【PaddlePaddle】使用高层API导入、导出模型
qq_50773353:
img = self.test_transforms(img)
return img#, label
traindataset = dataset('./data/train')
testdataset = dataset('./data/test', mode='test')
train_loader = DataLoader(traindataset, places=paddle.CUDAPlace(0), batch_size=batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(testdataset, places=paddle.CUDAPlace(0), batch_size=batch_size, shuffle=False, drop_last=True)
model = paddle.Model(resnet18(pretrained=False))
model.summary((-1, 3, 128, 128))
# 定义优化器
optimizer = paddle.optimizer.Adam(learning_rate=0.001,
#momentum=0.9,
#weight_decay=L2Decay(1e-4),
parameters=model.parameters())
# 进行训练前准备
model.prepare(optimizer, CrossEntropyLoss(), Accuracy())
use_gpu = True
paddle.set_device('gpu:0') if use_gpu else paddle.set_device('cpu')
# 启动训练
model.fit(traindataset,
epochs=50,
batch_size=8,
#save_dir="./output",
#save_freq=10,
verbose=1)
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