbacks.EarlyStopping(
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'loss',
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mode='min',
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patience=1,
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verbose=1,
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min_delta=0,
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baseline=None,
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save_best_model=True,
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)
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callbacks_1 = paddle.callbacks.EarlyStopping(
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'acc',
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mode='auto',
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patience=1,
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verbose=1,
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min_delta=0,
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baseline=0,
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save_best_model=True,
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)
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callbacks_2 = paddle.callbacks.EarlyStopping(
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'loss',
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mode='auto_',
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patience=1,
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verbose=1,
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min_delta=0,
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baseline=None,
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save_best_model=True,
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)
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callbacks_3 = paddle.callbacks.EarlyStopping(
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'acc_',
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mode='max',
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patience=1,
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verbose=1,
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min_delta=0,
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baseline=0,
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save_best_model=True,
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)
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model.fit(
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train_dataset,
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val_dataset,
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batch_size=64,
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save_freq=10,
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save_dir=self.save_dir,
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epochs=10,
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verbose=0,
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callbacks=[
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callbacks_0,
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callbacks_1,
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callbacks_2,
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callbacks_3,
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],
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)
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# Test for no val_loader
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model.fit(
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train_dataset,
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batch_size=64,
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save_freq=10,
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save_dir=self.save_dir,
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epochs=10,
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verbose=0,
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callbacks=[callbacks_0],
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)
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if __name__ == '__main__':
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unittest.main()
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