def test_softmax_forward(self):
x = np.asarray(rng.uniform(low=-1, high=1, size=(500, 10)))
x = theano.shared(x, borrow=True)
sm = N.SoftMax()
y = sm.forward([x])
y_shape = y[0].eval().shape
self.assertEqual(y_shape, (500, 10))
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 test():
network = N.Network()
network.debug = True
network.setInput(N.RawInput((28, 28)))
network.append(
N.Conv2d(filter_size=(3, 3), input_feature=1, output_feature=32))
network.append(N.Relu())
network.append(
N.Conv2d(filter_size=(2, 2),
input_feature=32,
output_feature=32,
subsample=(2, 2),
border='valid'))
network.append(
N.Conv2d(filter_size=(3, 3), input_feature=32, output_feature=64))
network.append(N.Relu())
network.append(
N.Conv2d(filter_size=(2, 2),
input_feature=64,
output_feature=64,
subsample=(2, 2),
border='valid'))
network.append(
N.Conv2d(filter_size=(3, 3), input_feature=64, output_feature=128))
network.append(N.Relu())
network.append(
N.Conv2d(filter_size=(2, 2),
input_feature=128,
output_feature=128,
subsample=(2, 2),
border='valid'))
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()
print(network)
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():
network = N.Network()
network.debug = True
network.setInput(N.RawInput((1, 28, 28)))
network.append(N.Conv2d(feature_map_multiplier=32))
network.append(ResLayer())
network.append(ResLayer())
network.append(ResLayer())
network.append(ResLayer(increase_dim=True))
network.append(ResLayer())
network.append(ResLayer())
network.append(ResLayer())
network.append(ResLayer(increase_dim=True))
network.append(ResLayer())
network.append(ResLayer())
network.append(ResLayer())
network.append(N.GlobalPooling())
network.append(N.FullConn(input_feature=128, 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_softmax_forwardSize(self):
x = [(500, 10)]
softmax = N.SoftMax()
y = softmax.forwardSize(x)
self.assertEqual(y, [(500, 10)])
