def forward(net):
transform = layers.Transform(mirror=True, crop_size=227, mean_file="data/ilsvrc12/imagenet_mean.binaryproto")
data = layers.Data(name="data", tops=["data", "label"], source="examples/imagenet/ilsvrc12_train_lmdb",
batch_size=256, transform=transform)
net.forward_layer(data)
alexnet_layers = alexnet.alexnet_layers()
loss = 0.
for layer in alexnet_layers:
loss += net.forward_layer(layer)
return loss
def forward(net):
transform = layers.Transform(
mirror=True,
crop_size=227,
mean_file="data/ilsvrc12/imagenet_mean.binaryproto")
data = layers.Data(name="data",
tops=["data", "label"],
source="examples/imagenet/ilsvrc12_train_lmdb",
batch_size=256,
transform=transform)
net.forward_layer(data)
alexnet_layers = alexnet.alexnet_layers()
loss = 0.
for layer in alexnet_layers:
loss += net.forward_layer(layer)
return loss
def forward(net):
transform = layers.Transform(mirror=True, crop_size=227, mean_file="data/ilsvrc12/imagenet_mean.binaryproto")
data = layers.ImageData(name="data", tops=["data", "label"], source="data/flickr_style/train.txt",
batch_size=50, new_height=256, new_width=256, transform=transform)
net.forward_layer(data)
alexnet_layers = alexnet.alexnet_layers()
for layer in alexnet_layers[:-2]:
net.forward_layer(layer)
# initialize fc8 from random weights
fc8_flickr = alexnet_layers[-2]
fc8_flickr.p.name = 'fc8_flickr'
fc8_flickr.p.top[0] = 'fc8_flickr'
net.forward_layer(fc8_flickr)
softmax_loss = alexnet_layers[-1]
softmax_loss.p.bottom[0] = 'fc8_flickr'
loss = net.forward_layer(softmax_loss)
return loss
def forward(net):
transform = layers.Transform(mirror=True, crop_size=227, mean_file="data/ilsvrc12/imagenet_mean.binaryproto")
data = layers.ImageData(name="data", tops=["data", "label"], source="data/flickr_style/train.txt",
batch_size=50, new_height=256, new_width=256, transform=transform)
net.forward_layer(data)
alexnet_layers = alexnet.alexnet_layers()
for layer in alexnet_layers[:-2]:
net.forward_layer(layer)
# initialize fc8 from random weights
fc8_flickr = alexnet_layers[-2]
fc8_flickr.p.name = 'fc8_flickr'
fc8_flickr.p.top[0] = 'fc8_flickr'
net.forward_layer(fc8_flickr)
softmax_loss = alexnet_layers[-1]
softmax_loss.p.bottom[0] = 'fc8_flickr'
loss = net.forward_layer(softmax_loss)
return loss
