def test2():
network = N.Network()
network.debug = True
#network.setInput(RawInput((1, 28,28)))
#network.append(conv.Conv2d(feature_map_multiplier=32))
#network.append(act.Relu())
#network.append(pooling.Pooling())
#network.append(conv.Conv2d(feature_map_multiplier=2))
#network.append(act.Relu())
#network.append(pooling.Pooling())
#network.append(conv.Conv2d(feature_map_multiplier=2))
#network.append(act.Relu())
#network.append(pooling.Pooling())
#network.append(reshape.Flatten())
#network.append(fullconn.FullConn(input_feature=1152, output_feature=1152*2))
#network.append(act.Relu())
#network.append(fullconn.FullConn(input_feature=1152*2, output_feature=10))
#network.append(output.SoftMax())
li = RawInput((1, 28,28))
network.setInput(li)
lc1 = conv.Conv2d(feature_map_multiplier=32)
la1 = act.Relu()
lp1 = pooling.Pooling()
lc2 = conv.Conv2d(feature_map_multiplier=2)
la2 = act.Relu()
lp2 = pooling.Pooling()
lc3 = conv.Conv2d(feature_map_multiplier=2)
la3 = act.Relu()
lp3 = pooling.Pooling()
lf = reshape.Flatten()
lfc1 = fullconn.FullConn(input_feature=1152, output_feature=1152*2)
la4 = act.Relu()
lfc2 = fullconn.FullConn(input_feature=1152*2, output_feature=10)
lsm = output.SoftMax()
network.connect(li, lc1)
network.connect(lc1, la1)
network.connect(la1, lp1)
network.connect(lp1, lc2)
network.connect(lc2, la2)
network.connect(la2, lp2)
network.connect(lp2, lc3)
network.connect(lc3, la3)
network.connect(la3, lp3)
network.connect(lp3, lf)
network.connect(lf, lfc1)
network.connect(lfc1, la4)
network.connect(la4, lfc2)
network.connect(lfc2, lsm)
network.build()
trX, trY, teX, teY = l.load_mnist()
for i in range(5000):
print(i)
network.train(trX, trY)
print(1 - np.mean(np.argmax(teY, axis=1) == np.argmax(network.predict(teX), axis=1)))
def test1():
network = N.Network()
network.debug = True
network.setInput((28, 28))
network.append(
conv.Conv2d(filter=(3, 3), input_feature=1, output_feature=32))
network.append(act.Relu())
network.append(
conv.Conv2d(filter=(3, 3), input_feature=32, output_feature=32))
network.append(act.Relu())
network.append(
conv.Conv2d(filter=(3, 3), input_feature=32, output_feature=32))
network.append(act.Relu())
network.append(pooling.Pooling((2, 2)))
network.append(
conv.Conv2d(filter=(3, 3), input_feature=32, output_feature=64))
network.append(act.Relu())
network.append(
conv.Conv2d(filter=(3, 3), input_feature=64, output_feature=64))
network.append(act.Relu())
network.append(
conv.Conv2d(filter=(3, 3), input_feature=64, output_feature=64))
network.append(act.Relu())
network.append(pooling.Pooling((2, 2)))
network.append(
conv.Conv2d(filter=(3, 3), input_feature=64, output_feature=128))
network.append(act.Relu())
network.append(
conv.Conv2d(filter=(3, 3), input_feature=128, output_feature=128))
network.append(act.Relu())
network.append(
conv.Conv2d(filter=(3, 3), input_feature=128, output_feature=128))
network.append(act.Relu())
network.append(pooling.Pooling((2, 2)))
network.append(reshape.Flatten())
network.append(
fullconn.FullConn(input_feature=1152, output_feature=1152 * 2))
network.append(act.Relu())
network.append(fullconn.FullConn(input_feature=1152 * 2,
output_feature=10))
network.append(output.SoftMax())
#network.setCost(N.CategoryCrossEntropy)
network.build()
f = h5py.File('/hdd/home/yueguan/workspace/data/mnist/mnist.hdf5', 'r')
trX = f['x_train'][:, :].reshape(-1, 1, 28, 28)
teX = f['x_test'][:, :].reshape(-1, 1, 28, 28)
trY = np.zeros((f['t_train'].shape[0], 10))
trY[np.arange(len(f['t_train'])), f['t_train']] = 1
teY = np.zeros((f['t_test'].shape[0], 10))
teY[np.arange(len(f['t_test'])), f['t_test']] = 1
for i in range(5000):
print(i)
network.train(trX, trY)
print(1 - np.mean(np.argmax(teY, axis=1) == network.predict(teX)))
def test_generative():
import mlbase.cost as cost
import mlbase.layers.activation as act
import h5py
network = N.Network()
network.debug = True
network.setInput(N.RawInput((1, 28, 28)))
network.append(N.Conv2d(feature_map_multiplier=32))
network.append(act.Relu())
network.append(N.Pooling())
network.append(N.Conv2d(feature_map_multiplier=2))
network.append(act.Relu())
network.append(N.Pooling())
network.append(UpConv2d(feature_map_multiplier=2))
network.append(act.Relu())
network.append(UpConv2d(feature_map_multiplier=32))
network.append(act.Relu())
#network.append(N.Flatten())
#network.append(N.FullConn(input_feature=1152, output_feature=1152*2))
#network.append(N.Relu())
