是否可以向 Caffe 添加新层?
Is it possible to add new layers to Caffe?
我可以用 Caffe 添加一个新层(用于不同的损失)吗?
我怎样才能使用 Matlab 或 Python?
是的,您可以使用 pycaffe 添加自定义损失函数。这是 python 中的欧几里得损失层示例(摘自 Caffe Github repo)。前向函数需要提供损失函数,后向函数需要提供梯度:
import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
"""
Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
to demonstrate the class interface for developing layers in Python.
"""
def setup(self, bottom, top):
# check input pair
if len(bottom) != 2:
raise Exception("Need two inputs to compute distance.")
def reshape(self, bottom, top):
# check input dimensions match
if bottom[0].count != bottom[1].count:
raise Exception("Inputs must have the same dimension.")
# difference is shape of inputs
self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
# loss output is scalar
top[0].reshape(1)
def forward(self, bottom, top):
self.diff[...] = bottom[0].data - bottom[1].data
top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
def backward(self, top, propagate_down, bottom):
for i in range(2):
if not propagate_down[i]:
continue
if i == 0:
sign = 1
else:
sign = -1
bottom[i].diff[...] = sign * self.diff / bottom[i].num
例如保存为pyloss.py。然后,您可以使用 prototxt 文件中的 python 层来加载它:
layer {
type: 'Python'
name: 'loss'
top: 'loss'
bottom: 'ipx'
bottom: 'ipy'
python_param {
# the module name -- usually the filename -- that needs to be in $PYTHONPATH
module: 'pyloss'
# the layer name -- the class name in the module
layer: 'EuclideanLossLayer'
}
# set loss weight so Caffe knows this is a loss layer.
# since PythonLayer inherits directly from Layer, this isn't automatically
# known to Caffe
loss_weight: 1
}
或者在您的 python 脚本中:
n.loss = L.Python(n.ipx, n.ipy,python_param=dict(module='pyloss',layer='EuclideanLossLayer'),
loss_weight=1)
在计算和实现梯度(反向函数)时要非常小心,因为它往往容易出错。
我可以用 Caffe 添加一个新层(用于不同的损失)吗? 我怎样才能使用 Matlab 或 Python?
是的,您可以使用 pycaffe 添加自定义损失函数。这是 python 中的欧几里得损失层示例(摘自 Caffe Github repo)。前向函数需要提供损失函数,后向函数需要提供梯度:
import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
"""
Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
to demonstrate the class interface for developing layers in Python.
"""
def setup(self, bottom, top):
# check input pair
if len(bottom) != 2:
raise Exception("Need two inputs to compute distance.")
def reshape(self, bottom, top):
# check input dimensions match
if bottom[0].count != bottom[1].count:
raise Exception("Inputs must have the same dimension.")
# difference is shape of inputs
self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
# loss output is scalar
top[0].reshape(1)
def forward(self, bottom, top):
self.diff[...] = bottom[0].data - bottom[1].data
top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
def backward(self, top, propagate_down, bottom):
for i in range(2):
if not propagate_down[i]:
continue
if i == 0:
sign = 1
else:
sign = -1
bottom[i].diff[...] = sign * self.diff / bottom[i].num
例如保存为pyloss.py。然后,您可以使用 prototxt 文件中的 python 层来加载它:
layer {
type: 'Python'
name: 'loss'
top: 'loss'
bottom: 'ipx'
bottom: 'ipy'
python_param {
# the module name -- usually the filename -- that needs to be in $PYTHONPATH
module: 'pyloss'
# the layer name -- the class name in the module
layer: 'EuclideanLossLayer'
}
# set loss weight so Caffe knows this is a loss layer.
# since PythonLayer inherits directly from Layer, this isn't automatically
# known to Caffe
loss_weight: 1
}
或者在您的 python 脚本中:
n.loss = L.Python(n.ipx, n.ipy,python_param=dict(module='pyloss',layer='EuclideanLossLayer'),
loss_weight=1)
在计算和实现梯度(反向函数)时要非常小心,因为它往往容易出错。