def evaluate(net_file, model_file):
""" main
"""
#1, build model
net_path = os.path.dirname(net_file)
if net_path not in sys.path:
sys.path.insert(0, net_path)
from lenet import LeNet as MyNet
#1, define network topology
images = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
net = MyNet({'data': images})
prediction = net.layers['prob']
acc = fluid.layers.accuracy(input=prediction, label=label)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
#2, load weights
if model_file.find('.npy') > 0:
net.load(data_path=model_file, exe=exe, place=place)
else:
net.load(data_path=model_file, exe=exe)
#3, test this model
test_program = fluid.default_main_program().clone()
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
fetch_list = [acc, prediction]
print('go to test model using test set')
acc_val = test_model(exe, test_program, \
fetch_list, test_reader, feeder)
print('test accuracy is [%.4f], expected value[0.919]' % (acc_val))
transforms.Normalize(norm_mean, norm_std),
])
# 构建MyDataset实例
train_data = DogCatDataset(data_dir=train_dir, transform=train_transform)
valid_data = DogCatDataset(data_dir=valid_dir, transform=valid_transform)
# 构建DataLoder
train_loader = DataLoader(dataset=train_data,
batch_size=BATCH_SIZE,
shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)
# ============================ step 2/5 模型 ============================
net = MyNet(classes=2)
net.initialize_weights()
net.to("cuda")
# ============================ step 3/5 损失函数 ============================
criterion = nn.CrossEntropyLoss() # 选择损失函数
# ============================ step 4/5 优化器 ============================
optimizer = optim.Adam(net.parameters(),
lr=LR,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-1) # 选择优化器
# optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=1e-1) # 选择优化器
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10,