def __call__(self, x, xmask=None):
probs = self.d(x) * 0.98 + 0.01
mask = srng.uniform(probs.shape) < probs
print xmask
mask.name = "mask!"
masked = self.h.activation(sparse_dot(x, xmask, self.h.W, mask, self.h.b, self.block_size))
if not "this is the equivalent computation in theano":
h = self.h(x)
if self.do_dropout:
h = h * (srng.uniform(h.shape) < 0.5)
h_r = h.reshape([h.shape[0], self.nblocks, self.block_size])
masked = h_r * mask.dimshuffle(0,1,'x')
masked = masked.reshape(h.shape)
self.sample_probs = T.prod(mask*probs+(1-probs)*(1-mask), axis=1)
self.probs = probs
return masked, mask
def __call__(self, x, xmask=None):
probs = self.d(x) * 0.98 + 0.01
mask = srng.uniform(probs.shape) < probs
print xmask
mask.name = "mask!"
masked = self.h.activation(sparse_dot(x, xmask, self.h.W, mask, self.h.b, self.block_size))
if not "this is the equivalent computation in theano":
h = self.h(x)
if self.do_dropout:
h = h * (srng.uniform(h.shape) < 0.5)
h_r = h.reshape([h.shape[0], self.nblocks, self.block_size])
masked = h_r * mask.dimshuffle(0,1,'x')
masked = masked.reshape(h.shape)
self.sample_probs = T.prod(mask*probs+(1-probs)*(1-mask), axis=1)
self.probs = probs
return masked, mask
def __call__(self, x, xmask=None):
mask = srng.uniform((x.shape[0], self.nblocks)) < self.rate
masked = self.h.activation(
sparse_dot(x, xmask, self.h.W, mask, self.h.b, self.block_size))
return masked, mask
def __call__(self, x, xmask=None):
mask = srng.uniform((x.shape[0],self.nblocks)) < self.rate
masked = self.h.activation(sparse_dot(x, xmask, self.h.W, mask, self.h.b, self.block_size))
return masked, mask
