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