def step(z, f, o, c):
'''
Runs fo-pooling at each time step
'''
c = f * c + (1 - f) * z
if opt.att: # attention
a = tf.nn.softmax(tf.einsum("ijk,ik->ij", H, c)) # alpha. (16, 150)
k = (a.sg_expand_dims() * H).sg_sum(axis=1) # attentional sum. (16, 150)
h = o * (k.sg_dense(act="linear") + c.sg_dense(act="linear"))
else:
h = o * c
return h, c # hidden states, (new) cell memories
def step(z, f, o, c):
'''
Runs fo-pooling at each time step
'''
c = f * c + (1 - f) * z
if opt.att: # attention
a = tf.nn.softmax(tf.einsum("ijk,ik->ij", H,
c)) # alpha. (b, seqlen)
k = (a.sg_expand_dims() * H).sg_sum(
axis=1) # attentional sum. (b, seqlen)
h = o * (k.sg_dense_gpus(act="linear",name = "k%d_%s"%(t,opt.name),dev = opt.dev,reuse=opt.reuse_vars)\
+ c.sg_dense_gpus(act="linear",name = "c%d_%s"%(t,opt.name),dev = opt.dev,reuse=opt.reuse_vars))
else:
h = o * c
return h, c # hidden states, (new) cell memories