def forward_gpu(self, inputs):
x, t = inputs
self.y, = Softmax(self.use_cudnn).forward_gpu((x, ))
ret = cuda.reduce('int* t, float* y, int n_channel',
'-log(y[i * n_channel + t[i]])', 'a+b', '0',
'crossent_fwd', numpy.float32)(t, self.y,
self.y.shape[1])
ret /= t.size
return ret,
def forward_gpu(self, inputs):
x, t = inputs
self.y, = Softmax(self.use_cudnn).forward_gpu((x,))
ret = cuda.reduce(
'int* t, float* y, int n_channel', '-log(y[i * n_channel + t[i]])',
'a+b', '0', 'crossent_fwd', numpy.float32)(t, self.y, self.y.shape[1])
ret /= t.size
return ret,
class SoftmaxCrossEntropy(Function):
"""Softmax activation followed by a cross entropy loss."""
def __init__(self, use_cudnn=True):
self.use_cudnn = use_cudnn
def forward_cpu(self, inputs):
x, t = inputs
self.y, = Softmax().forward_cpu((x, ))
return -numpy.log(self.y[range(len(t)),
t]).sum(keepdims=True) / t.size,
def forward_gpu(self, inputs):
x, t = inputs
self.y, = Softmax(self.use_cudnn).forward_gpu((x, ))
ret = cuda.reduce('int* t, float* y, int n_channel',
'-log(y[i * n_channel + t[i]])', 'a+b', '0',
'crossent_fwd', numpy.float32)(t, self.y,
self.y.shape[1])
ret /= t.size
return ret,
def backward_cpu(self, inputs, grad_outputs):
t, gloss = inputs[1], grad_outputs[0]
gx = self.y.copy()
gx[range(len(t)), t] -= 1
gx *= gloss[0] / t.size
return gx, None
def backward_gpu(self, inputs, grad_outputs):
t, gloss = inputs[1], grad_outputs[0]
gx = cuda.empty_like(self.y)
coeff = gloss / t.size
cuda.elementwise(
'float* gx, const float* y, const int* t, const float* coeff, int n_channel',
'gx[i] = *coeff * (y[i] - ((i % n_channel) == t[i / n_channel]))',
'softmax_crossent_bwd')(gx, self.y, t, coeff, self.y.shape[1])
return gx, None
class SoftmaxCrossEntropy(Function):
"""Softmax activation followed by a cross entropy loss."""
def __init__(self, use_cudnn=True):
self.use_cudnn = use_cudnn
def forward_cpu(self, inputs):
x, t = inputs
self.y, = Softmax().forward_cpu((x,))
return -numpy.log(self.y[xrange(len(t)), t]).sum(keepdims=True) / t.size,
def forward_gpu(self, inputs):
x, t = inputs
self.y, = Softmax(self.use_cudnn).forward_gpu((x,))
ret = cuda.reduce(
'int* t, float* y, int n_channel', '-log(y[i * n_channel + t[i]])',
'a+b', '0', 'crossent_fwd', numpy.float32)(t, self.y, self.y.shape[1])
ret /= t.size
return ret,
def backward_cpu(self, inputs, grad_outputs):
t, gloss = inputs[1], grad_outputs[0]
gx = self.y.copy()
gx[xrange(len(t)), t] -= 1
gx *= gloss[0] / t.size
return gx, None
def backward_gpu(self, inputs, grad_outputs):
t, gloss = inputs[1], grad_outputs[0]
gx = cuda.empty_like(self.y)
coeff = gloss / t.size
cuda.elementwise(
'float* gx, const float* y, const int* t, const float* coeff, int n_channel',
'gx[i] = *coeff * (y[i] - ((i % n_channel) == t[i / n_channel]))',
'softmax_crossent_bwd')(gx, self.y, t, coeff, self.y.shape[1])
return gx, None
def forward_cpu(self, inputs):
x, t = inputs
self.y, = Softmax().forward_cpu((x, ))
return -numpy.log(self.y[range(len(t)),
t]).sum(keepdims=True) / t.size,
def forward_cpu(self, inputs):
x, t = inputs
self.y, = Softmax().forward_cpu((x,))
return -numpy.log(self.y[xrange(len(t)), t]).sum(keepdims=True) / t.size,
