def sample_from_activation(self, vmap):
p0 = self.probabilities_from_activation(vmap)
s0 = samplers.multinomial(p0)
s = s0[:, :, :-1] # chop off the last state (zero state)
return s
def sample_from_activation(self, vmap):
p0 = self.probabilities_from_activation(vmap)
s0 = samplers.multinomial(p0)
s = s0[:, :, :-1] # chop off the last state (zero state)
return s
def sample_from_activation(self, vmap):
p = self.probabilities_from_activation(vmap)
return samplers.multinomial(p)
def sample_from_activation(self, vmap):
p = self.probabilities_from_activation(vmap)
return samplers.multinomial(p)
def sample_from_activation(self, vmap):
p0 = activation_functions.softmax_with_zero(vmap[self])
s0 = samplers.multinomial(p0)
s = s0[:, :, :-1] # chop off the last state (zero state)
return s
def sample_from_activation(self, vmap):
p = activation_functions.softmax(vmap[self])
return samplers.multinomial(p)
def sample_from_activation(self, vmap):
p0 = activation_functions.softmax_with_zero(vmap[self])
s0 = samplers.multinomial(p0)
s = s0[:, :, :-1] # chop off the last state (zero state)
return s
def sample_from_activation(self, vmap):
p = activation_functions.softmax(vmap[self])
return samplers.multinomial(p)