def check_forward(self, x_data, W_data, b_data):
# Check only data type, y is tested by SimplifiedDropconnect link test.
x = chainer.Variable(x_data)
W = chainer.Variable(W_data)
if b_data is None:
y = functions.simplified_dropconnect(x, W, None, self.ratio,
self.train)
else:
b = chainer.Variable(b_data)
y = functions.simplified_dropconnect(x, W, b, self.ratio,
self.train)
self.assertEqual(y.data.dtype, self.x_dtype)
def check_forward(self, x_data, W_data, b_data):
# Check only data type, y is tested by SimplifiedDropconnect link test.
x = chainer.Variable(x_data)
W = chainer.Variable(W_data)
if b_data is None:
y = functions.simplified_dropconnect(x, W, None,
self.ratio, self.train, None,
self.use_batchwise_mask)
else:
b = chainer.Variable(b_data)
y = functions.simplified_dropconnect(x, W, b,
self.ratio, self.train, None,
self.use_batchwise_mask)
self.assertEqual(y.data.dtype, self.x_dtype)
mask = y.creator.mask
mask = cuda.to_cpu(mask)
if self.use_batchwise_mask:
self.assertEqual(mask.shape, (x.shape[0],) + W.shape)
else:
self.assertEqual(mask.shape, W.shape)
def check_forward(self, x_data, W_data, b_data):
# Check only data type, y is tested by SimplifiedDropconnect link test.
x = chainer.Variable(x_data)
W = chainer.Variable(W_data)
if b_data is None:
y = functions.simplified_dropconnect(x, W, None, self.ratio,
self.train, None,
self.use_batchwise_mask)
else:
b = chainer.Variable(b_data)
y = functions.simplified_dropconnect(x, W, b, self.ratio,
self.train, None,
self.use_batchwise_mask)
self.assertEqual(y.data.dtype, self.x_dtype)
mask = y.creator.mask
mask = cuda.to_cpu(mask)
if self.use_batchwise_mask:
self.assertEqual(mask.shape, (x.shape[0], ) + W.shape)
else:
self.assertEqual(mask.shape, W.shape)
def f(x, W, b=None):
return functions.simplified_dropconnect(
x, W, b, self.ratio, self.train, mask,
self.use_batchwise_mask)
def f(x, W, b=None):
y = functions.simplified_dropconnect(x, W, b, self.ratio,
self.train, mask,
self.use_batchwise_mask)
return y * y