#network.append(N.FullConn(input_feature=1152*2, output_feature=10))
#network.append(N.SoftMax())
network.costFunction = cost.ImageSSE
network.inputOutputType = (
T.tensor4(),
T.tensor4(),
)
network.build()
f = h5py.File('/hdd/home/yueguan/workspace/data/mnist/mnist.hdf5', 'r')
trX = f['x_train'][:, :].reshape(-1, 1, 28, 28)
teX = f['x_test'][:, :].reshape(-1, 1, 28, 28)
trY = np.zeros((f['t_train'].shape[0], 10))
trY[np.arange(len(f['t_train'])), f['t_train']] = 1
teY = np.zeros((f['t_test'].shape[0], 10))
teY[np.arange(len(f['t_test'])), f['t_test']] = 1
for i in range(5000):
print(i)
#network.train(trX, trY)
#print(1 - np.mean(np.argmax(teY, axis=1) == np.argmax(network.predict(teX), axis=1)))
network.train(trX, trX)
print(
np.sum(
(teX - network.predict(teX)) * (teX - network.predict(teX))))
def test_seqlayer():
network = N.Network()
network.debug = True
class ConvNN(layer.Layer, metaclass=compose.SeqLayer,
seq=[Conv2d, act.Relu, pooling.Pooling],
yaml_tag=u'!ConvNN',
type_name='ConvNN'):
def __init__(self, feature_map_multiplier=1):
super().__init__()
self.bases[0] = Conv2d(feature_map_multiplier=feature_map_multiplier)
network.setInput(RawInput((1, 28,28)))
network.append(ConvNN(feature_map_multiplier=32))
network.append(ConvNN(feature_map_multiplier=2))
network.append(ConvNN(feature_map_multiplier=2))
network.append(reshape.Flatten())
network.append(fullconn.FullConn(input_feature=1152, output_feature=1152*2))
network.append(act.Relu())
network.append(fullconn.FullConn(input_feature=1152*2, output_feature=10))
network.append(output.SoftMax())
network.build()
trX, trY, teX, teY = l.load_mnist()
for i in range(5000):
print(i)
network.train(trX, trY)
print(1 - np.mean(np.argmax(teY, axis=1) == np.argmax(network.predict(teX), axis=1)))
def testbn():
network = N.Network()
network.debug = True
network.setSaveInterval(10)
network.setInput(RawInput((1, 28,28)))
network.append(conv.Conv2d(filter_size=(3,3), input_feature=1, output_feature=32))
network.append(N.BatchNormalization())
network.append(act.Relu())
network.append(pooling.Pooling((2,2)))
network.append(conv.Conv2d(filter_size=(3,3), input_feature=32, output_feature=64))
network.append(N.BatchNormalization())
network.append(act.Relu())
network.append(pooling.Pooling((2,2)))
network.append(conv.Conv2d(filter_size=(3,3), input_feature=64, output_feature=128))
network.append(N.BatchNormalization())
network.append(act.Relu())
network.append(pooling.Pooling((2,2)))
network.append(reshape.Flatten())
network.append(fullconn.FullConn(input_feature=1152, output_feature=1152*2))
network.append(act.Relu())
network.append(fullconn.FullConn(input_feature=1152*2, output_feature=10))
network.append(output.SoftMax())
network.build()
f = h5py.File('/home/rain/workspace/data/mnist.hdf5', 'r')
trX = f['x_train'][:,:].reshape(-1, 1, 28, 28)
teX = f['x_test'][:,:].reshape(-1, 1, 28, 28)
trY = np.zeros((f['t_train'].shape[0], 10))
trY[np.arange(len(f['t_train'])), f['t_train']] = 1
teY = np.zeros((f['t_test'].shape[0], 10))
teY[np.arange(len(f['t_test'])), f['t_test']] = 1
for i in range(5000):
print(i)
network.train(trX, trY)
print(1 - np.mean(np.argmax(teY, axis=1) == np.argmax(network.predict(teX), axis=1)))
def test_globalpooling():
network = N.Network()
network.debug = True
network.setInput(RawInput((1, 28, 28)))
network.append(conv.Conv2d(filter_size=(3, 3), feature_map_multiplier=32))
network.append(bn.BatchNormalization())
network.append(act.Relu())
network.append(polling.Pooling((2, 2)))
network.append(conv.Conv2d(filter_size=(3, 3), feature_map_multiplier=2))
network.append(bn.BatchNormalization())
network.append(act.Relu())
network.append(pooling.Pooling((2, 2)))
network.append(conv.Conv2d(filter_size=(3, 3), feature_map_multiplier=2))
network.append(bn.BatchNormalization())
network.append(act.Relu())
network.append(pooling.GlobalPooling())
network.append(fullconn.FullConn(input_feature=128, output_feature=10))
network.append(output.SoftMax())
network.build()
f = h5py.File('/hdd/home/yueguan/workspace/data/mnist/mnist.hdf5', 'r')
trX = f['x_train'][:, :].reshape(-1, 1, 28, 28)
teX = f['x_test'][:, :].reshape(-1, 1, 28, 28)
trY = np.zeros((f['t_train'].shape[0], 10))
trY[np.arange(len(f['t_train'])), f['t_train']] = 1
teY = np.zeros((f['t_test'].shape[0], 10))
teY[np.arange(len(f['t_test'])), f['t_test']] = 1
for i in range(5000):
print(i)
network.train(trX, trY)
print(1 - np.mean(
np.argmax(teY, axis=1) == np.argmax(network.predict(teX), axis=1)))
