def __init__(self, dimc, act=nn.ELU()):
super(MNISTConvEnc, self).__init__()
self.enc = nn.Sequential(
nn_.ResConv2d(1, 16, 3, 2, padding=1, activation=act), act,
nn_.ResConv2d(16, 16, 3, 1, padding=1, activation=act), act,
nn_.ResConv2d(16, 32, 3, 2, padding=1, activation=act), act,
nn_.ResConv2d(32, 32, 3, 1, padding=1, activation=act), act,
nn_.ResConv2d(32, 32, 3, 2, padding=1, activation=act), act,
nn_.Reshape((-1, 32 * 4 * 4)), nn_.ResLinear(32 * 4 * 4, dimc),
act)
def __init__(self, dimz, dimc, act=nn.ELU()):
super(MNISTConvDec, self).__init__()
self.dec = nn.Sequential(
nn_.ResLinear(dimz, dimc),
act,
nn_.ResLinear(dimc, 32 * 4 * 4),
act,
nn_.Reshape((-1, 32, 4, 4)),
nn.Upsample(scale_factor=2, mode='bilinear'),
nn_.ResConv2d(32, 32, 3, 1, padding=1, activation=act),
act,
nn_.ResConv2d(32, 32, 3, 1, padding=1, activation=act),
act,
nn_.slicer[:, :, :-1, :-1],
nn.Upsample(scale_factor=2, mode='bilinear'),
nn_.ResConv2d(32, 16, 3, 1, padding=1, activation=act),
act,
nn_.ResConv2d(16, 16, 3, 1, padding=1, activation=act),
act,
nn.Upsample(scale_factor=2, mode='bilinear'),
nn_.ResConv2d(16, 1, 3, 1, padding=1, activation=act),
)
import nn network = nn.Container() network.add(nn.Reshape((1, 784))) network.add(nn.Linear(784, 100)) network.add(nn.Sigmoid()) network.add(nn.Linear(100, 10)) network.add(nn.Sigmoid()) network.add(nn.MSE(), cost=True) network.make()
for filename, sents in data.iteritems():
sent = choice(sents)
mat = sent2matrix(sent)
img = cv2.imread(path + filename)
rs = cv2.resize(img, (100, 100)).reshape(
(300, 100)).astype(theano.config.floatX)
yield (mat, rs)
network = nn.Container()
# Encoder
network.add(nn.LSTM(dict_size, 300, 300))
network.add(nn.LSTM(300, 100, 100))
network.add(nn.LSTM(100, 100, 33))
# Decoder
network.add(nn.Reshape((1, 3, 11, 100)))
network.add(nn.SpatialConvolution((1, 3, 11, 100), (16, 3, 5, 5)))
network.add(nn.ReLU())
network.add(nn.SpatialMaxPooling((2, 2), 48))
network.add(nn.Reshape((2304, )))
network.add(nn.Linear(2304, 30000))
network.add(nn.Reshape((300, 100)))
network.add(nn.MSE(), cost=True)
print 'Network created'
print 'Compiling function'
network.make()
print 'Function created'
print 'In training'
k = 0
#n_train = 900