mirror of https://github.com/alibaba/MNN.git
95 lines
2.7 KiB
Python
95 lines
2.7 KiB
Python
import MNN
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import MNN.var as var
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c_train = MNN.c_train
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nn = c_train.cnn
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F = MNN.expr
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data = c_train.data
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import time
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class Net(MNN.train.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.conv(1, 20, [5, 5])
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self.conv2 = nn.conv(20, 50, [5, 5])
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self.fc1 = nn.linear(800, 500)
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self.fc2 = nn.linear(500, 10)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.max_pool(x, [2, 2], [2, 2])
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x = F.relu(self.conv2(x))
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x = F.max_pool(x, [2, 2], [2, 2])
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x = F.convert(x, F.NCHW)
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x = F.reshape(x, [0, -1])
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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x = F.softmax(x, 1)
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return x
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def testFunc(loader, net):
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loader.reset()
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net.train(False)
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iter_number = loader.iter_number()
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correct = 0
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for i in range(0, iter_number):
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example = loader.next()[0]
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data = example[0][0]
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label = example[1][0]
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data = F.cast(data, F.float) * var.float(1.0/255.0)
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predict = net(data)
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predict = F.argmax(predict, 1)
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accu = F.reduce_sum(F.equal(predict, F.cast(label, F.int)), [], False)
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correct += accu.read()[0]
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print("Accu: ", correct * 100.0 / loader.size(), "%")
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def trainFunc(loader, net, opt):
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loader.reset()
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net.train()
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t0 = time.time()
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iter_number = loader.iter_number()
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for i in range(0, iter_number):
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example = loader.next()[0]
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data = example[0][0]
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label = example[1][0]
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data = F.cast(data, F.float) * var.float(1.0/255.0)
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predict = net(data)
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target = F.one_hot(F.cast(label, F.int), var.int(10), var.float(1.0), var.float(0.0))
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loss = c_train.loss.CrossEntropy(predict, target)
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opt.step(loss)
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if i % 100 == 0:
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print(loss.read())
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t1 = time.time()
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cost = t1 - t0
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print("Epoch cost: %.3f" %cost)
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F.save(net.parameters(), "cache/temp.snapshot")
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net = Net()
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opt = c_train.SGD(0.01, 0.9)
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net.loadParameters(F.load("cache/temp.snapshot"))
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opt.append(net.parameters())
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import sys
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mnistDataPath = sys.argv[1]
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mnistDataset = data.mnist.create(mnistDataPath, data.mnist.Train)
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trainLoader = mnistDataset.create_loader(64, True, True, 0)
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testmnistDataset = data.mnist.create(mnistDataPath, data.mnist.Test)
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testLoader = mnistDataset.create_loader(10, True, False, 0)
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F.setThreadNumber(4)
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for epoch in range(0, 10):
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trainFunc(trainLoader, net, opt)
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# Save Model
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fileName = 'cache/%d.mnist.mnn' %epoch
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net.train(False)
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predict = net.forward(F.placeholder([1, 1, 28, 28], F.NC4HW4))
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print("Save to " + fileName)
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F.save([predict], fileName)
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testFunc(testLoader, net)
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