def test_maxout():
network = N.Network()
network.setInput(N.RawInput((1, 28, 28)))
network.append(N.Conv2d(filter_size=(3, 3), feature_map_multiplier=128))
network.append(N.FeaturePooling(4))
network.append(N.Pooling((2, 2)))
network.append(N.Conv2d(filter_size=(3, 3), feature_map_multiplier=8))
network.append(N.FeaturePooling(4))
network.append(N.Pooling((2, 2)))
network.append(N.Conv2d(filter_size=(3, 3), feature_map_multiplier=8))
network.append(N.FeaturePooling(4))
network.append(N.GlobalPooling())
network.append(N.FullConn(input_feature=128, output_feature=10))
network.append(N.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 test1():
network = N.Network()
network.debug = True
network.setInput((28, 28))
network.append(N.Conv2d(filter=(3, 3), input_feature=1, output_feature=32))
network.append(N.Relu())
network.append(N.Conv2d(filter=(3, 3), input_feature=32,
output_feature=32))
network.append(N.Relu())
network.append(N.Conv2d(filter=(3, 3), input_feature=32,
output_feature=32))
network.append(N.Relu())
network.append(N.Pooling((2, 2)))
network.append(N.Conv2d(filter=(3, 3), input_feature=32,
output_feature=64))
network.append(N.Relu())
network.append(N.Conv2d(filter=(3, 3), input_feature=64,
output_feature=64))
network.append(N.Relu())
network.append(N.Conv2d(filter=(3, 3), input_feature=64,
output_feature=64))
network.append(N.Relu())
network.append(N.Pooling((2, 2)))
network.append(
N.Conv2d(filter=(3, 3), input_feature=64, output_feature=128))
network.append(N.Relu())
network.append(
N.Conv2d(filter=(3, 3), input_feature=128, output_feature=128))
network.append(N.Relu())
network.append(
N.Conv2d(filter=(3, 3), input_feature=128, output_feature=128))
network.append(N.Relu())
network.append(N.Pooling((2, 2)))
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.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_pooling_forward(self):
x = np.asarray(rng.uniform(low=-1, high=1, size=(500, 20, 28, 28)))
x = theano.shared(x, borrow=True)
pooling = N.Pooling()
y = pooling.forward([x])
y_shape = y[0].eval().shape
self.assertEqual(y_shape, (500, 20, 14, 14))
def test():
network = N.Network()
network.debug = True
network.setInput(N.RawInput((1, 28,28)))
network.append(N.Conv2d(feature_map_multiplier=32))
network.append(N.Relu())
network.append(N.Pooling())
network.append(N.Conv2d(feature_map_multiplier=2))
network.append(N.Relu())
network.append(N.Pooling())
network.append(UpConv2d(feature_map_multiplier=2))
network.append(N.Relu())
network.append(UpConv2d(feature_map_multiplier=32))
network.append(N.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_binaryweight():
network = N.Network()
network.debug = True
network.setInput(N.RawInput((1, 28,28)))
network.append(N.Conv2d(feature_map_multiplier=32))
network.append(N.Relu())
network.append(N.Pooling((2,2)))
network.append(Binarize())
network.append(N.Conv2d(feature_map_multiplier=2))
network.append(N.Relu())
network.append(N.Pooling((2,2)))
network.append(Binarize())
network.append(BinaryConv2d(feature_map_multiplier=2))
network.append(N.Relu())
network.append(N.Pooling((2,2)))
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.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)))
def test_pooling_forwardSize(self):
x = [(100, 1, 28, 28)]
pool = N.Pooling()
y = pool.forwardSize(x)
self.assertEqual(y, [(100, 1, 14, 14)])
