def __call__(self, x, t):
"""Computes the loss value for given input and ground truth labels.
Args:
x (~chainer.Variable): Input of the weight matrix multiplication.
t (~chainer.Variable): Batch of ground truth labels.
Returns:
~chainer.Variable: Loss value.
"""
batch_size = x.shape[0]
if hasattr(self, 'sample_data'):
# for test
sample_data = self.sample_data
else:
shape = (batch_size, self.sample_size)
sample_data = self.sampler.sample(shape)
samples = chainer.Variable(sample_data)
return black_out.black_out(x, t, self.W, samples)
def forward(self, x, t):
"""Computes the loss value for given input and ground truth labels.
Args:
x (~chainer.Variable): Input of the weight matrix multiplication.
t (~chainer.Variable): Batch of ground truth labels.
Returns:
~chainer.Variable: Loss value.
"""
batch_size = x.shape[0]
if self.sample_data is not None:
# for test
sample_data = self.sample_data
else:
shape = (batch_size, self.sample_size)
sample_data = self.sampler.sample(shape)
samples = variable.Variable(sample_data, requires_grad=False)
return black_out.black_out(x, t, self.W, samples)
def forward(self, x, t):
"""Computes the loss value for given input and ground truth labels.
Args:
x (~chainer.Variable): Input of the weight matrix multiplication.
t (~chainer.Variable): Batch of ground truth labels.
Returns:
~chainer.Variable: Loss value.
"""
batch_size = x.shape[0]
if self.sample_data is not None:
# for test
sample_data = self.sample_data
else:
shape = (batch_size, self.sample_size)
sample_data = self.sampler.sample(shape)
samples = variable.Variable(sample_data, requires_grad=False)
return black_out.black_out(x, t, self.W, samples)
def __call__(self, x, t):
"""Computes the loss value for given input and ground truth labels.
Args:
x (~chainer.Variable): Input of the weight matrix multiplication.
t (~chainer.Variable): Batch of ground truth labels.
Returns:
~chainer.Variable: Loss value.
"""
batch_size = x.shape[0]
if hasattr(self, 'sample_data'):
# for test
sample_data = self.sample_data
else:
shape = (batch_size, self.sample_size)
sample_data = self.sampler.sample(shape)
samples = chainer.Variable(sample_data)
return black_out.black_out(x, t, self.W, samples)